Gasificación de residuos urbanos en lecho fluidizado: distribución de compuestos inorgánicos y traza

Tesis doctoral para optar al ttulo de Doctor por la Universidad de Sevilla y al ttulo de Doctor Internacional (Doctoral thesis for the degree of Doctor of Philosophy and International Doctorate) GASIFICACIÓN DE RESIDUOS URBANOS EN LECHO FLUIDIZADO: DISTRIBUCIÓN DE COMPUESTOS INORGÁNICOS Y TRAZA (GASIFICATION OF URBAN WASTES IN FLUIDIZED BED: DISTRIBUTION OF INORGANICS AND TRACE ELEMENTS) Vanessa Ferreira de Almeida Director de Tesis (Thesis director): Alberto Gmez Barea Departamento de Ingeniera Qumica y Ambiental Escuela Superior de Ingeniera (Universidad de Sevilla) Febrero 2023 To my parents, Claudia and Juramir i Abstract Processing waste in fluidized bed (FB) by gasification is an interesting alternative over other thermochemical options and types of equipment. However, it still presents several drawbacks compared to processing other standardized fuels, like coal or clean biomass. The challenges are associated with the intrinsic nature of waste fuels, especially their heterogeneity in size and composition, and with the higher content in contaminants precursors (N, S and Cl) and trace elements, such as heavy metals. This thesis aims to study the gasification of waste-derived fuels in a fluidized bed, emphasizing the main aspects that make the thermal conversion of these solid wastes different from other cleaner fuels, such as standardised biomass. In particular, this thesis focuses on the distribution of inorganic and trace elements during the gasification of urban wastes in fluidized bed (FB). In order to achieve this objective, experimental tests and thermodynamic models have been conducted, and extensive comparison with experimental experiences collected and published by researchers during the last decades. The results provide an understanding of the content in inorganics and trace elements in different types of waste fuels, some key issues during sampling and measuring contaminants, and establish rational prediction of the distribution of the main and trace elements during thermal conversion. More specifically, the main achievements of this thesis work are summarized as follows: - An extensive review of the literature regarding the content of inorganics and trace elements in waste fuels and their distribution during thermal conversion in the different product phases. - Detailed experimental tests identifying the most suitable materials, sampling methods and measurement technics for gasification tests at lab-scale aiming at measuring inorganics and trace elements. - Experimental results on the distribution of inorganics and trace elements during gasification of continuous tests in a bench-scale fluidized bed gasifier processing Refuse-Derived Fuel (RDF) and thermal conversion of various waste fuels in muffle furnace and batchwiseoperated lab-scale fluidized bed reactor under different operating conditions. Additional works on thermodynamic models to predict the distribution of the main compounds during the gasification of waste fuels and an environmental analysis to preliminarily assess the advantages ii of gasification of some fraction of wastes from municipal solid wastes compared to the option of landfilling are appended to this thesis. Keywords: gasification; fluidized bed; waste fuels; municipal solid waste (MSW); Refuse-Derived Fuel (RDF); Dried Sewage Sludge (DSS); trace elements; heavy metals; thermal conversion. iii Papers Part of the work of this thesis was materialized into two papers already published in international journals, both with a focus on contaminants, presented as follows. The content of papers 1 and 2 are gathered in Chapters 2 and 4, respectively. They are the following: PAPER 1. de Almeida, V.F., Gómez-Barea, A., Arroyo-Caire, J. and Pardo, I., 2020. On the Measurement of the Main Inorganic Contaminants Derived from Cl, S and N in Simulated Waste-Derived Syngas. Waste and Biomass Valorization, pp.1-16 PAPER 2. de Almeida, V. F., Gómez-Barea, A., Nilsson, S. and Tuomi, S., 2021. Distribution of Inorganics and Trace Elements during Waste Gasification in a Bench-Scale Fluidized Bed. Energy & Fuels, 35(19), pp.15802-15816. Complementary conference papers were published along this thesis (papers 3 – 5), which are listed below. Paper 3 is a previous preliminary discussion of the content of paper 1, and papers 3 and 4 contain part of the results presented in Chapter 3. They are the following: PAPER 3. de Almeida, V. F., Arroyo-Caire, J., Suárez-Almeida, M., Pardo, I., Nilsson, S., Haro, P., Gmez-Barea. On the measurement of main inorganic contaminants (HCl, H2S and NH3) in waste-derived syngas. 7th International Conference on Engineering for Waste and Biomass Valorization, July 2018, Prague, Check Republic. PAPER 4. de Almeida, V. F., Ronda, A., Arroyo-Caire, J., Suárez-Almeida, M., Pardo, I., Fuentes-Cano, D., Haro, P., Gómez-Barea, A. Heavy metals partitioning during thermal devolatilization of solid wastes in a fluidized bed reactor. 7th International Conference on Engineering for Waste and Biomass Valorization, July 2018, Prague, Check Republic. PAPER 5. de Almeida, V.F., Ronda, A., Pardo, I. and Gomez-Barea, A., Partitioning of contaminant and inorganic species during thermal devolatilization of dried sewage sludge. Proceedings of 7th International Conference on Sustainable Solid Waste Management, June 2019, Heraklion, Crete Island, Greece. During the doctorate, the candidate has contributed to the discussion, edition and preparation of several works, along with other researchers. These works were related to the thematic of this thesis. iv Papers 6 – 10 comprise these works, which are relevant to this thesis but not included in this document. They are the following: PAPER 6. Nilsson, S., Gmez-Barea, A., Pardo-Arias, I., Surez-Almeida, M. and de Almeida, V.F., 2019. Comparison of Six Different Biomass Residues in a Pilot-Scale Fluidized Bed Gasifier. Energy & Fuels, 33(11), pp.10978-10988. PAPER 7. Ronda, A., Gómez-Barea, A., Haro, P., de Almeida, V.F. and Salinero, J., 2019. Elements partitioning during thermal conversion of sewage sludge. Fuel Processing Technology, 186, pp.156-166. PAPER 8. Suárez-Almeida, M., de Almeida, V. F., Arroyo-Caire, J., Haro, P., Gómez-Barea, A. Technoeconomic assessment for implementing gasification technologies to process MSW refuse in Spain. 7th International Conference on Engineering for Waste and Biomass Valorization, July 2018, Prague, Check Republic. PAPER 9. Ronda, A., de Almeida, V.F., Salinero, J., Haro, P., Fuentes-Cano, D., Nilsson, S. Gómez- Barea, A. Study of the potential ecological risk of heavy metals in the residue from thermal conversion of a sewage sludge. 26th European Biomass Conference & Exhibition, May 2018, Copenhagen, Denmark. PAPER 10. Haro, P., Arroyo-Caire, J., de Almeida, V.F., Salinero, J., Vidal-Barrero, F., Gómez-Barea, A. Waste gasification for power generation: Assessment of industrial and non-industrial alkaline residues as sorbents for acid gas removal. 26th European Biomass Conference & Exhibition, May 2018, Copenhagen, Denmark. v Resumen de la tesis doctoral (Summary in Spanish) 1. Introducción Durante los últimos años, la sociedad ha ido cambiando de una economía lineal a una circular. La creciente demanda de energía y la necesidad de procesos más sostenibles ha llevado a un mayor interés en el uso de residuos como materia prima para la producción de energía. La conversión térmica de residuos sólidos presenta la ventaja de generar energía mientras se procesa una fracción que de otro modo se depositaría en vertederos, siendo un proceso atractivo que responde a la necesidad aguda de procesos circulares y sostenibles. Sin embargo, esta alternativa aún presenta varios inconvenientes asociados con la naturaleza intrínseca de los combustibles residuales, es decir, heterogeneidad en tamaño y composición y contenido en precursores de contaminantes, en comparación con otros combustibles estandarizados, como el carbón o la biomasa limpia. El propósito de este trabajo es estudiar la gasificación de combustibles derivados de residuos en lecho fluidizado y los principales puntos que diferencian la gasificación de residuos de un combustible limpio, como la biomasa, con el objetivo de contribuir a la aplicación del proceso a mayor escala. Como la emisión de contaminantes durante el procesamiento de residuos es uno de los mayores retos con respecto a la utilización de biomasas limpias, esta tesis se ha centrado principalmente en el comportamiento de los precursores de contaminantes durante la conversión térmica de estos combustibles residuales sólidos en el proceso de gasificación. En los siguientes capítulos se revisará la literatura, incluyendo las principales técnicas para la medición de contaminantes, su contenido en el combustible y en las corrientes de producto. Luego, se presentarán los datos de experimentos propios tanto en escala de laboratorio como en “bench scale”. En los anexos se presentarán los resultados de las simulaciones de modelización y, por último, se mostrará una evaluación ambiental de los procesos de gasificación en operación continúa utilizando combustible derivado de residuo. A continuación, se presenta un resumen de los capítulos de la presente tesis. xii Acknowledgements I would like to thank everybody who has supported me during the development of this thesis, colleagues, family, and friends. First and foremost, I would like to praise and thank God, the Almighty, who supported me in each step of my progress towards the successful completion of my research work, and who has granted countless blessings and knowledge so that I have been finally able to accomplish this thesis. Especially, I would like to thank my director, Professor Alberto Gómez Barea, for his assistance and advice during the development of this thesis and for allowing me to work on this project. In addition, I also want to thank Dr Susanna Nilsson for her assistance in developing this thesis. I would also like to thank Professor Fengqi You for receiving me at Peese Research Group and also Professor Jefferson Tester and Professor Johannes Lehmann for all the support and knowledge shared with me during my stay at Cornell. I want to also express my gratitude to Sanna Tuomi during my stay at VTT for all her support and guidance and to Dr Esa Kurkela for receiving me at his research group. This thesis would not have been completed without the help of my co-authors and colleagues. Thank you, Javi and Isra, for sharing knowledge and working with me at the laboratory. Your contribution was essential for the experimental part developed at the University of Seville. Thanks, Maite, Diego and Rafa for your support and kindness in the laboratory. I am also grateful for my colleagues and friends from the Chemical Engineering Department of the University of Seville, all the time together at “becaria”. Special thanks to Karla y Neto (Dr Gutiérrez) for sharing this PhD journey with me, which friendship and support were indispensable to keep me going. Also, thanks to Soko and Yanqiu for our time together at Cornell. I want to thank all my colleagues and friends from IDENER. Maria Tripiana and Dr Alejandro del Real, thanks for believing in me, allowing me to work on research and continuously learn. Special thanks to Maria Tripiana for her friendship and being my mentor along these years. Special thanks Patri, Andrea, Fer, Ivan and Alex Hall, which friendship and support have kept me going since 2019. Also, thanks to Cris and Ciro for their support in this final stage. xiii To my family and friends: my deepest gratitude for giving me the strength to believe in myself and pursue my dreams, even if it was on the other side of the ocean. To my parents, Claudia and Juramir - to whom this thesis is dedicated - and whose love, support, friendship, values and encouragement enabled the development of this thesis. To my godmother, Chica, my aunts Ana, Zita, Dete, Elena and Biga, and to Amanda - who, together with my mother and grandmothers – are the inspirational women of my life. To my uncle Hélio and cousins Rafael, Gabriela and Ingrid, which friendship was so important to fuel my work. To the memory of my grandparents, João, Maria, Juliano and Noemia. To William, Kate and Amora, which company and love were indispensable to finishing this work. To Marina (now Dr Casanova), my dearest friend, whose support and friendship are invaluable to me and have supported my journey since 2015. To my beloved friends Bianca and Judy, who have shared their passion for Chemical Engineering with me since 2009. To Gonzalo, whose continuous encouragement, kindness and understanding in this last sprint made everything looks easier. I cannot imagine this thesis without you all. Thanks for everything. Finally, I would like to acknowledge the financial support from the "Science without Borders" program, supported by CNPq, Brazil (Project 201591/2015-4), that enabled the development of this thesis. In addition, the financial support from the Spanish National Plan I+D+I (project NetuWas, CTM2016-78089-R) is also acknowledged. Complementary, I would like to thank the University of Seville for the financial support for my stay at the Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University (USA). I would also like to thank the funding from Horizon 2020's Research and Innovation Program under grant agreement number 731101 (Brisk II) for financially supporting my stay at the VTT Research Centre Ltd (Finland). xiv “Life is not easy for any of us. But what of that? We must have perseverance and, above all, confidence in ourselves. We must believe that we are gifted for something, and that this thing, at whatever cost, must be attained.” Marie Curie xv Gasification of urban wastes in fluidized bed: distribution of inorganics and trace elements xvi 1 Introduction Chapter 1 Introduction 1.1. Gasification of solid wastes In recent years, society has changed from a linear to a circular economy. The escalating energy demand and the requirement for more sustainable processes have increased interest in using waste as feedstock for energy production. The thermal conversion of solid wastes presents the advantage of generating energy while processing a fraction that would otherwise be landfilled (1–3), being an attractive process that responds to the acute need for circular and sustainable processes. However, this alternative still presents several drawbacks associated with the intrinsic nature of waste fuels - i.e., heterogeneity in size and composition and content in contaminants precursors - compared to other standardized fuels, such as coal or clean biomass (4). Incineration is the most common process for the large-scale thermal valorisation of such fuels, while pyrolysis and gasification are still in development and, thus, are typically used at smaller plants (5). Nevertheless, gasification is of great interest since it produces a gas that can be used in boilers, engines, turbines, and fuel cells to generate electricity when efficiently cleaned. Municipal Solid Waste (MSW) can be processed to generate Refuse Derived Fuel (RDF), or Solid Recovered Fuel (SRF), the latter being a standardized material from MSW, which can be easier processed to generate energy (6). Nevertheless, solid wastes, such as MSW, RDF, SRF and Dried Sewage Sludge (DSS) contain high amounts of species that potentially form pollutants during gasification: Cl, S, N and trace elements, i.e. heavy metals (HM) (4,7–9). Therefore, the thermochemical processes that use these fuels must be adapted with cleaning steps and close control of the pollutants generated. 2 Introduction 1.2. Nitrogen, Chlorine, and Sulphur in waste-derived fuels During the gasification of wastes, special attention is paid to the formation of some compounds derived from Cl, S and N in the gas phase, i.e., HCl, H2S, NH3, and HCN, as well as to some harmful HM. Furthermore, the content of pollutants precursors in these fuel types is much higher than in plantbased biomass, which is the most common fuel in gasification plants. Hence, the novelty of dealing with high amounts of Cl, S and N in the fuel may justify why several lab-scale plants are not appropriately prepared for quantifying such pollutants. Table 1.1 below shows the typical content of S, N and Cl for different types of fuels. Table 1.1. Content of S, N and Cl for different types of fuels according to literature. ND and NM stand for not detected and not measured, respectively (4,10–19) Fuel Amount of S, N and Cl (% wt. daf) Reference Wood <0.1 / 0.1 – 0.5 / 0.08 – 0.1 R. Zevenhoven & P. Kilpinen (10 – 12) 0.03 / 0.4 / 0.02 Paasen et al. (taken from the Phyllis2 database) (4)(15) <0.03 / <0.03 / <0.03 Schweitzer et al. (19) Pinewood ND / ND / ND Ryu et al. (16) Reed canary grass 0 / 1.3 / 0.6 Ryu et al. (16) Bark 2 / ~ 0.5 / 0.02 – 0.4 R. Zevenhoven & P. Kilpinen (10 – 12) Straw ~ 0.2 / 0.5 – 1 / 0.1 – 1.5 R. Zevenhoven & P. Kilpinen (10 – 12) Dry Sewage sludge (DSS) ~ 0.2 / 1 – 5 / 0.03 – 1 R. Zevenhoven & P. Kilpinen (10 – 12) 2 / 7 / 0.03 A. Ronda et al. (14) 0.1 – 1.1 / 0.3 – 7.6 / 0.02 – 0.5 Literature in A. Ronda et al. (14) 0.02 / 0.07 / 0.002 Schweitzer et al. (19) Poultry litter 0.9 / 6. 5 / 0.7 D. Pandey et al. (17) Cattle manure 0.01 / 0.03 / 0.008 Schweitzer et al. (19) Pig manure 0.01 0.03 / 0.005 Schweitzer et al. (19) Municipal solid waste (MSW) 0.1 – 1 / ~ 1 / 0.05 – 0.25 R. Zevenhoven & P. Kilpinen (10 – 12) 0.5 / 1.2 / 1.1 Paasen et al. (taken from the Phyllis2 database) (4)(15) Refuse-derived fuel (RDF) 0.1 – 1 / ~ 1 / 0.3 – 0.8 R. Zevenhoven & P. Kilpinen (10 – 12) 0.1 / 0.5 / 0.7 V. F. de Almeida et al. (13) 0.4 / 1.1 / 0.4 Paasen et al. (taken from the Phyllis2 database) (4)(15) 0.1 / 1.2 / 2.3 Ryu et al. (16) Solid recovered fuel (SRF) – 80% plastic, 20% cellulose 0.03 / 0.1 / NM U. Arena & F. Di Gregorio (18) From Table 1.1 data, some highlights can be made, even though, due to the intrinsic heterogeneity of waste fuels, concentrations can vary substantially: - DSS typically contains around 0.1 – 1.1% wt. dry and ash-free (daf) of S (up to 2.0% wt. daf) and 0.03 to 1% wt. daf of Cl (15)(13). 3 Introduction - RDF usually contains less S (0.1 – 0.4% wt. daf, up to 1% daf) and more Cl (0.3 – 0.8% wt. daf), up to 2.3% wt. daf. Nitrogen is also higher in DSS due to its protein content, up to 7.6% wt. daf, while about 0.5 – 1.2% wt. daf in RDF (4). - For wood, S and Cl appear in trace amounts, from 0.03 – 1% for S and 0.02 – 0.08% wt. daf, while N varies from 0.03 to 0.5% wt. daf (4). 1.3. Trace elements and heavy metals in waste-derived fuels Besides the major elements (elements that constitute more than 1 percent of the fuel by weight: carbon, hydrogen, oxygen, nitrogen, and sometimes chlorine and sulphur) the fuel contains minor elements (between 1.0 to 0.01 %w/w) like Ca, Cl, S, Mg, Na, K, Si, Al, Fe, P. The rest of elements in the fuel in concentrations less than 0.1% wt. is called trace elements. They typically include the EDD (European Dirty Dozen: As, Cd, Cr, Cu, Co, Hg, Mn, Ni, Pb, Sb and V (20)) as well as Zn and Sn. The major elements have a significant role in chemical balance, which affects the behaviour of pollutants during thermal conversion. Due to the nature of waste-derived fuels, such as MSW, RDF, and Dried Sewage Sludge (DSS), their content in trace elements and heavy metals is much higher than the content in clean biomass, or coal. For instance, clean biomass usually contains a high amount of Mn, K, P, Cl, Ca, Mg and Na. In the other side, waste-fuels normally presents a substantial content in Cu, Pb, Mn, and even harmful metals, such as Hg, As, and Cd. Table 1.2 below shows the typical concentrations of different elements for DSS, RDF and wood, gathering data from own dedicated tests (ODT) and the literature. 4 Introduction Table 1.2. Comparative concentration of trace elements in different types of fuels in g/kg of dry fuel. Element DSS DSS DSS DSS RDF RDF RDF Wood Wood Description (840μm – 2mm) (500 - 840μm) (500μm – 2mm) Literature range* Pellets Pellets NA Pellets NA Reference ODT ODT A. Ronda et al. (15) A. Ronda et al. (15) ODT De Almeida et al. (14) Zevenhoven & Kilpinen (21) ODT Zevenhoven & Kilpinen (21) Al 14.34 5.13 8.26 10.5 – 23.6 3.28 17.60 NM 2.00 NM As 0.01 0.01 0.05 0.01 – 1.50 3.47 0.00 ~ 0.01 0.33 0.00 Ba NM 0.25 NM NM ND 1.15 NM ND NM Be NM NM 0.00 ND NM NM 0.00 NM NM Ca 46.54 23.89 24.49 22.8 – 82.6 17.24 2.55 NM 1.90 NM Cd 0.00 0.00 0.00 0.00 – 0.05 0.01 0.00 0.00 – 0.01 0.01 NM Co 0.01 0.01 0.00 0.00 – 0.05 0.00 0.01 NM 0.03 0.00 Cr 0.14 0.08 0.07 0.00 – 0.50 0.04 0.06 0.05 – 0.25 0.03 0.00 Cu 0.73 0.40 0.21 0.11 – 1.22 0.17 0.04 < 1.00 0.19 0.00 Fe 21.52 11.50 17.86 6.80 –107.00 1.58 1.70 NM 0.23 NM Hg ND 0.01 NM NM ND 0.01 0 – 0.01 ND 0.00 In NM 0.00 NM NM ND NM NM ND NM K 4.96 7.72 4.51 2.38 – 9.70 2.90 0.42 NM 0.89 NM Mg 7.55 3.74 5.63 3.13 – 6.72 1.33 1.15 NM 0.67 NM Mn 0.20 0.19 0.21 0.03 – 1.93 0.01 0.05 ~ 0.25 0.07 0.01 – 1.00 Na 2.25 6.95 3.05 0.01 – 3.00 2.33 7.45 NM 2.78 NM Ni 0.17 0.13 0.11 0.02 – 1.52 0.25 0.01 0 – 0.10 0.25 0.00 P 30.06 23.94 27.42 0.23 – 21.00 ND 8.15 NM ND NM Pb 0.09 0.07 0.05 0.09 – 0.264 0.00 0.04 0.10 – 0.50 ND 0.00 – 0.02 Sb 0.01 0.02 0.01 0.01 0.03 0.03 < 0.01 0.01 NM Se ND 0.00 <0.01 0.00 – 0.02 ND NM 0.00 – 0.01 ND 0.00 Si 16.58 ND 0.46 1.00 – 71.10 9.49 2.05 NM 0.89 NM Sn 0.06 1.06 0.28 0.00 – 0.09 0.08 0.01 ~ 0.50 0.00 NM Sr ND 0.35 NM NM ND 0.04 NM ND NM Te ND 0.00 NM NM ND NM NM ND NM Ti 1.29 1.16 1.09 0.01 – 1.500 1.67 0.21 NM 0.01 NM V 0.02 0.02 0.06 0.00 – 0.03 0.00 0.01 NM 0.00 ~ 0.00 Zn 1.17 1.18 0.30 0.10 – 2.33 0.24 0.22 0.30 – 0.80 0.16 0.01 – 0.15 NA: Not applicable / NM: Not measured / ND: Not detected / ODT: Own dedicated tests Ronda et al. (15); Zevenhoven & Kilpinen (21); de Almeida et al. (14) 5 Introduction From Table 1.2, it is possible to observe that the content of trace elements is in the same range for the different types of fuels presented, in this case Dried Sewage Sludge (DSS), Refuse-Derived Fuel (RDF) and wood. Additionally, in this table it is evidenced that the content of trace elements is much higher in the waste fuels compared to clean biomass. 1.4. The release of inorganic elements during the gasification of wastederived fuels The chemical form in which these elements are bounded in the parent fuel – for instance, organic or inorganic compounds - greatly affects its partitioning between gas and solid phases. Therefore, when processing waste-derived fuels in thermochemical routes, it is important to understand how the elements in the fuel behave during their processing, i.e., if the element tends to stay in the solid phase or be released to the gas phase, as well as the way in which the combine one with each other. This knowledge should be estimated as a function of the physical (size, density) and chemical properties (elemental and proximate analysis) of the fuel and the operating conditions (temperature, pressure, and composition of the bulk gas environment). Furthermore, adapting the downstream processes to comply with the current normative for heavy metals is crucial for scaling up a process, such as fluidized-bed gasification to meaningful scales. Complementary, nowadays, the need for some of valuable elements, such as the Critical Raw Materials (CRM) -includes Mg, Co, Ti, Li, V and P – suggest that the recuperation of these elements are also of great interest during the waste processing for energy (22). During thermal conversion, organic compounds are more prone to be released to the gas phase, because their bound energies are weaker (23,24). Other parameters also influence the contaminants partitioning, i.e. temperature, oxygen ratio (or equivalence ratio, ER), fuel particles size, solids residence time, gasification agent and reactor design (8,9,25–28). Measurement of contaminants is an essential task, both for gas cleaning design, as well as for keeping contaminants under the environmental limits enforced by governmental standards. 12 Chapter 2 Information of fuel, temperature, reactor material, gasification agent, equivalence ratio (ER), mass balances, tar, and char (when available), are included in Table 2.1, as well as relevant details of the collection setup. All works deal with gasification, except for Ryu et al. (17), which covers pyrolysis. Particular assumptions were made for the calculations in order to assess Cl, S and N mass balances in some works (19,32–36), as indicated in the comments of Table 2.1. Most recoveries (R) were calculated considering only the gas phase (the only one reported), with the exception of Paasen et al. (4), Ryu et al. (17) and Arena and Gregorio et al. 2014 (32), in which data of tars and solid streams were provided and , thus, included in the mass balances. 13 Chapter 2 Table 2.1. Review and analysis of literature works measuring contaminants derived from Cl, N, and S. Recovery (%) Reference Feedstock Temperatur e ER Reactor / Material Atmosphere Bed material Gas Tar Solid Total Summary of sampling setup ºC Cl S N Cl Cl S Cl S N Paasen et al 3 SS + WP 750 0.22 FB/NS Air Silica Sand 6.1 108.6 41.4 1.2 5.0 5.3 12.2 113.9 41.4 * H2S and COS in gas phase: Micro GC, when in low concentrations they were measured by GC SS + WP 850 0.23 FB/NS Air Silica Sand 8.5 92.3 23.8 9.1 4.5 7.6 22.1 99.8 23.8 * HCl in gas phase: collected in impingers, HCl analysed by IC RDF 725 0.21 FB/NS Air Silica Sand 16.3 57.4 3.3 35.9 1.7 1.5 53.9 58.8 3.3 while NH3 and HCN by FIA RDF 820 0.21 FB/NS Air Silica Sand 53.4 27.0 11.4 44.2 8.6 6.8 106.2 33.8 11.4 *Cl in tar: EOX analyser, coulometry after combustion *Solid phase: Cl same method used for tar, and S by ICP Ryu et al 15 RDF* 350 - 700 - Packed Bed/316SS N2 - - - - - 0.0 50.6 1.6 53.4 - *Pyrolysis tests *S and Cl also measured in oil phase/ ICP and SEM analysis of char *Cl: analysed by titration, using the Schöninger oxygen flask combustion Arena and Gregorio 16 SRF 849 0.26 FB/NS Air Olivine 1.2 0.8 1.5 - - - 1.2 0.8 1.5 *NH3, H2S and HCl in gas phase: collected in impingers and analysed by IC SRF 852 0.26 FB/NS Air Olivine 0.0 0.0 0.0 - - - 0.0 0.0 0.0 *Average of recovery rates, since contaminants content in fuel was in an interval SRF 869 0.27 FB/NS Air Olivine 0.4 0.0 2.8 - 135.0 - 135.4 0.0 2.8 SRF 879 0.30 FB/NS Air Olivine 16.4 4.7 0.9 - - - 16.4 4.7 0.9 SRF 898 0.32 FB/NS Air Olivine 0.3 0.1 0.1 - - - 0.3 0.1 0.1 14 Chapter 2 SRF 932 0.33 FB/NS Air Olivine 3.2 0.1 0.5 - 133.8 - 137.0 0.1 0.5 Arena and Gregorio 17 SRF 833 0.26 FB/NS Air Olivine* 885.2 63.6 117.6 - - - 885.2 63.6 117.6 * SRF type 1 / Bed material also contained Fe and Mg silicates SRF 816 0.31 FB/NS Air Olivine* 432.3 80.4 371.3 - - - 432.3 80.4 371.3 * Same collection and measurement method of Arena & Gregorio, 2014 SRF 847 0.37 FB/NS Air Olivine* 412.8 77.2 250.3 - - - 412.8 77.2 250.3 SRF 850 0.39 FB/NS Air Olivine* 401.9 76.2 131.7 - - - 401.9 76.2 131.7 Berrueco et al 18 RT 700 0.30 FB/Hastelloy- X Air Silica Sand 19.6 5.2 3.2 - - - 19.6 5.2 3.2 *NH3, H2S and HCl in gas phase: collected in Tedlar bags, then pumped RT 750 0.31 FB/Hastelloy- X Air Silica Sand 13.0 0.7 3.4 - - - 13.0 0.7 3.4 into impingers and analysed by means of ISE RT 800 0.31 FB/Hastelloy- X Air Silica Sand 13.7 7.7 2.6 - - - 13.7 7.7 2.6 RT 850 0.31 FB/Hastelloy- X Air Silica Sand 6.0 20.3 9.6 - - - 6.0 20.3 9.6 Recari et al 19 RT 750 0.31 FB/Hastelloy- X Air Silica Sand 13.1 0.0 3.2 - - - 13.1 0.0 3.2 * Same collection and measurement method of Berrueco et al, 2015 RT 850 0.31 FB/Hastelloy- X Air Silica Sand 7.3 21.5 10.8 - - - 7.3 21.5 10.8 FL 750 0.30 FB/Hastelloy- X Air Silica Sand 30.8 0.0 2.1 - - - 30.8 0.0 2.1 FL 850 0.30 FB/Hastelloy- X Air Silica Sand 34.8 0.0 2.5 - - - 34.8 0.0 2.5 RT 850 0.31 FB/Hastelloy- X Air Dolomite 6.1 21.6 0.0 - - - 6.1 21.6 0.0 FL 850 0.31 FB/Hastelloy- X Air Dolomite 4.5 0.0 0.0 - - - 4.5 0.0 0.0 RT 850 0.30 FB/Hastelloy- X O2/Steam Dolomite 6.8 0.0 4.3 - - - 6.8 0.0 4.3 Recari et al 20 FL290* 850 0.29 FB/Hastelloy- X Air Silica Sand 2.2 48.1 24.2 - - - 2.2 48.1 24.2 *FL290, FL320: Torrefacted SRF at 290 and 320ºC, respectively. 15 Chapter 2 FL290 850 0.31 FB/Hastelloy- X Air Dolomite 0.9 32.6 8.3 - - - 0.9 32.6 8.3 * Same collection and measurement method of Berrueco et al, 2015 FL320* 850 0.29 FB/Hastelloy- X Air Silica Sand 20.1 62.8 22.0 - - - 20.1 62.8 22.0 FL320 850 0.29 FB/Hastelloy- X Air Dolomite 14.1 49.5 23.9 - - - 14.1 49.5 23.9 FL290 850 0.32 FB/Hastelloy- X O2/Steam Silica Sand 1.9 24.3 30.0 - - - 1.9 24.3 30.0 FL290 850 0.32 FB/Hastelloy- X O2/Steam Dolomite 1.0 62.8 42.7 - - - 1.0 62.8 42.7 FL320 850 0.30 FB/Hastelloy- X O2/Steam Silica Sand 5.7 35.8 48.0 - - - 5.7 35.8 48.0 FL320 850 0.31 FB/Hastelloy- X O2/Steam Dolomite 6.5 118.8 64.3 - - - 6.5 118.8 64.3 FL320 850 0.31 FB/Hastelloy- X O2/Steam Olivine 2.3 78.1 38.7 - - - 2.3 78.1 38.7 Recari et al 21 RT 850 0.31 FB/Hastelloy- X O2/Steam Silica Sand 97.5 14.2 47.2 - - - 97.5 14.2 47.2 * Same collection and measurement method of Berrueco et al, 2015 RT 850 0.34 FB/Hastelloy- X O2/Steam Dolomite 27.5 103.8 92.4 - - - 27.5 103.8 92.4 RT 850 0.29 FB/Hastelloy- X O2/Steam Olivine 98.5 3.7 47.7 - - - 98.5 3.7 47.7 FL 850 0.30 FB/Hastelloy- X O2/Steam Silica Sand 106.7 15.6 53.7 - - - 106.7 15.6 53.7 FL 850 0.31 FB/Hastelloy- X O2/Steam Dolomite 91.5 38.4 111.6 - - - 91.5 38.4 111.6 FL 850 0.30 FB/Hastelloy- X O2/Steam Olivine 93.0 34.0 44.1 - - - 93.0 34.0 44.1 Pandey et al 22 PL 700 0.18 FB/NS Air+N2 Silica Sand 0.7 18.9 96.2 - - - 0.7 18.9 96.2 * H2S and COS in gas phase: Micro GC PL 700 0.22 FB/NS Air+N2 Silica Sand 0.7 27.6 96.2 - - - 0.7 27.6 96.2 * NH3 and HCl in gas phase: Collected once a day in impingers and PL 700 0.30 FB/NS Air+N2 Silica Sand 0.7 23.8 96.2 - - - 0.7 23.8 96.2 measured by means of IC, they were collected in the end of four tests, 16 Chapter 2 PL 700 0.30 FB/NS Air+N2 Silica Sand 0.7 37.6 96.2 - - - 0.7 37.6 96.2 so results are in average. PL 700 0.29 FB/NS Air+N2 Sand+Limestone 4.2 25.6 98.4 - - - 4.2 25.6 98.4 PL 700 0.35 FB/NS Air+N2 Sand+Limestone 4.2 30.2 98.4 - - - 4.2 30.2 98.4 PL 700 0.41 FB/NS Air+N2 Sand+Limestone 4.2 15.2 98.4 - - - 4.2 15.2 98.4 PL 700 0.35 FB/NS Air+N2 Sand+Limestone 4.2 35.7 98.4 - - - 4.2 35.7 98.4 PL 750 0.23 FB/NS Air+N2 Sand+Limestone 0.6 11.2 71.9 - - - 0.6 11.2 71.9 PL 750 0.28 FB/NS Air+N2 Sand+Limestone 0.6 13.7 71.9 - - - 0.6 13.7 71.9 PL 750 0.33 FB/NS Air+N2 Sand+Limestone 0.6 12.5 71.9 - - - 0.6 12.5 71.9 PL 750 0.28 FB/NS Air+N2 Sand+Limestone 0.6 17.8 71.9 - - - 0.6 17.8 71.9 PL 800 0.25 FB/NS Air+N2 Sand+Limestone - 11.1 - - - - - 11.1 - PL 800 0.30 FB/NS Air+N2 Sand+Limestone - 12.6 - - - - - 12.6 - Schweitzer et al 23 SS 800 - CFB/NS O2/Steam Silica Sand* 84.2 20.9 47.6 - - - 84.2 20.9 47.6 *NH3, H2S and HCl in gas phase: collected in impingers CM 800 - CFB/NS O2/Steam Silica Sand* 9.6 80.0 37.0 - - - 9.6 80.0 37.0 *Quantification: NH3 by photometry analysis, HCl by coulometry and PM 800 - CFB/NS O2/Steam Silica Sand* 8.9 39.3 34.2 - - - 8.9 39.3 34.2 H2S through the reaction with Zn, quantified by iodometric titration - For Arena & Gregorio works, the MB in gas phase was done assuming that contaminants concentration (mg/nm3) was referring to the same gas of the gas yield results provided in the papers, because in both works the product gas was not specified as wet or dry. - For Berrueco et al and Recari et al works it was considered that, contaminants concentration was given for a syngas containing N2. Both, gas yield and contaminants concentration were considered for a dry syngas. - For the remaining papers included, the gas yield and contaminants concentrations were provided in a way that mass balance in gas phase could be assessed directly. 17 Chapter 2 The majority of authors collected contaminants containing Cl and N in impingers and then measured their concentrations by IC (4,18,19,32). In those works, H2S was sampled and measured in the same way as Cl and N (32)(19), or online via micro-GC (4)(18). Berrueco et al. (33) and Recari et al. (34– 36) also collected contaminants through impingers, but they analysed the solutions with a different method, the Ion-Selective Electrode (ISE). Schweitzer et al. (11) used impingers as well, and measured contaminants by using three different methods: photometry (NH3), coulometry (HCl), and reaction with Zn, followed by iodometric titration (H2S). The concentrations of such species in syngas can be analysed online using more sophisticated technologies as mentioned before, i.e. mass spectroscopy (37)(38) or FTIR (39)(40). However, these methods are not included in Table 2.1 due to the lack of data for the calculation of elemental mass balances. Although in most works presented in Table 2.1 data for calculations of the Cl, N and S contents in syngas were available, it was not possible to close the elemental balances considering all exit streams (tar in syngas, exit ash and char streams). This is because information was not available. Paasen et al. (4) measured contaminants in tars and in the solid residues, and Arena and Gregorio 2014 (32) and Ryu et al. (17) measured Cl in the solid residue. Generally, data provided is incomplete to establish the species partitioning. In all works included in Table 2.1, contaminants in gas phase were collected in a liquid solution for posterior analysis. However, samples storage time was not included in any of them (unless for Paasen et al. (4)) which could be an additional source of inaccuracies in contaminants quantification. 2.4. Analysis and discussion of results from literature From the analysis of works summarized in Table 2.1 it is observed that, for some tests, the calculated recoveries of contaminants are extremely low or exceed 100%. In some experiments, the release of gas contaminants was negligible, while in others, more Cl, S or N were found in the gas phase than in the parent fuel. Fuel heterogeneity together with the inherent difficulties in the measurement (sampling and/or analysis, and interaction with the reactor/piping material of the rig) of some species could explain these inconsistencies (30)(31). 18 Chapter 2 Paasen et al. (4) measured contaminants derived from Cl, S and N, using two types of fuels: SRF and a blend of DSS and clean biomass (white pellets). They were focused on the Cl and S partitioning, and to our knowledge it was the only work found in literature in which the partitioning of contaminants into gas, tar and solid phases is reported in detail. They described the measurement methods for S and Cl in gas phase (tar included), the influence of Cl and S sampling points, as well as the impact of the tests duration on the results. The Cl and S in the solid residues were measured by sampling solids located in the freeboard and cyclone, showing that these species are more concentrated in the cyclone ash, due to their condensation on particulate matter surfaces. They concluded that Cl in tar is overestimated, probably because some HCl is dissolved from the gas phase when sampling tars (4). Authors recognized that Cl and S mass balances were not closed in most tests, which was attributed to the partial recollection of ashes. They claimed that mass balances for Cl and S would have been closed if all ash had been collected. Ryu et al. (17) performed pyrolysis tests and then quantified Cl and S in solid and oil phases. It can be concluded that most of Cl, and about 50% of S, were released to the gas phase, which agrees with the expected partitioning behaviour for these species (41). However, contaminants in gas phase were not quantified, so, in this case, the mass balance closure does not apply. Mass balances calculations for the results reported by Arena and Gregorio (32) showed very low recoveries for Cl, S and N in the gas phase. Chlorine was measured in two tests in solid phase (fly ashes), with a recovery of around 134%. Therefore, it was concluded that Cl from the gas phase was condensed on ash, in agreement with observations from previous studies (4). Cl recovery for exceeds 100% and the surplus may be justified by the low content of Cl in the fuel and fuel heterogeneity. Also, several processes that samples are submitted to, which are susceptible to errors (i.e., sampling, storage and analysis). These results contrast to the ones from Arena and Gregorio (19), who reported Cl and N recoveries much greater 100% (achieving 885.2% and 371.3% for Cl and N, respectively). HCl and NH3 concentrations in the gas phase were not high. However, Cl and N contents in SRF were very low, which may explain the inconsistencies found. The outsized values may indicate the incorrect quantification of those species in the fuel. S recoveries are in the range of 63.6−80.4%, demonstrating that most fuel-S was released to gas phase, which is the expected partitioning behaviour of this element during gasification (4)(41). 19 Chapter 2 Berrueco et al. (33) and Recari et al. (34–36) measurements were made applying the same sampling and analysis procedures, so results are discussed altogether. Recoveries from Berrueco et al. (33) are low for all contaminants in all tests, being the highest ones 19.6% and 20.3% for Cl and S, respectively. Recoveries from Recari et al. (34) were also small, even though, in some tests, Cl and S recoveries reached 34.8% and 21.6%, respectively. These range (19.6 – 34.8%) is still classified as low to moderate recoveries, especially for S, since at least over 90% of S is expected to be found in gas phase. Inconsistencies may be related to the measurement method, which was made by sampling the gas with Tedlar® bags, and the sampling of HCl, H2S, NH3 and HCN with impingers, analysed by ISE. As bags were maintained at ambient temperature, the water vapor in syngas may have condensed inside them together with some fraction of contaminants. Moreover, the ISE method is susceptible to interferences during measurements and may not quantify properly species in low concentrations (42), affecting results. Likewise, Norton and Brown (43) found negative deviations (up to 22%) in NH3 concentrations using the ISE method when measuring ammonia in doped gas. Recoveries calculated from data Recari et al. (35)(36) indicate that the gross of contaminants was captured from the gas phase in most of the tests, using the same measurement method discussed previously (33)(34). Chlorine recovery was very low for the results from Recari et al. (35), which they attributed to the effect of torrefaction, the process SRF was submitted to before gasification. By analysing the results of their last work (36), it can be observed that the measurement and sampling issues were improved. In Pandey et al. (18) tests, Cl and N recoveries are identical for some tests, because they provided an average of HCl and NH3 concentrations. That is because these compounds were sampled in the same impingers in several tests (tests 2 – 4) – using one set of impingers for each component NH3 and HCl – and then, made an average of NH3 and HCl concentrations in the product syngas from those experiments. Recoveries were high for N (up to 98.4%) but moderate for S (11.1 - 37.6%). Nonetheless, Cl recovery in the gas was very low, reaching only 4.2%. This may be related to the condensation of Cl in the hot filter because the contaminants’ sampling point was after it. The S released to the gas phase was expected to be even higher (37.6%), since in their experiments COS was also analysed in gas phase. H2S and COS are the two major species found in syngas, and because all sulphur is supposed to be found in gas during gasification in the range of 700 – 800 ºC, S recovery should approach 100%. The reactor material plays a role in H2S quantification, which may explain the low S recovery. 20 Chapter 2 Results of Schweitzer et al. (11) show moderate recoveries for N (34.2 - 47.6%) and from moderate to high recoveries for S (20.9 - 80.0%). Authors alerted about the possible errors in the Cl measurement method, because coulometry also detects bromine, oxalate, thiosulphate or thiocyanate compounds. Thus, consequently, Cl concentration in gas could be overestimated. Conversely, Cl recuperation in gas was low when using cattle and pig manure as fuels – recoveries of 9.6 and 8.9%, respectively. For sewage sludge, Cl recovery was high (84.2%), though. They reported that the line before the impingers was maintained at 180C. However, it is known that at this temperature the formation of NH4Cl as solid is already favoured, due to its vapor pressure is very low (4). Thus, this could explain the low and moderate recoveries of Cl and N, respectively. Chlorine and sulphur low recoveries can be associated to corrosion issues. Most works do not report the reactor material used in the gasification tests (Table 2.1). Berrueco et al. (33) and Recari et al. (34)(35)(36) used a reactor made of Hastelloy-X, which is known to be more resistant than other stainless steels (i.e. 316SS). Nevertheless, it can still suffer corrosion at gasification temperatures (700 – 900 ºC), as we later demonstrate in our experiments (presented in the next sections). Ståhlberg et al. (31) showed that stainless steel can be safely used for NH3 and HCN up to 600-700 ºC, while it is not recommended for HCl and H2S. An overview of all these results suggests that inaccuracies in the contaminants evaluation are associated specially with their interaction with the material lining; uncontrolled condensation (water condensation, formation of Cl salts with Na and K, NH4Cl formation) and issues with the contaminants measurement and sampling methods. All the presented issues are not considered in most works aiming at contaminants measurement in gas phase in the gasification community. These results are used in the experimental section of this chapter to explain the possible errors during measurements of contaminants derived from Cl, N and S. 21 Chapter 2 2.5. Experiments to evaluate the accuracy of the measurement methods To evaluate the precision of some of the measurement methods used in our facilities, tests were performed with typical contaminants concentrations in waste-derived syngas. 2.5.1. Materials HCl aqueous solution (37% vol.), NH3 aqueous solution (25% vol.) and H2S 1000 ppmv (diluted in N2) were used to simulate typical contaminants concentrations in syngas from waste-derived fuels. HCN was not studied due to its associated danger, to which our installations are not equipped for. The following compounds were used as absorption solutions: distillate water or NaOH (5% vol.) for HCl, and H2SO4 solution (0.05 mol/L) for NH3. After ammonia sampling, the pH of the captured solution was checked (pH = 2). KOH was added in the impingers to increase pH to 3-4, since it is the necessary range for the operation of the IC column. 2.5.2. Experimental setup and operating conditions The experiments were conducted in two different setups, shown in Figure 2.1. below. 28 Chapter 2 intensive use. Hence, the reactor internal layers should be checked periodically to guarantee the measurements validity. 2.7.2. Measurement of NH3 in the lab-scale tests Figure 2.4 contains the results of ammonia tests, showing low recovery of NH3 when using the first setup (Rig1), which may be related to 316SS deterioration, as reported before. For the tests using the second arrangement (Rig2), the ammonia recovery was high when employing alloy C-276, around 80%. Nonetheless, even higher recoveries were initially expected using C-276, since Ni-based alloys and low Cr levels are likely to be better for nitridation (46). Figure 2.4. Comparison of NH3 recovery for different materials at 800 ºC, only ▽ 316SS-Rig2 tests were performed at 500 ºC. Experimental time was set as 5 minutes. Tests using the 316SS in Rig2 (new material) showed the highest NH3 recoveries, higher than 95%. Additionally, steel lines do not seem to be a problem for transporting low amounts of NH3 in low concentrations, at least it was validated between 25 and 200 ºC (43). Furthermore, steels were reported to be resistant to ammonia even at higher temperatures (600-700 ºC) (30). This agrees with our results, which shows that 316SS is an appropriate material for gases containing NH3, when the reactor internal surface is preserved properly. 29 Chapter 2 It can also be appreciated in Figure 2.4 that the decrease in temperature did not influence the recovery of ammonia, when comparing tests at 800 ºC and 500 ºC. 2.7.3. Measurement of H2S in the lab-scale tests Figure 2.5 displays the H2S recovery from tests using different materials and temperatures. Figure 2.5. Comparison of H2S recovery for different materials using 1000 ppmv of H2S (unless for △ , which was performed using 500 ppmv of H2S) versus temperature. For □ Kanthal-APM tests the thermocouple protective cover –made of quartz was used. Experimental time was set as 5 minutes. For the 316SS, C-276 and 310SS, the decrease in the recovery with temperature is evident, explaining that these materials execute well in sulphur corrosive atmospheres at temperatures up to 500 °C, but they do not endure higher temperatures. Comparing these materials performance at 500 °C, 310SS was the best (R=90.6%), slightly better than C-276 (R=89.1%), while 316SS showed the worst behaviour (R=79.8%). Nevertheless, at 800 °C, alloy 316SS (R=38.3%) worked better than alloys 310SS (R=15.0%) and C-276 (R=10.5%). Figure 2.6 compares the H2S recovery at 800 °C for all the materials studied for 1000 ppmv of H2S in the inlet stream. 30 Chapter 2 Figure 2.6. Comparison of H2S recovery for different materials at same conditions (T = 800 °C, 1000 ppmv of H2S and 5 minutes of duration). For some materials, results were presented as the average from various tests conducted at same conditions (error bars added). Moreover, tests with quartz were also conducted, and results have shown complete recovery (R99%). Besides quartz, Kanthal-APM was the best performing material (and the best alloy among those investigated here). Two tests were conducted using a quartz cover on the thermocouple (which is made of alloy 310SS), to see its interference on the results. These tests revealed higher H2S recovery compared to the test without thermocouple’s cover, which remarks the importance of avoiding even small surfaces of materials that are prone to H2S corrosion. On the other hand, despite showing great endurance in the presence of H2S, Kanthal-APM is not suitable in chlorine corrosive atmospheres (43). Nevertheless, it appears to be an appropriate choice for fuels containing high S and low Cl amounts. 31 Chapter 2 2.7.4. General comments The material selection for a lab-scale reactor for the analysis of waste-derived syngas can be quite complex, because certain alloys behave well in presence of S but not in Cl. Quartz is inert but is also fragile. It can be concluded from Figures 3 and 6 that the most suitable choices for the analysis of gas containing both Cl and S at 800 °C – for instance, the case of MSW/RDF/SRF syngas – would be 316SS and HR-160. Alloy 316SS behaved better in presence of HCl, and, thus, it would be considered the best choice overall, between these alloys. Nonetheless, even in this scenario, the measurement of H2S would be severely compromised if those materials were employed at high temperature. This is because from the results of our own dedicated tests, a reduction of H2S concentration up to 60% in the outlet stream was observed when using those materials. Even though this is a specific problem of laboratory or pilot scale devices, it is an important issue when scaling up the results (refractory lining is applied in full-scale reactors). For a syngas containing low S and high Cl, 316SS is recommended, whereas Kanthal-APM is suggested when the syngas has high concentrations of these species. When carefully measurements of these species are required, the use of quartz is strongly encouraged. 2.8. Conclusions Earlier studies on waste gasification at laboratory scale have shown inconsistencies regarding the measurement of the main pollutants formed from Cl, S and N, i.e., HCl, H2S and NH3. In this paper, the fate of these contaminants was analysed: (i) by formulating elemental balances of Cl, S and N from selected literature measurements, and (ii) by conducting dedicated tests employing different setups and reactor materials, doping a carrier gas (N2) with HCl, NH3 and H2S with typical concentrations of waste-derived syngas. The analyses made in this paper explain some of the inconsistencies detected in literature. Thus, results enable recommendations for proper measuring of these contaminants in research facilities that focus on the partitioning of elements that form contaminants. The main findings are summarized as follows: 32 Chapter 2 Concentrations of HCl, NH3 and H2S reported in literature during gasification of wastes vary from virtually zero, to well in excess to the maximum allowable (the Cl, S and N content in the parent fuel). The reasons for these inconsistencies may be related to interaction between the reactor material and some species; uncontrolled condensation in the lines between the reactor and sampling ports; or imperfections in the sampling and measurement techniques employed. The method for sampling contaminants by using impingers and the posterior analysis of the solution by ion chromatography is reliable. It enables the detection of even small quantities of contaminants (1 ppmv), given that the temperature upstream the reactor is maintained above 200 ºC to avoid water condensation and, thus, the loss of NH3 and HCl. This is because these species are soluble in water, and condensation of chlorine in the form of alkali salts (Na, K) or as NH4Cl in the presence of NH3 may occur. Samples storage time did not affect the species concentration. Corrosion by H2S and HCl was found in tests at high temperature (above 500˚C), impacting on the measurement of these species. Thus, the selection of adequate materials is required (i.e., one that does not interact with these species). Even alloys that are considered resistant to corrosion, such as Hastelloy® C-276, Haynes® HR-160 and Kanthal-APM, showed severe limitations for their use at high temperature and corrosive atmospheres. The most suitable steel-based materials for waste-derived syngas containing both Cl and S, are 316 Stainless Steel (SS316) and Haynes® HR-160, even though it could result in under-prediction of both H2S (a loss of 60% for both materials) and HCl (a loss of 20 and 40% for SS316 and HR-160, respectively). The use of Kanthal-APM was shown to be the most appropriate for syngas containing high H2S and very low HCl, yielding S recovery above 95%. The use of C-276 and SS316 resulted in high recovery of NH3 (above 96%, reaching recovery ratios using SS316). Hence, quartz is strongly recommended for pyrolysis and gasification tests aiming at contaminants measurement in syngas. 33 Chapter 3 Chapter 3 Measurements and distribution of inorganics and trace elements during devolatilization of batch of fuel in a laboratory fluidized bed Summary The aim of this chapter is studying the fate of main inorganic precursors contaminants (Cl, N, S, and trace elements) during devolatilization tests in fluidized bed, using different solid wastes as fuels. The study was done by conducting batch-wise experiments using batch of fuels and measuring the transient response of the gas yield, using the following fuels: Dried Sewage Sludge (DSS), Refuse-Derived Fuel (RDF), and commercial wood pellets (for comparison) and N2 as the carrier gas. Analyses were made to understand the results and to compare the measurements with previous literature works. Two experimental campaigns using fluidized bed setups were conducted, the first one in 2017 and the second one in 2018 (1st: 2017 / 2nd: 2018). Additionally, muffle furnace tests were conducted. Further explanation about the tests are given below: - In the first experimental campaign of fluidized bed devolatilization tests (2017), devolatilization tests were conducted on the lab-scale fluidized bed (FB) reactor at 800 ºC using DSS (840μm – 2mm), RDF and commercial wood pellets as fuels. - In the second experimental campaign of fluidized bed devolatilization tests (2018), just DSS and wood pellets were employed as fuels. In this experimental campaign a wider temperature range was studied (600 – 900 °C), as well as some tests in air atmosphere. The differences of the second experimental campaign from the first one are: new reactor, although same design and material - Stainless Steel 316 (SS316); online measurement of the contaminants in gas phase by a mass spectrometer (previously these species were collected 34 Chapter 3 using impingers - HCl - and an online UV analyser - H2S); and smaller DSS particles sizes (500 – 840 μm). Additional muffle furnace tests using DSS particles (the same used in the second experimental campaign, with particle size of 500 – 840 μm) were conducted. The muffle furnace was included because in this setup the solid residue mass could be easily measured, which enables the obtaining of more accurate volatilization results for trace elements. In this type of test, the focus was only on the solid residue, therefore, the gaseous product released during the thermal conversion was not collected and analysed. The focus of the tests presented in Chapter 3 is on inorganics – N, S and Cl - and other trace elements – such as heavy metals - and the effect of temperature and atmosphere during the devolatilization step. Species containing N, S and Cl are usually measured in the gaseous phase, while trace elements are measured only in the solid residue. Additionally, main product gases from devolatilization, such as CO, CO2, CH4 and H2, were measured in both fluidized bed experimental campaigns. More details regarding the two fluidized bed experimental campaigns (1st: 2017 / 2nd: 2018) and the muffle furnace tests are given in the next sections. 3.1. Introduction During gasification, the devolatilization is one of the first steps – after drying – that a fuel particle goes through (47). The products of the of devolatilization of biomass are usually grouped into light gases, which include CO, CO2, H2, CH4 and other light hydrocarbons, other contaminant gases, HCl, NH3, HCN, H2S, condensable compounds, including organic compounds , water and char. Char is a carbonaceous solid that remains after thermal decomposition of the fuel, which is about 10-30 wt.% of the total mass fed (48), also depending on the initial fuel composition in ashes. The distribution of products obtained during devolatilization is a consequence of both the primary generation due to the thermal decomposition of the fuel and secondary reactions involving the 35 Chapter 3 produced volatiles. Secondary reactions may occur homogeneously or heterogeneously, both inside the fuel particle and in the reaction furnace, for example, with the water vapor that is released from the fuel drying. Devolatilization experiments are usually conducted in inert atmosphere, for example, using N2 as carrier gas for the evolving volatiles (48). 3.1.1. Literature review Ronda et al. (15) have presented a literature review of the partitioning of heavy metals during the thermal conversion of waste in 2019. The review included different types of waste fuels, such as doped sewage sludge, dried sewage sludge (DSS), food waste, PVC and municipal solid waste (MSW). For this work, a review of the advancements on the thermal conversion of solid waste was performed, analysing the literature from 2019 up to the present (January 2023). Some works detailed studied the devolatilization process of waste, during pyrolysis, combustion, as well as the conversion by thermogravimetric analysis (TGA), but the content in inorganic or trace elements, including heavy metals were not assessed (49–54). Other works found in recent literature studied the evolution of trace elements and polluting gases toward the clean co-combustion of coal and sewage sludge (55) and the co-combustion of torrefied municipal solid waste and coal in bubbling fluidized bed (56). Most works that study the trace elements behaviour during thermal conversion, focus on coal or solid waste as fuel, but during the combustion (57–60). Additional works that did studied the gasification of solid waste and inorganics were included in the literature review presented in Chapter 4 (Table 4.1) (32,61–64). Nevertheless, to the best of our knowledge, no recent work studying the devolatilization of waste derived fuels in fluidized bed focusing on inorganics and the partitioning of trace elements was published. Thus, the relevance of the results presented in this chapter are evidenced and can shed some light into the understanding of the conversion of solid waste - overall Dried Sewage Sludge (DSS) during its devolatilization in fluidized bed. 36 Chapter 3 3.2. Experimental Two types of experiments were conducted to analyse the devolatilization process: the first one is devolatilization tests in fluidized bed reactors, and the second one using a muffle furnace to analyse in detail the behaviour of the fuel. The tests conducted in the fluidized bed set up were conducted in two separate experimental campaigns (1st: 2017 / 2nd: 2018). Although the reactors used in both campaigns were made using the same design and material - Stainless Steel 316 (SS316) - in the second experimental campaign, the reactor was recently bought and new. In the first experimental campaign of fluidized bed devolatilization tests (2017), devolatilization tests were conducted on the lab-scale fluidized bed (FB) reactor at 800 ºC using DSS (840μm – 2mm), RDF and commercial wood pellets as fuels. In the second experimental campaign of fluidized bed devolatilization tests (2018), just DSS and wood pellets were employed as fuels. In this experimental campaign a wider temperature range was studied (600 – 900 °C), as well as some tests in air atmosphere. The differences of the second experimental campaign from the first one are: new reactor (although same design and material - Stainless Steel 316, SS316); online measurement of the contaminants in gas phase by a mass spectrometer (previously these species were collected using impingers - HCl - and an online UV analyser - H2S); and smaller DSS particles size (500 – 840 μm). Additionally, muffle furnace tests using DSS particles (the same used in the second experimental campaign, with particle size of 500 – 840 μm) were conducted. The muffle furnace was selected because in this setup the solid residue mass could be easily measured, which enables the obtaining of more accurate volatilization results for trace elements. In this type of test, the focus was only on the solid residue, the gaseous product released during the thermal conversion was not collected and measured. 37 Chapter 3 3.2.1. Materials Sand was used as bed material (250μm  d  500μm) in both FB tests campaigns. When contaminants sampling was done with impingers, water or NaOH solution (%) was used for HCl capture, and H2SO4 (%) with a buffer solution was used for HCN. Fuels proximate and elemental compositions are shown in Table 3.1, while Table 3.2 displays concentrations of trace elements. The DSS particles used in the muffle furnace experiment and in the last experimental campaign using the fluidized bed reactor, the DSS particles were smaller (500 - 840μm) than the DSS particles used in the first experimental campaign (840μm – 2mm). Nevertheless, they were considered to have the same composition in ash, fixed carbon, volatiles, moisture, and main elements (C, H, N, Cl, S, O), because they were from the same source. Different particles sizes were used in experimental campaigns due to fuel availability in the moment that experiments were being conducted. Table 3.1. Proximate and elemental composition of fuels Fuel DSS (0.5-2mm) RDF pellets Wood pellets Analysis Method % weight, as received Moisture 6.53 8.05 4.96 CEN/TS 14744-1 % weight, dry basis Ash 27.76 11.48 0.63 CEN/TS 14775 Fixed Carbon 10.06 11.24 15.59 * Volatiles 62.18 77.28 83.78 CEN/TS 15148 % weight, dry and ash free basis C 53.60 60.40 51.05 CEN/TS 15104 H 8.32 7.85 6.55 CEN/TS 15104 N 8.46 1.18 0.12 CEN/TS 15104 Cl 0.05 0.13 0.01 UNE ISO 16994 S 1.25 0.15 0.02 ** O 28.32 30.29 42.25 * * Calculated by difference, for RDF and DSS at 750°C, and for wood at 550°C ** Measured by ICP 44 Chapter 3 Bed material samples were also taken under the same procedure in 2017. The composition of solid residue samples was determined the same way as in 2017, however ICP analyses were conducted in Granada due to schedule issues at Seville. Their ICP equipment allows the more precise measurements of a wider selection of elements, thus some of them was added (Ba, Hg, In, Se, Sr and Te). 3.2.4.2. Muffle furnace experiments Trace elements composition were analysed by ICP, while C, H and N by in accordance with the European standard CEN/TS 15104 using a LECO apparatus, which consists of the sample combustion (at 1050ºC) and elementals to measure in the flue gas. 3.2.5. Description of experiments 3.2.5.1. First batch of experiments in a fluidized bed reactor FB tests were operated in two different ways, explained as follows. The first one consisted in the sequential pyrolysis of several batches of fuel particles (1g of fuel added/batch) and then, at the end, the removal of the whole reactor bed material after cooling the reactor down using an inert gas (N2 gas). The second type of tests consisted in three (3) batches of pyrolysis (3g of fuel added/batch), bed sampling, followed by the combustion of the remaining fuel to start a new set of experiments. After combustion, it was considered that all solid material was consumed, and another pyrolysis-combustion cycle was performed. This type of tests maximized the number of experiments it was possible to execute in a day, since in the other type of experiments, the reactor needed to be cooled down and opened to remove the whole final bed material, which takes about six hours to occur. 3.2.5.2. Second batch of experiments in a fluidized bed reactor Each test consisted in ten (10) additions of approximately 500 mg of fuel, waiting 90 seconds between one addition and the following one. After the test, 10 minutes were waited until a sample of the bed material was taken in the point indicated in the experimental setup (Figure 3.1) and then, before 45 Chapter 3 starting the new test, combustion was performed to burn the remaining solid material in the bed. Bed sampling was conducted the same way as in the first experimental campaign. 3.2.5.3. Muffle furnace experiments In these experiments, samples of approximatively 1g of DSS were placed in the ceramic crucible, covered, and then placed in the furnace once the desired experimental temperature was reached. The sample was maintained in the furnace for 15 min and the removed to cool at ambient temperature. Finally, the sample was weighted and stored until analysis. These tests can be considered as exempt of oxygen since the air amount inside the ceramic crucible is so low and can be considered as zero. . 3.2.6. Definitions for calculation methods The definition of recovery and volatility used in this chapter are described in the next topics, so they can be 3.2.6.1. Recovery Recovery is defined as the percentage of element i in the phase of interest, and it is calculated dividing its amount in this phase by the amount of the same element in the fuel: 𝑅𝑖=𝑚𝑖 𝑝 𝑚𝑖 𝑓∗100 (Eq. 3.1) Where mip is the mass of the element i in the analysed phase (gas/solid) after the thermochemical conversion and mif is the mass of the element i in the fuel. 3.2.6.2. Volatility Volatility is calculated using the following equation: 𝑉𝑖=(𝑚𝑖 𝑓−𝑚𝑖 𝑝) 𝑚𝑖 𝑓.100 (Eq. 3.2) 46 Chapter 3 Where mif is the component i mass in the fuel, while mip is the mass of the element i in the phase of interest (in this case, solid phase) after the thermochemical conversion, what is called solid residue. 3.3. Results and discussion In the next topics, the results obtained in both experimental campaigns using the fluidizing bed set ups and the tests using the muffle furnace will be described. First, at Section 3.3.1 “Product gas containing S, Cl and N”, the content in contaminants derived from S, Cl and N in the gaseous phase in both FB experimental campaigns will be presented. Next, at Section 3.3.2 “Solid residue: analysis their content in major and trace elements” the content of C, H and N as well as trace elements is presented and discussed for the muffle furnace and the two experimental campaigns for the FB devolatilization tests. In this section, the trend of elements with temperature, and the elemental mass balances for the measured trace elements were also presented and discussed. 3.3.1. Product gas containing S, Cl and N In this section, the product gases containing S, Cl and N were analysed for both experimental campaigns of devolatilization tests conducted in the FB facilities. The results of the first experimental campaign conducted in 2017 are related to the content of Chapter 2, since they were being studied simultaneously, i.e., when analysing the results presented in Chapter 2 and learning about the measurement of inorganics in the syngas, the devolatilization tests were being made, therefore, potential measurement inconsistencies were not yet detected. The results of the second experimental campaign conducted in 2018 are also related to the content of Chapter 2. Even though it does not use the same sampling methods, because a mass spectrometer is used to measure the inorganics product gas online (not using impingers as in the first campaign), the issues with corrosive compounds and the reactor material at high temperatures are corroborated and in line with the findings of the second chapter of this thesis. 47 Chapter 3 Either way, these results are not part of Chapter 2, because its aim is to review the literature and to analyse the measurement methodology, without including the additional step of the fuel thermochemical conversion and all the inherent complexity it brings to the measurement dynamics, by including, for example, tars and other major gases. Nevertheless, most of the findings presented below reflect the results 3.3.1.1. First batch of devolatilization experiments H2S As H2S was selected to be measured in the first experimental campaign, since it is the main product gas of devolatilization step. The H2S concentration results are not reliable and so were not included here. They were extremely high and far exceeded the S content in the fuel. Later, it was detected that the UV analyser cannot properly measure H2S with other species, besides N2. In this case, S release to the gas phase was calculated from the S content in the solid residue. HCl Chlorine results in gas phase are available in Table 3.5 below: Table 3.5. Chlorine content in the fuel and syngas. Experiment Fuel Cl in the fuel (mg) Cl in the product gas (mg) 1 DSS 3.51 4.87 2 DSS 2.39 14.91 3 RDF 3.27 9.01 4 RDF 9.92 11.66 5 Wood 0.97 4.47 6 Wood 0.96 4.69 From the analysis of Table 3.5, it is shown that the Cl content measured in the gas phase exceeded the Cl content in the fuel in all tests. This analysis leads to the conclusion - extensively discussed previously - that either the Cl measure in the fuel is not accurate, or the sampling method (bubblers) or analysis method are not suitable. From the previous work included in this thesis – Chapter 2 - HCl sampling with bubblers was proved to be a reliable method, as well as IC. Furthermore, the effect of 48 Chapter 3 316 stainless steel (316SS) on the Cl capture was proved to be very low (~15% loss of Cl), and to decrease its recovery when the material is new (65). Nevertheless, in the work of de Almeida et al (65), HCl was fed into the reactor just with N2, and in these experiments HCl was sampled in a gas containing other compounds, by instance CO, CO2, CH4, H2 and tars in an experiment that HCl is released from a solid fuel particle. This difference may lead to the overall conclusion that Cl is not properly measured in the fuels, or other ions may interfere in Cl measurement by IC and, thus, Cl content in the gas is overestimated. Following our knowledge and experience in analytical chemistry, for instance, there is no evidence that other ions affect Cl measurement by IC, but this possibility must be further investigated. The measurement of fuel-Cl is not simple, since Cl is very volatile, and there is evidence of inaccurate results of Cl in the fuel, even when following European standards (24). Furthermore, the issue reported in Table 3.5 may be a mix of the hypotheses mentioned. An additional option to further investigate this issue would be, besides measuring again the fuel-Cl content by the European standard method based on fuel combustion, would be conducting devolatilization tests using an inert reactor (i.e., a quartz reactor). 3.3.1.2. Second batch of devolatilization experiments H2S During pyrolysis the main products in the gas are H2S and COS. The gas analysis was focused just on hydrogen sulphide because it is the main product, like the first experimental campaign. H2S yields were extremely low compared with the S content in the fuel. In the figure below are present H2S recovery ratios with temperature. 49 Chapter 3 Figure 3.2. H2S recovery at different temperatures for DSS. The recoveries of S in the gas phase were very low compared to literature because for DSS, almost the 95% of S-fuel should be found in the gas phase (4). This low recovery could be, to a minor extent, associated to the lack of the analysis of COS in the gas phase (usually about 5-10%). However, it is mainly linked to potential corrosion issues at a reducing environment and high temperatures (700 - 900°C). This is because, according to the literature, stainless steel is not free from corrosion at such high temperatures. It was reported a reduction of H2S concentration up to 60% in the outlet gas when using a reactor of 316SS (4). In fact, it can be seen the pattern of the H2S increasing until 700°C and then it starts to decrease in the recovery with temperature and this is in accordance with kinetic studies because the reaction rate of H2S with the reactor material increase with temperature, while it is not high enough below the 700°C (4). Several studies on different reactor materials were made and it was found that the quartz reactor is the one that allow almost the 100% of H2S recovery because quartz is an inert material. Kanthal-APM is also suitable when in the fuel contains only S, and not Cl. The use of SS316 for similar tests – fuels containing a high content of sulphur, reducing environment and high temperatures - the results of H2S can be severely compromised. The release of S to the gas phase can also be estimated measuring S in both fuel and solid residues. This is discussed in another topic, because this one is only focused on gas phase measurements. 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 500 600 700 800 900 1000 Recovery (%) Temperature (˚C) 50 Chapter 3 HCl The recovery results of HCl for the devolatilization tests made are showed in the following Figure 3.3: Figure 3.3. HCl recovery at different temperatures for DSS. These results were obtained using the mass-spectrometry technique to measure HCl online at the outlet stream. At 600°C the recovery is very low, because inorganic chlorine that needs higher temperature to be released into the gas phase. At 800 and 900°C, it can be observed that recovery ratios reach approximately 100%, which means that all the HCl in the fuel is released into the gas phase, and this allows to close the mass balance for this compound. In some tests, complementary to the mass-spectrometer tests, HCl was also sampled offline by impingers, and Cl was measured afterwards using the IC technique, as the data obtained in the first experimental campaign. Like the first experimental campaign, more Cl was determined in the product gas phase than in the original fuels (recoveries that surpass 100%), when using the bubblers (impingers) as a sampling method. Figure 3.4 shows HCl recoveries trend with temperature for both methods: 0.0 20.0 40.0 60.0 80.0 100.0 120.0 500 600 700 800 900 1000 Recovery (%) Temperature (˚C) 51 Chapter 3 Figure 3.4. HCl recovery ratio at different temperatures for DSS using bubblers (orange) and mass spectrometer for HCl (green). The same trend of Cl recovery increasing with temperature was observed for both methods: mass spectrometer (green points) and bubblers (orange points). However, as mentioned before, with impingers, the recovery is much higher and thus not acceptable as a reliable result. Cl release to the gas phase is very complex to be estimated by measuring fuel-Cl and the Cl content in the product solid residue. This is because the Cl content is very low in the product solid residue, due to the high volatility of Cl. Therefore, measurement of Cl in the product solid residue was not included in this study. NH3 and HCN HCN was not measured with MS, because it was not possible to calibrate the equipment for this compound because HCN is a dangerous species. HCN was sampled in some tests with impingers, but it was impossible to analyse its content in the recovered solution due to the influence of other ions (buffer solution added to maintain the basic pH). Just NH3 was measured with MS and Figure 3.5 below shows the tendency of N-recovery from it: 0.0 100.0 200.0 300.0 400.0 500.0 600.0 500 600 700 800 900 1000 Recovery (%) Temperature (˚C) Bubbler HCl 52 Chapter 3 Figure 3.5. NH3 recovery ratio at different temperatures for DSS. Results show very low recovery of NH3. The decrease in NH3 with temperature is in accordance with equilibrium studies that show the same behaviour. A hypothesis that might explain the low recovery of fuel-N in form of ammonia is that the DSS ash might have a catalytic effect on NH3 production due to its high content in metals, resulting in a very low amount of NH3 in the product gas (66). A similar example, not of the catalytic effect of ash, but of another component, is dolomite. When used as bed material, dolomite might increase the conversion of fuel-N into NH3 by affecting the whole gasification process, including the char conversion, steam conversion, gas composition, etc. Furthermore, it can catalyse the transformation of other nitrogenous compounds into ammonia (67). N release to the gas phase can also be estimated by measuring fuel-N and the N content in the solid residue, as for S, and this will be further discussed in another topic (solid samples). 0.0 0.5 1.0 1.5 2.0 2.5 3.0 500 600 700 800 900 1000 Recovery (%) Temperature (˚C) 53 Chapter 3 3.3.2. Solid residue: analysis their content in C, H, N and trace elements 3.3.2.1. Muffle furnace tests Tests in muffle furnace were made only for DSS. Results are shown in terms of metals concentration in the solid residue and metals volatility. Table 3.6 shows the DSS initial mass, solid residue mass and R, which is the ratio of the mass of the solid residue and the mass of the initial fuel, for all the tests performed. Table 3.6. Initial mass of fuel, solid residue mass and the ratio for all the tests performed for DSS. Test T (ºC) Fuel initial mass (g) Solid residue mass (g) Ratio Average Error (%) 1 500 1.08 0.52 0.48 0.46 2.33 2 500 1.03 0.47 0.46 3 500 1.03 0.46 0.45 4 700 1.04 0.40 0.38 0.43 4.14 5 700 1.00 0.41 0.41 6 700 0.96 0.35 0.36 7 900 0.97 0.33 0.34 0.41 2.61 8 900 0.98 0.30 0.31 9 900 1.06 0.35 0.33 Ratio means the solid residue mass divided by the fuel initial mass. It slightly varies for tests at the same temperature, and the associated errors are below 5%. The solid residues compositions in terms of C, H, N, and trace elements are presented in Tables 3.7 and 3.8 below. 60 Chapter 3 3.3.2.4. Analysis of the behaviour of trace elements by their concentration with the temperature From the results of all tests performed in the different experimental campaigns - one in the muffle furnace, and two using fluidized bed reactors - it is possible to analyse the concentration of the elements in the solid residue, and then comparing its change with the tests’ parameters, for instance: fuel, temperature, and rig type (muffle furnace or fluidized bed). To enable the analysis of the behaviour of the different trace elements during the devolatilization tests, it is possible to observe the change in their concentration depending on the process conditions, in this case, the temperature. To better understand how the concentrations are expected to change compared to their concentration in the fuel, or to another process condition, the following principles are proposed with temperature increase for a given element i: ⎯ The concentration of an element i decreases: the component has volatilized and was released to the gas phase. It can condensate on flying ash particles downstream or be released to the atmosphere, if not captured in gas cleaning systems. This behaviour is expected for volatile components, such as: As, Hg, Pb and Cd. ⎯ The concentration of an element i remains the same or slightly changes: the component has volatilized, but not as much as in the previous case. It means that the component loss was the same as the solid residue mass loss. This behaviour is expected for less volatile components, such as Zn. ⎯ The concentration of an element i increases: the volatility of the component i is very low, or the component i has completely remained in the solid (volatility = 0). This behaviour is expected for low/no volatile components, such as: Mn, Cu, Cr, Ni. Therefore, the following sections compare: ▪ The concentration of trace elements in the solid residues from the tests conducted at the second experimental campaign in the fluidized reactor (2018) at different temperatures and for each fuel type (DSS and wood pellets). ▪ The first (2017) and the second (2018) experimental campaigns at the fluidized bed reactors at different temperatures, using DSS as fuel. ▪ The muffle furnace tests and the second experimental campaign (2018) at the fluidized bed reactor at different temperatures, using DSS as fuel. 61 Chapter 3 Second experimental campaign at the fluidized bed reactor Figure 3.6 below enables the analysis of temperature effect on DSS devolatilization at N2 atmosphere. a) b) c) Figure 3.6. Concentration of trace elements – a) major, b) intermediate, and c) minor elements - in the solid residue from devolatilization tests at N2 atmosphere using DSS as fuel, at different temperatures: 600, 700, 800, and 900 ºC. Major: C ≥ 10000 / Intermediate: 10000 < C ≤ 100 / Minor: C < 100 in mg/kg of sample. 0.0 20000.0 40000.0 60000.0 80000.0 100000.0 120000.0 140000.0 Al Ca Fe Mg P mg / kg of sample Major 600 700 800 900 0.0 2000.0 4000.0 6000.0 8000.0 10000.0 Ba Cr Cu KMn Na Ni Sn Sr Ti Zn mg / kg of sample Intermediate 600 700 800 900 0.0 20.0 40.0 60.0 80.0 100.0 120.0 As Cd Co Pb Sb Se Te V mg / kg of sample Minor 600 700 800 900 62 Chapter 3 Based on the Figure 3.6 above and the explanation about trace elements behaviour, it can be observed that for DSS that the elements behaviours are the following: ▪ Most volatile elements: P, Sn, Zn and Pb. ▪ Least volatile elements: Ca, Fe, Mg, Al, K, Na, Ti, Co, and V. ▪ Elements which concentration remain the same with temperature increase: Ba, Cu, Ni and Sr. This indicates that the element i presents the same mass loss trend as the fuel during devolatilization. 63 Chapter 3 Figure 3.7 below enables the analysis of temperature effect on wood devolatilization at N2 atmosphere. a) b) c) Figure 3.7. Concentration of trace elements – a) major, b) intermediate, and c) minor elements - in the solid residue from devolatilization tests at N2 atmosphere using wood pellets as fuel, at different temperatures: 700, 800, and 900ºC. Major: C ≥ 100 / Intermediate: 100 < C ≤ 5 / Minor: C < 5 in mg/kg of sample. 0.0 5000.0 10000.0 15000.0 20000.0 25000.0 Al Ca Fe KNa Mg Mn P mg / kg of sample Major 700 800 900 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 Ba Cr Cu Ni Sr Ti Zn mg / kg of sample Intermediate 700 800 900 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 As Cd Co Pb Sb Sn Se Te V mg / kg of sample Minor 700 800 900 64 Chapter 3 First (2017) and second experimental (2018) campaigns at the fluidized bed reactor Figure 3.8 below presents the results for DSS devolatilization at 800°C for the first (2017) and the second (2018) experimental campaigns. a) b) c) Figure 3.8. Concentration of trace elements – a) major, b) intermediate, and c) minor elements - in the solid residue from devolatilization tests at N2 atmosphere using DSS as fuel at 800ºC, at different experimental campaigns: the first one conducted in 2017 (particle size: 840μm ≤ DSS ≤ 2mm) and the second one conducted in 2018 (particle size: 500μm ≤ DSS ≤ 840μm). Phosphorous (P) was measured only in the second experimental campaign and, thus, its concentration is zero for the first one. 0.0 20000.0 40000.0 60000.0 80000.0 100000.0 120000.0 140000.0 Al Ca Fe Mg P mg / kg of sample Major 800 (2018) 800 (2017) 0.0 2000.0 4000.0 6000.0 8000.0 10000.0 12000.0 Ba Cr Cu KMn Na Ni Sn Sr Ti Zn mg / kg of sample Intermediate 800 (2018) 800 (2017) 0.0 20.0 40.0 60.0 80.0 As Cd Co Pb Sb Se Te V mg / kg of sample Minor 800 (2018) 800 (2017) 65 Chapter 3 Comparing results for devolatilization tests at 800C and N2 atmosphere, results in terms of concentration of trace elements in the solid residue are similar. Variations can be attributed to the differences of trace element in the fuel (2017/2018), the fuel size (particles from the last experimental campaign are smaller) and the intrinsic fuel heterogeneity. Major elements results are very similar. Note that P was not quantified in 2017, so comparisons could not be made. For intermediate elements, the most accentuated differences are found for Cu, K and Na, being the concentrations of Cu and K lower in 2018, while Na concentration was higher. In the case of minor elements, Sb is the only element which concentrations differ considerably, being much higher in 2018. Muffle furnace and last experimental campaign (2018) at the fluidized bed reactor The following Figure 3.9 compares trace elements concentrations for tests using DSS at the muffle furnace (mf) and the fluidized bed (FB) reactor in the second experimental campaign (2018). 66 Chapter 3 a) b) c) Figure 3.9. Concentration of trace elements – a) major, b) intermediate, and c) minor elements - in the solid residues from DSS devolatilization tests at N2 atmosphere: comparison between different temperatures from muffle furnace (mf) tests - 500ºC, 700ºC and 900ºC - at and fluidized bed tests (2018) - 600ºC, 700ºC, 800ºC and 900ºC. Major: C ≥ 10000 / Intermediate: 10000 < C ≤ 100 / Minor: C < 100 in mg/kg of sample. 0.0 40000.0 80000.0 120000.0 160000.0 Al Ca Fe Mg P mg / kg of sample Major 500 mf 600 700 mf 700 800 900 mf 900 0.0 5000.0 10000.0 15000.0 20000.0 Cr Cu KMn Na Ni Zn mg / kg of sample Intermediate 500 mf 600 700 mf 700 800 900 mf 900 0.0 50.0 100.0 150.0 200.0 As Cd Co Pb Sb V mg / kg of sample Minor 500 mf 600 700 mf 700 800 900 mf 900 67 Chapter 3 The concentration of major elements (Al, Ca, Fe, Mg, P) are very similar for both type of tests (mf/FB). All elements show the tendency of slightly increasing their concentration with temperature, which shows that they are not volatile, since they tend to stay in the solid. Moreover, it is possible to conclude that the high concentrations of major elements, for instance, at least Al, Ca, and Fe in the samples of the solid residues from the fluidized bed tests, are not because a potential contamination from bed material, in this case, sand, which present a high content in these species, since concentrations do not differ from the muffle furnace tests. The concentrations of most elements that are in the “intermediate” scale were not affected by the difference in the experiment rig (mf/FB). K and Zn were the ones in which some differences were noted, being their concentrations higher in muffle tests. The concentration of minor elements in the solid residues varied more for both type of experiments and same conditions. The following observations are highlighted: ▪ As concentration is not affected either by temperature or experiment configuration (mf/FB). As is very volatile, so it is released at low temperatures. The temperature increases (≥ 500°C) or the fluidization effect cannot increase its volatility since it is soon released to the gas phase. ▪ The concentrations of Cd and Pb are higher in the muffle furnace experiments. This might be explained that the effect of fluidization supports their release, decreasing their concentration in the FB solid residue. During the fluidization, the particles are heated faster, which might contribute their release to the gas phase. ▪ Sb and V concentrations are lower in muffle furnace experiments, which suggests that the fluidization decrease their volatility. ▪ Pb and Sb are the elements which concentrations are more affected by experiment type. Pb lower concentrations in FB could be due to particles mixing, since they break and move, volatility could be favoured. However, this do not explain Sb higher concentration in FB. It can be observed that the concentrations of the major elements are not affected by the experimental setup, since they tend to have low volatility. Furthermore, the similar result for both configurations suggests that the solid residues from the fluidized bed tests are not contaminated by the bed material, sand. 68 Chapter 3 3.3.2.5. Elemental mass balance for the trace elements in the fluidized bed tests It is known that with temperature increase, the resulting solid residue mass decrease. This is mainly due to carbon and other major elements loss/release to the gas phase, the release of volatiles to the gas phase (68). In the muffle furnace tests, it was possible to collect all the solid in the end of the test (called here as “solid residue”). However, in the tests performed in the fluidized bed reactors, it was not possible to collect all the solid residue in the end of the test and, thus, determining the final mass of the solid residue with precision, enabling the calculation of a precise elemental mass balance. This is because, during the fluidized bed tests, there are several events that prevent the full collection of the solid residue, being the most important: the solid residue break down into smaller particles and its mixing with the bed material. If the solid residue particles are very small, like dust, and it is impossible to recollect it. Furthermore, collecting all the bed material in the end of each test and then separating the solid residue from the bed is a very time-consuming task, which delays the experiments schedule considerably. That is why the masses of solid residues are unknown in the fluidized bed tests. The expected final mass of the solid residue be done by using the results from the muffle furnace tests (see Table 3.6), since in this type of tests the solid residue can be collected and weighted. Even if the fuel does not behave the same way in a fluidized bed reactor compared to a fixed bed test, it is possible to estimate the solid residue mass with high precision using this approach. Figure 3.10 below shows the ratio of the mass of the solid residue with the mass of the fuel (axis y) versus the temperature in degree Celsius for the muffle furnace tests using DSS as fuel. 69 Chapter 3 Figure 3.10. Ratio between the solid residue mass and the initial mass of the fuel versus the temperature for the muffle furnace tests using DSS as fuel. The square points were calculated by using the estimation provided by the experimental points (black diamonds). In any case, it is important to highlight that both the fuel and the solid residue analyses in content of main (i.e., C, H, N) and trace elements (i.e., As, Cu, Ni) are done by using samples of these solids. As in this thesis the focus is on wastes, their content is not homogenous and may vary, i.e., the content of DSS or RDF is not standardized as the woody biomass pellets. Therefore, a very precise elemental mass balance is very challenging in this scope, even in the muffle furnace tests, leading to variations in the results. For instance, by establishing that the solid mass residue for the fluidized bed tests follows the trend with temperature as for the muffle furnace tests and using the elements’ concentrations both in the fuel and in the solid residues, variations in the mass balances can be encountered (i.e., finding that the amount of an element i is higher in the output stream than its initial content in the inlet stream, which is not possible). However, as explained, such inconsistencies are linked to the intrinsic heterogeneity found when using waste-derived fuels (15). By estimating the mass of the solid residue, it is possible to estimate the mass of the elements in the solid at the end of the process. This way it is possible to analyse the elemental mass balance of such elements and estimate, for each element i, how much stayed in the solid, and how much went to the gas phase. Through equation 3.2, it is possible to calculate the volatility of an element i. However, this approach does not give good results, since when the elemental mass balances are performed, for most elements, the masses of the elements in the solid stream are higher than in the y = -0.085ln(x) + 0.9897 R² = 0.999 0.39 0.4 0.41 0.42 0.43 0.44 0.45 0.46 0.47 0.48 400 500 600 700 800 900 1000 Ratio Temperature (ºC) 76 Chapter 4 experiments. The fuel-N and fuel-S were mainly converted into ammonia (≥ 40%) and H2S (≥ 20%). Most of fuel-Cl was measured in the filter ash, whereas only a fraction of the fuel-S was detected in the filter ash, which decreased with higher filter temperature. The distribution of trace elements into the filter and bottom ashes was consistent with their inherent volatile behaviour, although precise quantification was difficult due to the heterogeneity of the fuel. Preliminary assessment of ash utilization/disposal options of filter and bottom ashes generated was made by studying the enrichment factor. 4.3. Literature review Several works in the literature have studied the content of inorganics and trace elements during the fluidized bed gasification of solid waste in the last years. Table 4.1 below shows a selection of these works. 77 Chapter 4 Table 4.1. Literature review of the inorganics and trace elements during the gasification of waste in fluidized bed. NA: Not applicable / NM: not measured Waste fuel Studied elements in the product gas and solid System mode Analysed parameters Year Reference Inorganics Trace elements * Major Minor Sewage Sludge NM NM Cd, Cu, Ni, Pb, Zn Fluidized bed reactor (continuous) Residence time 2011 Syc et al. (71) Municipal solid waste (MSW) HCl, H2S, NH3 Al, Ca, Fe, K, Mg, Mn, Na, P, Si As, Cd, Co, Cr, Cu, Hg, Ni, Pb, Sb, Si, V, Zn Pilot scale bubbling fluidized bed (continuous) Temperature 2014 Arena & Di Gregorio (72) Solid recovered fuel and woody biomass (waste wood and beech wood) H2S, COS / N and Cl ** NM NM Lab scale fluidized bed air gasification (continuous) Temperature and atmosphere 2019 Valin et al. (8) Poultry litter H2S, NH3, S (solid) Al, Ca, Fe, K, Mg, Mn, Na, P, Si As, Ba, Cd, Co, Cr, Cu, Hg, Mo, Ni, Pb, Sb, Si, Tl, Ti, V, Zn Bench scale bubbling fluidized bed (continuous) Temperature for inorganics in gaseous form 2020 Pandey et al. (63) N-rich fibreboard HCN and NH3 Al, Ca, Fe, K, Mg, Mn, Na, P, Si Zn Air blown fluidized bed gasification Hot gas filtration on hydrogen cyanide and ammonia and distribution of trace elements between cyclone and filter 2021 Ruiz et al. (64) References: Valin et al. (8); Syc et al.(71); Arena & Di Gregorio (72); Pandey et al. (63); Ruiz et al. (64); * Minor elements are considered the ones in concentrations lower than 0.1% (<0.1% or <100mg/kg or <0.1g/kg) ** The measurements of nitrogen and chlorine were not satisfactory, with elemental balances far from being closed, so results were not reported 78 Chapter 4 4.4. Experimental 4.4.1. Test rig Gasification experiments were conducted as part of the Brisk2 program at VTT's bench-scale gasification, which consists of an atmospheric bubbling fluidized-bed gasifier connected to a gas filter and a reformer/gas residence time tube (Figure 4.1). The so-called gas residence time tube is an electrically heated pipe that can also be operated as a catalytic reformer. In this work, our interest was on the gasification step and subsequent filtration, so the reformer was used as a gas residence time tube. The maximum fuel feeding rate is 5 kg/h, and the gasification tests can be conducted with air, air/steam, oxygen/steam or at steam-blown mode. Figure 4.1. VTT's bench-scale, atmospheric pressure gasification test facility BFB100. The gasifier (bottom bed i.d. 100 mm, freeboard i.d. 150 mm) is surrounded by electrically heated ovens, which compensate thermal losses and supply extra heat to operate at a pre-set gasification temperature. Gasifying agents (steam, air, nitrogen) are introduced into the reactor's bottom through a multi orifice plate distributor and controlled through mass flows. The gases are pre-heated before 79 Chapter 4 entering the reactor and, in this study, water was introduced directly with the pre-heated air to generate steam (since the steam concentration was only about 20 vol-% of fluidizing gases). Temperature is continuously measured in the gasifier at different points in the bottom bed (125, 225 and 425 mm from the plate distributor) and in the freeboard (1270 and 1470 mm from the plate distributor). Pressure drop in the bed and distributor plate; freeboard and the whole reactor are continuously monitored. A small amount of purge nitrogen (about 7 vol-% of the total feed gas flow) is fed with the fuel for inertization and pressure stabilization purposes. Adding this nitrogen aims to flush the measurement lines (pressure or temperature) in the bed and prevent the loss of bed material into these lines. Bed material is added as a batch at the beginning of each experiment. The fuel feeding system is composed of two subsequent tanks and feeding screws. The first one is located at the tank bottom and adjusts the fuel feeding rate. The second feeding screw adjusts the first screw speed, and it is located inside water-cooled jackets. Fuel feeding is located 101 mm above the plate distributor. The hot gas filter downstream of the reactor separates the elutriated bed material and fly ashes from the gas. Inside the vessel, two rigid ceramic filter elements (Pall Corporation: Pall Dia-Schumalith 10- 20 KK). The filter is electrically heated with two-zone oven (upper and lower zone), with a maximum temperature of 850°C. The solids are detached from the filter through nitrogen pulses (2 bars and 150 ms) and then collected in the bottom container. Temperature is measured both at filter inlet ("dirty" side) and outlet ("clean" side). The dust-free gas then flows into a gas residence time tube (i.d. 100 mm) covered by electrically heated ovens. It was maintained at 350 – 400°C to prevent tar condensation in the pipeline. The gas is then incinerated for final combustion. 80 Chapter 4 4.4.2. Materials 4.4.2.1. Feedstock and bed materials Crushed Refuse Derived Fuel (RDF) pellets were used as feedstock. The original pellets (2 cm of diameter) were ground with Weima WL 2 Labor crusher (with a 15 mm sieve). The final RDF particles were ≤ 15 mm. The proximate and ultimate analyses of the feedstock are presented in Table 4.1. Table 4.1. Proximate and ultimate analysis of the feedstock Feedstock RDF pellets Error (+/-), % Standard applied Moisture content, wt% 4.1 2 SFS-EN ISO 18134-2 LHV, MJ/kg (dry)* 23.9 1 - Proximate analysis, wt% (d.b.) Volatile matter 80.5 1 SFS-EN ISO 18123 Fixed carbon 10.2 3 Calculated Ash (550 °C) 9.3 2 SFS-EN ISO 18122 Ultimate analysis, wt% (d.b.) C 55.3 1 SFS-EN ISO 16948 H 7.9 2 SFS-EN ISO 16948 N 0.5 7 SFS-EN ISO 16948 S 0.1 6 ASTM D 4239 (mod), SFS-EN ISO 16994 Cl 0.7 13 SFS-EN ISO 10304-1 O (cal. as difference) 26.3 3 Calculated Ash (550 °C) 9.3 2 SFS-EN ISO 18122 *LHV was calculated based on fuel composition (73) Table 4.2 contains the composition of RDF pellets in inorganic elements in mg/kg of dry fuel. Major elements were measured by Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) by applying the SFS-EN ISO 17294-2 standard (74). Minor elements by ICP Mass Spectroscopy (ICPMS), using the SFS-EN ISO 11885 standard (75). By following the standards, the samples were digested using an acid solution. 81 Chapter 4 Table 4.2. RDF composition in major and minor inorganic elements (mg/kg of dry fuel) Major elements, mg/kg dry fuel Error (+/-), mg/kg dry fuel Error (+/-), % Al 17600.0 200.0 1 Ba 1150.0 50.0 4 Ca 2550.0 50.0 2 Fe 1700.0 10.0 1 K 415.0 15.0 4 Mg 1150.0 50.0 4 Na 7450.0 550.0 7 P 8150.0 450.0 6 Si 2050.0 50.0 2 Ti 210 10.0 5 Minor elements, mg/kg dry fuel As 1.0 0.1 10 Cd 3.4 2.0 59 Co 7.1 0.8 11 Cr 58.5 28.5 49 Cu 43.0 1.0 2 Hg 2.2 0.1 5 Mn 47.0 4.0 9 Mo 4.3 1.1 26 Ni 6.3 0.7 11 Pb 35.0 3.0 9 Rb 4.3 0.1 2 Sb 31.5 8.5 27 Sn 7.4 0.4 5 Sr 39.0 0.1 <1 V 2.9 0.1 3 Zn 215.0 45.0 21 Silica sand and dolomite (Myanit B by Björka Mineral) were used as bed material, with a particle size of ≤ 0.25 mm and 0.28 – 0.55 mm, respectively. The Sauter mean diameter (d32) of the RDF particles is 10 mm, while the sand d32 is 0.18 mm and the dolomite d32 is 0.40 mm, with a minimum fluidization velocity (umf) of 0.016 m/s for sand and 0.099 m/s for dolomite at 25°C, calculated using Wen and Yu’s correlation (76). 4.4.2.2. Filter elements Pall Dia-Schumalith 10-20 KK filter elements (o.d./i.d. 60/40 mm, length 1000 mm) by Pall Corporation were employed in this work. It consists of a coarser support body (material: SL 20) with a porosity of 38% and a dense membrane (material: mullite grains) as a top layer (77). 82 Chapter 4 4.4.3. Sampling and analytical methods The composition of the product gas was measured after the filter unit (Figure 4.1). The sampling lines were heated to 350-400°C. Continuous gas analyser (ABB AO2020) was used to measure CO, CO2, H2, CH4 and O2. Non-dispersive infrared absorption was used to measure CO, CO2 and CH4. H2 and O2 were measured by thermal conductivity and a magneto-mechanical oxygen analyser, respectively. The hot gas filter's sampling line to the gas analyser consisted of a condenser and a filter. The condenser is used to cool the gas to 2°C to condense the heaviest tar compounds and water vapor (78). Afterwards, the gas is led to the filter, consisting of a filter case filled with cotton wool. After the analyser (ABB AO2020), the gas was transferred through a Teflon tube to an online micro gas chromatograph (HP3000, equipped with a TC detector), which was used to analyse CO, CO2, O2, N2 and light hydrocarbons (C2 – C5 hydrocarbons). Samples were taken every 7 min. The continuous gas analyser is focused on monitoring the process's stability, while the micro gas chromatographs results are used to calculate the mass balances discussed in the next section. Tars were sampled according to the European Tar protocol (79). Isopropanol was used as an absorbent. Samples were analysed by a gas chromatograph (Agilent 6890A, column: Agilent 19091B-112 Ultra 2) equipped with a Flame Ionization Detector (FID) detector, for benzene and compounds up to coronene (C24H12, 300.35g/mol). A complete list of the 52 compounds analysed is available in the Appendix section. Water content was determined from the tar samples using a gas chromatograph (HP 5890 series II, column: PoraPLOT U) equipped with a TC detector. Gaseous pollutants such as H2S, COS, NH3, HCN and HCl were also analysed during the tests, all collected after the filter unit. Ammonia (NH3), hydrogen cyanide (HCN) and hydrogen chloride (HCl) were collected in a set of impingers. A gas stream of 5-6 l/min was drawn from the process and led through six impinger bottles placed in series. The first three bottles were filled with their proper capture solution for each substance, described separately below. The last three impingers were washing bottles used to extract water and tars from the gas for all substances. The fourth impinger contained 70 ml of isopropanol, the fifth one was filled with 70 ml of isopropanol and glass beads, and the last impinger contained only glass beads. The first four impingers were placed in an ice/water bath (< 5 °C) and the last two in an ice/water/NaCl bath (< -15 °C). 83 Chapter 4 For ammonia capture, the first three impingers contained 70 ml (each) of 5% H2SO4 solution. The first three impingers' contents were combined after sampling, and the glassware was also rinsed with a small amount of 5% H2SO4 solution and added to the collected sample. The final sample was preserved in a tightly sealed glass bottle before analysis (away from sunlight and below 5 °C). The pH of the sample was kept below 2. The sample was analysed using the Kjeldahl distillation method according to ASTM D1426-93 standard. For hydrogen cyanide, the first three bottles were filled with 70 ml of 5% NaOH solution (each). The pH of the washing liquid was maintained at above 10.5 during sampling. The three impingers' contents were combined after sampling, and the glassware was also rinsed with a small amount of 5% NaOH solution, which was added to the final sample. The sample was analysed within 6 hours of sampling to prevent cyanide from reacting further with sulphur compounds. The sample was analysed with a static headspace method using a gas chromatograph equipped with FID. Hydrogen chloride sampling was performed by using six impingers. The first three impingers were filled with 100 ml of ion-exchanged water and glass beads (each). The fourth and the fifth impingers contained 70 ml of isopropanol and glass beads each, and the sixth one contained only glass beads. The contents of the first three impingers were combined after sampling. The quartz probe was also washed with ion-exchanged water, and the washing liquid was poured into the sample bottle. The sample was preserved below 5°C (and away from sunlight) before analysis. The sample was analysed by ICP-OES. Gaseous H2S and COS samples were collected in Teflon gas bags and analysed for sulphur compounds using a gas chromatograph equipped with FPD (Agilent 7890B) and analysed within a few hours of sampling. Note that H2S was also measured online using Dräger tubes, but this method is not as accurate as offline methods. Thus, sulphur elemental mass balances were performed using offline results. 4.4.4. Description of experiments and calculation methods The gasification tests were conducted under air/N2 and air/steam conditions with crushed RDF pellets as feedstock and sand and dolomite (Myanit B) as bed materials. All tests were carried out at the same gasifier temperature, using varying the Equivalence Ratio (ER), bed material, hot filter temperature 84 Chapter 4 and fluidizing agent. In each test, one of these parameters was varied separately to appreciate their effect on the results. The operating conditions are summarized in Table 4.3. Table 4.3. Target conditions in the air+N2 and steam/air gasification experiments Gasification experiments 1 2 3 4 5 6 Feedstock RDF RDF RDF RDF RDF RDF Particle size, mm ≤ 15 ≤ 15 ≤ 15 ≤ 15 ≤ 15 ≤ 15 Bed material Sand Sand Sand Sand Sand Dolomite Particle size, mm 0.15 - 0.25 0.15 - 0.25 0.15 - 0.25 0.15 - 0.25 0.15 - 0.25 0.28 - 0.56 Target conditions Bed temperature, °C 850 850 850 850 850 850 Freeboard temperature, °C 850 850 850 850 850 850 Filter temperature, °C 450 450 450 550 450 450 Fluidizing velocity*, m/s 0.40 0.40 0.40 0.40 0.40 0.40 ER 0.30 0.25 0.30 0.30 0.25 0.30 steam in fluidizing gases, vol-% - 0.20 - - - - Steam-to-fuel feed ratio, kg H2O/kg fuel (daf) - 0.40 - - - - Target compounds to be measured N, S N, S Cl Cl N, S N, S daf: dry and ash-free *calculated Six gasification tests were conducted with fuel particle size ≤15mm, and each experiment lasted at least 4h at steady state except for experiment 6, which lasted only 3h. The filter temperature was kept at 450°C, except for one test, in which it was increased to 550°C. Before each experiment, the rig was electrically heated up close to the test target temperature in nitrogen atmosphere. The bed material was added at the beginning of each test, and a static bed height was maintained at 35 cm in all tests. For the test using dolomite, the loss of solids during calcination was considered and, thus, more bed material was added, so the bed height was 35 cm after calcination. The dolomite was calcinated in-situ before the test start, by adding a batch of the bed material (dolomite) in the reactor that was maintained there for approximately 2h at 800°C. The CO2 released was monitored and when the CO2 stopped being detected we proceeded to the experiment. No additional bed material was added during the tests using sand as bed material. Fuel feeding was initiated to start the gasification experiments, and the feed gas was switched to the fluidization agent selected for each test (air/N2, air/steam). The operation conditions were kept stable during the tests. Every hour the filter was pulse cleaned by the addition of nitrogen. The particulate matter entrained by the gas (fly ash and bed material) were collected at a nitrogen pressurized vessel to cool, and then, 85 Chapter 4 the detached solids were stored for further analysis. The bottom ash was removed only after the experiment completion and the cooling process in N2 atmosphere. Both filter and bottom ash samples were analysed in C, H, N, O, S, Cl, and ash for all tests. Nevertheless, for test 6, bottom ash results cannot be used directly, as C comes from the CaCO3 used as bed material. Trace elements (Table 5) were measured in both filter and bottom ash samples for tests 3 and 4. After each test run and the removal of the bed material, the remaining carbon-containing matter in the filter was oxidized with nitrogen/air mixture. The oxidation data was used to calculate the carbon release in the filter. Mass balance calculations were conducted for all experiments using average values from the measured data (solid and gas streams). The dry gas flow rate was calculated from nitrogen balance in all tests. The water content in gas was calculated by measuring the water content in the tar samples and hydrogen balance. Carbon conversion to gas and tars was calculated by two methods: (1) directly by the C content in gas and tars divided by the total carbon input in the fuel, and (2) indirectly by the total carbon input in the fuel, deducting the carbon losses (C in fly ash, bottom ash, and residual in the gasifier and on the filter), and dividing it by the total carbon in the fuel. Average dust content in gas (g/s) was calculated by dividing the mass of the accumulated dust in the filter (g), divided by the setpoint duration (s). The cold gas efficiency, CGE (lower heating value in the syngas/lower heating value of the fuel) and the hot gas efficiency, HGE, (sensible energy plus the lower heating value of the syngas/lower heating value of the fuel) were calculated for all tests. The species considered for calculating the syngas lower heating value were CO, CO2, H2, CH4, C2H2, C2H4, C2H6, C4H10 (n-butane, representing C3 – C5 fraction). 4.4.5. Tars classification Tars were classified based on their molecular weight, divided into four groups: (1) benzene, (2) light tars, (3) naphthalene and (4) heavier tars: tars heavier than naphthalene, up to coronene. GC- unidentified tars were also included as they were present in high amounts and were described as 92 Chapter 4 decreases with ER (68)(85). The same tendency is observed for the HGE, which varied from 81.4 – 85.8%. 4.3.2. Tars The average concentration (mg/m3n) of different tar groups and benzene sampled after the filter is presented in Figure 4. Tars were separated in the following groups: light, heavier, unknown, naphthalene and benzene (78). Figure 4.4. The concentration of different tar groups and benzene after the filter in air/steam and air+N2 gasification experiments with RDF and sand or dolomite (Myanit B) as bed material. Total tars stands for the sum of light, heavier and unknown tar groups. Benzene was the predominant species in all experiments (Figure 4), followed by heavier tars, naphthalene, light tars, and unknown groups. Due to the lower presence of oxygen, the lower the ER, the higher the yield of tars produced. Even though in-bed dolomite is expected to decrease tar concentration due to its catalytic effect (78)(86)(28), the effect of dolomite decreasing the tars was not observed in our experiment (6, ER=0.3), comparing with experiments 1 and 3 with similar conditions. More experiments should be conducted to evaluate the catalytic effect of dolomite on tar cracking. Benzene Light tars Naphthalene Heavier tars Unknown Total tars (Gcunidentif. incl.) Test 1 17.9 3.5 5.8 6.1 2.7 12.3 Test 2 23.6 4.3 7.8 8.4 4.3 17.0 Test 3 14.3 2.8 4.5 4.8 2.1 9.8 Test 4 14.7 2.8 4.6 4.8 1.9 9.5 Test 5 20.3 4.2 6.9 7.7 4.5 16.4 Test 6 16.8 3.9 5.2 5.1 2.4 11.4 0.0 5.0 10.0 15.0 20.0 25.0 Tar concentration, g/m3n (per dry gas) 93 Chapter 4 Different filter temperatures were set in experiments 3 (Tfilter = 450°C) and 4 (Tfilter = 550°C) at the same ER (0.32). At lower filter temperature, it is expected that the heavier tars may condense more in the filter. Nevertheless, the reduction of tar concentrations has been only reported at a higher filter temperature (87). In our experiments, the tars concentrations in tests 3 and 4 were very similar, since conditions are the same (T and ER) and the filter temperature gap was only 100°C. 4.3.3. Conversion of fuel-N Figure 4.5 shows the fuel-N conversion into filter dust-N, tar-N, HCN and NH3. HCN and NH3 are expected to be the main species derived from fuel-N. Quantification of NOx and HNCO were not included in the paper, because NOx is not formed in reducing conditions at large scale and HNCO is not stable so it would ultimately be transformed to NH3 and HCN (88)(41). Figure 4.5. Conversion of fuel-N (wt-%) into N2 (calculated by difference), filter dust-N, tar-N, HCN and NH3. In experiments 3 and 4 ammonia and hydrogen cyanide were not sampled. In all experiments in which ammonia was measured (1, 2, 5 and 6), it was the main measured product from the conversion of fuel- 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 air+N2 air+steam air+N2 air+N2 air+N2 air+N2 0.28 0.24 0.32 0.32 0.25 0.30 Conversion of fuel-N, wt-% Fuel-N conversion, wt-% Filter dust-N Tar-N HCN NH3 Test Conditions ER ** *T(filter) = 550 °C **dolomite * 94 Chapter 4 N. Ammonia concentration was much more significant than hydrogen cyanide, as expected for several types of feedstocks (4). Nevertheless, higher concentrations of HCN than NH3 during solid recovered fuel (SRF) gasification have been reported (33). Although the chemistry of Fuel-N evolution during gasification is not yet completely understood, it is known that HCN is formed during the pyrolytic conversion of pyrrolic and pyridinic forms (from polyamides and polyacrylonitriles, present in textile). In contrast, NH3 is formed from amino-groups (biomass). RDF is a mix of different fractions - for instance, organic, plastic and textile – the Fuel-N conversion from different sources must be understood. In this work, ammonia was the predominant species probably due to the high content in organic matter in the fuel. Besides the effect of the feedstock, experimental conditions, such as temperature, residence time, ER, fuel feeding location and heating rate also affect Fuel-N conversion (33)(89)(90). By comparing Tests 1 and 5, the ER increase is found to boost ammonia production, probably concerning higher char conversion, in agreement with previous findings (65)(91). The addition of steam (test 2) increased the ammonia production, due to the higher availability of hydrogen. HCN can react with H2O to produce ammonia and CO, and also HNCO (isocyanic acid) can react with steam to form ammonia and CO2, explaining the higher NH3 content found in this experiment (90)(92)(93)(94)(95). The conversion of fuel-N into HCN decreased with the increase in ER. This may be explained by the ammonia production being favored over HCN when more oxygen is available. Fuel-N conversion into ammonia was highest in experiment 6, in which dolomite was used as bed material, while the conversion into HCN was the lowest for this test. Dolomite has also increased NH3 and decreased HCN in peat gasification in fluidized bed (96). This seems to be related to the catalytic properties of calcined dolomite in converting char and producing ammonia, as calcium oxide (CaO) is known to enhance the conversion of HCN into NH3 (90)(95). Regarding the filter dust, it increases when using dolomite as bed material, and this can be related to smaller particles being formed during the gasification, due to attrition, and then being elutriated from the bed and collected in the filter, which could explain the higher amount of filter dust-N (36). The conversion into tar-N was the lowest in experiment 6 (0.5 wt-%), while it did not considerably vary in experiments 1 (1.3 wt-%), 2 (1.4 wt-%) and 3 (1.4 wt-%). 95 Chapter 4 This result may be related to the lower tar production in experiment 6 due to dolomite's catalytic properties for tar conversion, even though a reduction of total tars was not observed in experiment 6 (see Figure 4). For instance, two N-tar compounds – benzonitrile and quinoline - showed lower concentration in experiment 6 compared to all other experiments (see Supporting Information). Furthermore, the increase in filter temperature from 450°C (test 3) to 550°C (test 4) did not affect fuel-N conversion into filter dust-N and tar-N. 4.3.4. Conversion of fuel-S Figure 4.6 shows the fuel-S conversion into bottom ash-S, filter dust-S, COS and H2S. Figure 1.1. Conversion of fuel-S (wt-%) into bottom ash-S, filter dust-S, COS and H2S. Gaseous sulphur compounds were not measured in experiments 3 and 4. As expected, H2S is the major sulphur species, and it is much higher than COS (8)(33)(90)(97)(98)(99). 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 air+N2 air+steam air+N2 air+N2 air+N2 air+N2 0.28 0.24 0.32 0.32 0.25 0.30 Conversion of fuel-S, wt-% Fuel-S conversion, wt-% Filter dust-S COS H2S Test Conditions ER ** *T(filter) = 550 °C **dolomite * 96 Chapter 4 The ER increase decreased the conversion into H2S, while steam in the gasifying agent had the opposite effect, resulting in the highest H2S concentration in the syngas (46.4 wt-% of the fuel-S ends up as H2S) since more hydrogen was available for reaction. About 3 wt-% of S in the fuel forms COS. This value did not seem to be affected by the ER. The results show that the formation of COS increased as steam is added to the gasification gas (3.7 wt-% in test 2), while it decreased when dolomite is used as bed material (2.0 wt-% in test 6) (100). No sulphur was found retained in bottom ash in experiments 3 and 4, the only ones that analysed bottom-ash content. This may be related to the fact that fuel-S is generally converted at low temperatures during devolatilization stages (33). The increase in the filter temperature from 450°C to 550°C (test 4) decreased fuel-S conversion into filter dust-S, probably due to the lower capture of components-S in the filter. The fuel-S conversion was not 100%, which is related to the S-containing species that were not measured, the filter ash that was not recovered and due to the sulphur retention in the reactor material and piping (65). Some additional S species may be formed, such as thiophene, and its derivates. However, their concentrations are very low and, thus, they are not included here (99). Extended information about the behaviour of sulphur compounds is available (65). 97 Chapter 4 4.3.5. Conversion of fuel-Cl Figure 4.7 shows the fuel-Cl conversion into bottom ash-Cl, filter dust-Cl and HCl. Figure 4.7. Conversion of fuel-Cl (wt-%) into bottom ash-Cl, filter dust-Cl and HCl. HCl was measured only in experiments 3 and 4. In all experiments, most of fuel-Cl was converted into filter dust-Cl. During gasification, fuel-Cl is mostly released to the gas phase as HCl (some may also react with Na or K) forming NaCl or KCl (7). Afterwards, HCl can be retained in the filter (19)(24). For instance, in experiment 4, where filter temperature is higher (550°C), more chlorine is found as HCl than in test 3 (filter temperature 450°C). Moreover, bottom ash-Cl and filter dust-Cl were highest in test 6 since dolomite retains Cl at solid phase (CaCl2). The fuel-Cl conversion did not reach 100% in any experiment, which can be explained by the loss of filter ash material and mostly because of Cl's retention in the reactor walls and pipelines (65)(4). Extended information about the behaviour of chlorine compounds is available (65). 0 10 20 30 40 50 60 70 80 90 100 123456 air+N2 air+steam air+N2 air+N2 air+N2 air+N2 0.28 0.24 0.32 0.32 0.25 0.30 Conversion of fuel-Cl, wt-% Fuel-Cl conversion, wt-% Bottom ash-Cl Filter dust-Cl HCl Test Conditions ER ** *T(filter) = 550 °C **dolomite * 98 Chapter 4 4.3.6. Conversion of trace elements Table 4.5 contains the filter's composition and bottom ashes collected at the end of the experiments. Filter ash was analysed twice for each experiment (3 and 4), so the associated error is provided. Table 4.5. Filter and bottom ashes composition for experiments 3 and 4. Sample type Filter ash Bottom ash Test run 3 Error (+/-) 4 Error (+/-) 3 4 Ultimate analysis, wt-% (dry basis) C* 6.0 0.3 4.9 0.4 0.1 < 0.1 H* 0.3 0.0 0.2 0.1 < 0.1 < 0.1 N* 0.1 0.0 0.1 0.0 < 0.1 < 0.1 O (calc. as difference) 0.1 0.0 0.8 0.1 0.0 0.0 S** 0.3 0.0 0.2 0.0 <0.02 <0.02 Cl*** 4.9 0.3 4.4 0.4 0.037 0.026 Ash 815 °C 93.3 0.3 93.9 0.4 99.9 100.0 Trace elements concentration Minor elements, mg/kg dry sample + As 11 0 11 0.9 2.3 2.2 Cd 10 0 2.6 0.1 0.14 0.11 Co 70 3 82 8.5 6.8 6.5 Cr 460 0 400 25 150 76 Cu 1100 0 970 130 240 20 Hg <0.02 - <0.02 0 <0.02 <0.02 Mn 640 0 740 50 340 350 Mo 45 0.5 44 4 3.8 2.9 Ni 320 5 270 10 61 21 Pb 580 15 190 10 14 14 Sb 290 20 170 15 33 21 Sn 120 5 64 5.5 11 7.6 Sr 350 5 400 30 200 210 V 44 1 48 1 23 23 Zn 1400 150 1300 195 250 240 Rb 93 2 110 6 63 65 Major elements, g/kg dry sample ++ Ca 142.0 3.0 138.0 4.5 24.3 23.3 Mg 14.4 0.3 13.8 0.5 3.6 3.5 Na 25.1 0.7 24.3 1.0 18.8 18.9 K 24.8 0.3 25.1 0.5 20.2 20.2 P 4.0 0.2 3.9 0.3 0.4 0.4 Fe 20.4 1.0 21.1 0.8 13.2 12.8 Al 95.7 1.7 99.0 6.0 49.0 48.8 Si 118.0 3.5 119.0 2.5 154.0 156.0 Ti 12.1 0.4 13.2 1.0 2.7 2.7 Ba 2.4 0.1 2.4 0.2 0.7 0.7 *SFS-EN ISO 16948, **ASTM D 5016 (mod), ***SFS-EN ISO 10304-1, +SFS-EN ISO 17294-2, ++SFS-EN ISO 11885 99 Chapter 4 The concentrations in trace elements were used to calculate the elements conversion presented in Figure 4.8 and 4.9 below. Figure 4.8 shows the conversions to filter and bottom ashes calculated for experiments 3 and 4. a) b) Figure 4.8. Conversion of elements into filter ash and bottom ash for experiments varying the filter temperature a) Tfilter 450°C (test 3) and b) Tfilter 550°C (test 4). It was expected that the sum of the conversion is ≤100% for all elements. Nevertheless, it is possible to appreciate that for several elements (As, Co, Ni, Cu, Sn, Sr, and V) it greatly surpasses 100%. First, some elements (As and Sn) were not measured in the bed material, and some of them were below the detection limit of 0.01 wt% (V, Co, and Ni). 0% 100% 200% 300% 400% 500% As Cd Co Cr Cu Hg Mn Mo Pb Sb Sn Sr Zn Rb Ca Mg Na K P Fe Al Si Ti Ba Conversion, wt-% Elements 0% 100% 200% 300% 400% 500% As Cd Co Cr Cu Hg Mn Mo Pb Sb Sn Sr Zn Rb Ca Mg Na K P Fe Al Si Ti Ba Conversion, wt-% Element Filter ash Bottom ash 100 Chapter 4 However, if measured with exact techniques, they could increase the conversion even in low quantity. Some elements also surpassed 100% for the conversion sum, but to a lower degree (Mn, Mo, Sn, Sb, and Pb). As the deviation is low, it may be related to the fuel heterogeneity, since it is a waste and, thus, its composition can vary from sample to sample. a) b) Figure 4.9. Comparison of the conversion of trace elements for tests 3 and 4, comparing the conversion into a) the filter ashes and b) bottom ashes. The difference between experiments 3 and 4 lies on the filter temperature (Tfilter(3)=450°C and Tfilter(4)=550°C. Figure 4.9. contains the conversion of selected fuel elements into (a) filter ashes and (b) bottom ashes. In Figure 9a it was expected that the conversion of filter ashes elements would be higher in test 4 than in test 3 due to the lower filter temperature in test 4 (Tfilter(4) = 550°C, Tfilter(3) = 450°C) Therefore, it was expected that the increase in temperature of 100°C in test 4 would lower the elements retention in the filter. Nevertheless, the effect of filter ash temperature was minor, and for most elements (As, Co, Mn, Mo, Sr, V, Zn, Ca, Mg, P, Fe, Al, Ti, and Ba) the filter ash conversion was higher in experiments 4 (excluding only Cd, Pb, Sb and Sn). Experiments with different filter temperature, 0% 30% 60% 90% 120% As Cd Co Cr Cu Hg Mn Mo Pb Sb Sn Sr Zn Rb Ca Mg Na K P Fe Al Si Ti Ba Conversion, wt-% Element 0% 100% 200% 300% 400% As Cd Co Cr Cu Hg Mn Mo Pb Sb Sn Sr Zn Rb Ca Mg Na K P Fe Al Si Ti Ba Conversion, wt-% Element Test 3 Test 4 101 Chapter 4 increasing the temperature intervals are necessary to evaluate this effect on the retention of elements in the hot filter. The release of volatile elements such as As, Cd, Hg, and Pb into filter ash is expected to be higher than bottom ash. This is because they are ultimately released to the reactor's gas phase and subsequently condense in the filter. Hg is exceptionally volatile (low condensation temperature) and was not captured in the hot filter (15). Cd was not found in the bottom ash, and a small fraction was converted to the filter ash. For Cd, the filter-ash conversion was higher in experiment 3 (lower filter temperature) than experiment 4, as expected (101). Pb conversion to filter ash was also higher than bottom ash conversion (102). Moreover, the filter-ash conversion of Pb was higher in experiment 3, as expected. Low-volatile elements, such as: Ca, Mg, Na, K, Fe, Al, Si, Ti and Ba were expected to be more concentrated in the bottom ash, which was observed in both experiments, 3 and 4. The concentration of heavy metals on filter ash may enable using the bottom ash if they agree with the current legislation (103). Moreover, both filter and bottom ashes may serve as sources for valuable metals since they are concentrated in these streams. If this is not yet possible, the ash fraction has a lower volume than the original waste (RDF), reducing material fraction that needs to be landfilled (80). 4.3.7. Enrichment Factor The calculated Enrichment Factor (EF) for the main trace elements in both bottom and filter ashes is presented in Table 4.6. The EF of elements is usually higher for the filter ash than the bottom ash. This is because the trace elements concentration in the bottom ash is diluted in the bed material, in this case, sand. The only exception is Si since it is the primary element present in sand, the bed material used in these experiments. EF for Hg was zero for both filter and bottom ashes, since it is extremely volatile and did not condense at the filter temperature (450°C and 550°C, for tests 3 and 4, respectively). Moreover, the EF for the bottom-ash elements, such as As, Cd and Pb, are low, consistent with their volatile behaviour (104). Co, Cr, Cu and Ni presented higher EF for the filter ashes. These trends are explained due to their 108 Annex I (110). All databases contain the same data for the gaseous compounds containing carbon, hydrogen, and oxygen, but they considerably differ on trace elements and condensing species. HSC is the largest database, containing almost four thousand (4000) compounds, while FACTSAGE and SGTE contain only about two thousand and five hundred compounds (2500). Nevertheless, the databases may differ, for instance, the HSC database may lack some compounds that a smaller database contains. A.I.3. Literature review Inorganic and trace element composition during the thermal conversion of solid fuels has been the subject of several thermodynamic studies in recent years. A sampling of these works is shown in Table AI.1 below. 109 Annex I Table AI.1. Literature review of the inorganics and trace elements during in thermodynamic simulations. Fuel Studied elements Software and database System mode Analysed parameters Comments Year Reference Carbon As HSC Chemistry® version 4.0 Gasification Temperature Considered the composition of a typical gasification syngas, including CO, CO2, H2, H2O, HCl, H2S and N2 2003 Díaz- Somoano (111) Biomass S (H2S, COS, CS2, SO2) and N (NH3, HCN) MATLAB Pyrolysis, gasification and combustion Atmosphere (equivalence ratio) Solid fuel composition in the beginning, changing the equivalence ratio for a fixed pressure and temperature 2008 Baratieri et al. (112) Municipal Solid Waste (MSW) Na, K, Pb, Zn, As, Cr, Cd, Cl and S Factsage 6.0 with a custom-selected database on the Scientific Group Thermodata Europe (SGTE) Combustion Six different waste (one by one), different waste together (two at a time) and temperature Comaparison to experimental results show good agreement, but to fully comprehend the mechanisms at work, additional experimental laboratory and pilot-scale research under wellcontrolled test circumstances are required. 2010 Becidan et al. (113) Solid recovered fuel (SRF) • Inorganics: S (COS, H2S), Cl (KCl, HCl) • Major trace elements: Al, Fe, K, Mg, Mn, Na, P, Si • Minor trace elements: As, Cd, Co, Cr, Cu, Hg, Ni, Pb, Ti, Tl, V, Zn Software not specified. FACT database (Fact Sage 5.2.) was used for basic data, complemented with data from HSC Chemistry version 5.11; GFE database; SGTE pure substance database; and the Ivtanthermo for Windows for compounds not available in FACT database. Fluidized bed gasification and product gas cooling Temperature and different case scenarios considering different inputs Focus of research on the behaviour of the following elements: As, Cd, Cr, Cu, Co, Hg, Mn, Ni, Pb, Sb, V and Zn 2012 Konttinen et al. (20) Municipal Solid Waste (MSW) Cu, Pb, Zn Factage 7.2 software. FactPS, FToxid, FTsalt and HSCA databases. Combustion The authors also studied samples of ashes (20 bottom ashes and 17 fly ashes) using XRD, SEM-EDX and XPS techniques. Oxygen Carrier Aided Combustion 2022 Stanicic et al. (114) References: Konttinen et al. (20); Díaz-Somoano (111); Baratieri et al. (112); Stanicic et al. (114); Becidan et al. (113) 110 Annex I AI.4. Methodology AI.4.1. Software HSC Chemistry In this study, the database from the HSC Chemistry® version 6.1 was selected to conduct the studies of thermochemical conversion of biomass. The HSC Chemistry® software allows the simulation of thermodynamic equilibriums of reactions and processes on a computer and can carry out conventional calculations quickly and easily. To do this, it uses a database in which enthalpy (H), entropy (S) and heat capacity (C) values are collected. The program calculates the equilibrium composition of multicomponent mixtures in heterogeneous systems. As an input, the user needs to specify the chemical elements that make up the system (e.g.: C, H, O) and then determine the compounds that are in the starting compounds (e.g.: CO, H2O...) and their quantities in mass or in moles. At this stage, it is also necessary to determine the initial temperature of these components, their physical state (solid, liquid, or gaseous) and in which phase of the system they will belong. The software has predetermined the phases of the components, for example: sulphates, oxides, chlorides, gas, etc., and a component has the phase to which it is associated by default. As an example, there is the case of CO (gas), which is in the gas phase, while C (solid) is in the solid phase. However, the number of phases can be changed by the user and a simulation can be carried out with just one phase, even if the components of the phase belonged to a different phase at the beginning of the simulation. The user must specify the potentially stable substances and phases that the software must consider during the equilibrium calculation. It is necessary to consider that the change of the species and of the phases considered in the equilibrium leads to a change in the results of the composition in the equilibrium. This point is very important during the simulations, since the software offers for the calculation all the possible species found in its database and, therefore, it is up to the user to decide which components should or should not participate in the equilibrium calculation. 111 Annex I In addition, the software calculates equilibrium quantities under isothermal and isobaric conditions. The equilibrium composition is calculated using the GIBBs or SOLGASMIX solvers that use the Gibbs energy minimization method. AI.4.2. Methodology used in literature Konttinen et al. (20) conducted studies related to biomass gasification and recycled fuels for efficient power and heat production using the FACTSAGE software. The objective of his work was to develop and test thermodynamic models to analyse the behaviour of trace elements in the product gas. Due to the number of elements that are present in fuels, the proposed method consisted in carrying out several simulation cases with FACTSAGE, progressively adding specific elements and analyzing the results. The steps performed are described as follows: ▪ Case 1: The main elements of the fuel and the trace elements are added, excluding the components of the ash (Ca, Mg, K, Na, Si, Al, Fe, P), in addition to the air fed into the gasifier. ▪ Case 2: The same elements are introduced as in first case, and also Na, K, Mg and P. Ca, Al and Si are excluded, because they were found to be of great importance for the calculation of chemical equilibrium. When they were introduced, the results presented solid compounds that are not kinetically realistic to be formed during the gasification process. ▪ Case 3: In this case, all the components presented in the ash and the bed material were included in the simulation. The database used in the calculations had the following characteristics: ▪ Number of elements: 35 ▪ Number of phases: 4 (1 gas + 3 liquids) ▪ Number of gaseous species: 200 ▪ Number of solid species: 540 Kuramochi et al. (115) investigated the behaviour of HCl and H2S released during the gasification of different fuels, analysing the influence of the composition of the feed due to the influence of some metals on the composition of the species mentioned (HCl, H2S). The study of the behaviour of these 112 Annex I gases was predicted using the calculation of chemical equilibrium in a temperature range between 673 K and 1473 K. The calculations were made with FACTSAGE using the Gibbs energy minimization method. However, the software presented a limiting number of the potential elements to be formed. Thus, a specific procedure was followed: ▪ Step 1: The elements were selected in order of greatest content and their elemental compositions were assigned until reaching the software limit. ▪ Step 2: The equilibrium calculations were performed and only species whose concentration were higher than 10-12 moles were considered. ▪ Step 3: Next, step 1 is carried out again, substituting the element with the lowest content that was considered in the initial calculation. ▪ Step 4: Follow again all the steps until the equilibrium calculations have been made for all the elements. Figure A.1 below illustrates the scheme of the steps to follow in the proposed model: Figure A.1. Diagram of the calculation procedure followed by Kuramochi et al. 113 Annex I Regardless of the database used, although there are differences in the calculations, both methods presented by Konttinen et al. (116) and Kuramochi et al. (115) can be implemented. The first method, proposed by Konttinen et al. (116) does not include Ca, Al, and Si in the simulations. This because when they are included, the results obtained are not kinetically possible in a real gasification experiment. This might be linked to the fact that including the bed material composition in the simulations, usually sand -which is very stable and does not react with the fuel and is in higher amount than the fuel – presents a high content of those elements, which would interfere in the simulation results. However, in this work, the author opted for including the bed material composition in the simulation, excluding only Ca, Al, and Si. The second method, proposed by Kuramochi et al. (115), follows a more structured sequence. As a drawback, it can be highlighted that the calculations are performed without involving different minor elements simultaneously, since the ones with less concentration are excluded until the limiting number of potential compounds is reached, which is defined by the program. So, for instance, if two minor elements might react, and one of them is not included because of the software limitation, the study would not consider this potential compound. Despite the weaknesses of the methods, satisfactory results can be obtained to understand the distribution of pollutants during gasification, which will be presented in the next section. AI.5. Results and Discussion AI.5.1. Thermodynamic analysis of the thermochemical conversion of biomass In this section, a thermodynamic analysis of thermochemical conversion of biomass was performed. Baratieri et al. (112) proposed an equilibrium model (gas-solid) based on the minimization of the Gibbs free energy, to estimate the theoretical yield and the composition of the reaction products (gas of synthesis – syngas - and char) from the thermochemical conversion of biomass. In their work, the calculation of the chemical equilibrium was carried out the MATLAB (117), which determined the equilibrium composition by minimizing the Gibbs energy of an ideal mixture. The proposed algorithm calculated the composition for a specific temperature and pressure. With the aim of comparing studying the thermochemical conversion of biomass and exploring similarities and differences with 114 Annex I the work conducted by Baratieri, simulations were carried out using the HSC Chemistry® 6.1 software using the same fuel composition and conditions from their work. AI.5.1.1. Fuel composition One hundred kilograms (100kg) of fuel was used as a calculation basis. No bed material was considered in this simulation, as well as the composition of trace elements to limit the number of components and simplify the study. Tables A.2 shows the composition of the biomass used and the operating conditions. Table AI.2. Fuel composition Element Content (wt.%) C 57.2 H 3.3 O 16.2 N 0.7 S 0.2 Moisture 9.0 Ash 13.4 AI.5.1.2. Operating conditions Table AI.3 below present the conditions considered for the study of the thermochemical conversion of biomass. Table AI.3. Conditions considered in the study of the thermochemical conversion of biomass Temperature (K) 1100 Pressure (kPa) 155 ER 0 - 1 The study was performed containing different amounts of oxygen, varying from zero (ER = 0) up to the amount necessary for complete combustion (ER = 1). AI.5.1.3. Compounds considered in the equilibrium Table AI.4 shows the species considered in the simulation conducted in the HSC Chemistry software, as well as the components considered by Baratieri et al. (112). 115 Annex I Table A.4. Compounds considered at the equilibrium for the study of the thermochemical conversion of biomass. The first column A) shows the species considered by Baratieri et al., while the second column B) shows the species included in this work. Compounds A) Baratieri et al. B) HSC Chemistry C C(g), CO, CO2 C(g), CO, CO2 H H, H2, O, O2, OH, H2O, HO2, H2O2, HCO H, H2, O, O2, OH, H2O, HO2¸H2O2, HCO N N, N2, NH, NH2, NH3, NO, NO2, N2O, HNO, CN H2CN, HCNN, HCNO HOCN¸HNCO, NCO N, N2, NH, NH2, NH3, NO, NO2, N2O, HNO, CN H2CN, HOCN, HNCO, NCO S S, SO2, SO3, H2S, COS, CS2 S, SO2, SO3, H2S, COS, CS2 Hydrocarbons CH, CH2, CH3, CH4, C2H, C2H2, C2H3, C2H4, C2H5 C2H6, C3H7, C3H8, C6H6, C10H8, C12H10 CH, CH2, CH3, CH4, C2H, C2H2, C2H3, C2H4, C2H5 C2H6, C3H7, C3H8, C6H6, C10H8, C12H10 Other organics CH2O, CH2OH, CH3O, CH3OH HCCO, CH2CO, HCCOH, CH2CHO, CH3CHO CH2O, CH2OH, CH3O, CH3OH Solid Carbon C(s) C(s) Most of the compounds considered by Baratieri et al. are included in the HSC simulation, with exception of some compounds in the sections of “N” and “other organics”, highlighted in Table A.4. A5.1.4. Results and comparison The focus was on the evaluation of the gaseous species formed that contain S and N, since they are considered as pollutants. Their content on the final syngas may affect its use and determine the downstream cleaning processes that the syngas must go through. Figure A.2 below shows the comparison between the results obtained in this study and the ones obtained by Baratieri et al. (112). 116 Annex I A) B) Figure A.2. Comparison of the results obtained A) by Baratieri et al. and B) in this work. The simulation was conducted in the temperature and pressure indicated previously in Table A.3, with the evolution of the Equivalence Ratio (ER). The results obtained in this study (Figure 5.2 B) will be discussed below. As expected, in a reducing environment, S is decomposed mainly into H2S. COS is also one of the main S-products, even though its concentration is much lower than H2S (more than ten times). When the ER increases close to one (1), which shows an oxidizing environment, sulphur oxide is found (SO2 in this work and SO2 and SO3 at Baratieri et al.’s work). Additionally, a very low amount of CS2 appears in the results for ERs up to 0.6. In the case of the N-compounds, at a reducing environment, NH3 and HCN are the main products, being the concentration of NH3 approximately ten times higher than the concentration of HCN. Each concentration increases from ER zero to 0.1 at a similar rate. Then the concentration of NH3 slowly decreases from ER 0.1 to 0.9, to decrease at a high rate from 0.9 to 1. The concentration of HCN is the same from ER 0.1 to 0.3 and then decreases from ER 0.3 to 0.9. Both results are very similar among them. Differences might be related to the compounds considered in the equilibrium, which were shown previously in Table A.3 117 Annex I AI.5.2. Thermodynamic analysis of the distribution of Arsenic during gasification using carbon as fuel In this section, the study of the distribution on heavy metals was studied during the gasification of carbon, with focus on the behaviour of Arsenic with the temperature. Díaz et al. (111) studied the behaviour of trace elements during the gasification of Carbon, using the software HSC Chemistry® version 4.0 - the same software used in this work, but in an older version. The analysis was made by calculating the equilibrium composition for each trace element separately, considering the major gaseous species (CO, CO2, N2, H2, H2S, HCl). AI.5.2.1. Composition considered in the equilibrium Tables AI.5 shows the composition used by Díaz et al. (111) as gasification atmosphere. Table A.5. Composition of the gaseous mix, simulating a gasification atmosphere, used by Díaz et al. Component Content (kmol/l) CO 2,57 x 10-5 CO 2 1,47 x 10-6 H 2 8,39 x 10-6 H 2 O 1,79 x 10-6 HCl 4,02 x 10-7 H2S 2,01 x 10-8 N2 6,88 x 10-6 Differently from the work developed in the previous Section A.3.1, instead of using as an input for the simulation the fuel composition, in this case, the gasification atmosphere for the major gases was used. AI.5.2.2. Operating conditions The behaviour of Arsenic was studied for a temperature range of 200 - 1200 °C and at 25atm. AI.5.2.3. Compounds considered in the equilibrium Arsenic was considered in the form of FeAsS as an input for the equilibrium calculations, both by Díaz et al. and in this work. The compound in which Arsenic will be found in the fuel depends on the 124 Annex II Figure AII.1. Schematic diagram of the case of study of the gasification of Case 0: landfilling of MSW and Case 1: gasification of RDF pellets in fluidized bed to produce electricity. AII.2.3. Environmental Life Cycle Assessment (E-LCA) LCA is defined as a framework used to analyse the environmental impacts of a product, process, or system throughout its life (131). It is comprised by four steps: goal and scope definition, inventory analysis, impact assessment and interpretation, which are described below. AII.2.3.1. Goal and scope The goal and scope of this study is to determine the resources consumption and environmental impact of RDF gasification in fluidized bed and compare it to the landfilling of MSW (common practice in Spain). The functional unit was defined as 1000 kg (1t) of RDF (See composition at Table 4.1). The environmental assessment presented in this study was performed using a cradle-to-grave LCA approach. The system boundary contains building materials production, the transportation of the RDF 125 Annex II to the gasification facilities, ashes transportation, consumed electricity and fuels, chemicals production, electricity, and emissions. Figure AII.1 contains the major unit process of LCA for this case of study. AII.2.3.2. Life Cycle Inventory Analysis The LCI plays a key role in an LCA as the basis for the subsequent LCIA, sensitivity analysis, and economic analysis. LCIs associated with the treatment of 1000 kg of RDF for both cases – landfilling and fluidized bed gasification were compiled. Data was obtained from own dedicated experimental tests (fluidized bed gasification tests) and literature, as described in the previous sections. The Ecoinvent Database was also used for the LCI (132). An avoided burden approach was used in which displaced products provided a corresponding environmental benefit and allocation methods were avoided. The following assumptions were made in the Life-Cycle Inventory Analysis: Transportation: ▪ The distance from the RDF to the thermochemical facility was supposed to be 50 km, while the distance from ashes to landfilling and chemicals disposal were assumed as 50 km. Distances were limited to avoid transportation emissions. ▪ The flying and bottom ashes were assumed to be stored and then transported together to a landfill. ▪ The diesel consumption of the trucks was considered to be 0.4 L/km. ▪ The total truck capacity was considered to be 23.5 tons. ▪ The direct emission of diesel into CO2 was estimated as 2.68 kg of CO2 equivalent / L of consumed diesel. ▪ Diesel was considered to be a fraction of 30% of crude. ▪ Crude density was estimated at 0.85 kg/L. Other assumptions were made for the Life Cycle Inventory Analysis: 126 Annex II ▪ All the above-mentioned transportations were assumed to be done by trucks using diesel as the fuel. The return distance of empty trunks was also accounted. ▪ The electricity used in the processes is assumed to be produced from the mix of electricity in Spain. In 2021, the average emissions of Spain were estimated to be 170 g of CO2 equivalent / kWh. System expansion* was used to account for the electricity produced from gasification. ▪ 1 ton of MSW is equivalent to 1 ton of RDF. The volume reduction of MSW to RDF was of seventy percent (70%). ▪ 100 m2 was considered as the land occupied in both cases. Natural land occupation was not considered, just agricultural and urban land occupation, by supposing that a land that was already previously occupied was used in both cases. ▪ The content of fuel characterized as fixed carbon was considered to not be released to the atmosphere in the landfilling scenario (Case 0). The remaining carbon was considered to be released as CH4 (61 %v) and CO2 (39 %v), following data found in literature (133). ▪ 0.006 kg of N / kg of N-fuel was considered to be released as N2O in the landfilling case, also using literature data (134). ▪ The consumption of energy (electricity) for the pelletizing process of MSW was considered to be 185 kWh / ton of MSW, following literature data (135). ▪ Using data from Chapter 4 of content of Hg and Cd in the ashes, it was considered that 90% of the Cd and all Hg went to the air phase, so it was considered for the elemental mass balances here. ▪ The remaining of Cd (10%) and the rest of minor elements were considered to be fully concentrated in the ashes and then to be split equally between soil and water phases after disposal. Then associated errors were calculated, by supposing that the elements would be placed totally in the soil or in the water, since leachability data of these elements in the fuel and ashes are not available. This is further detailed and discussed in the results section. ▪ Benzene was considered as the chemical representative of all tars produced in Case 1. ▪ The amount of bed material was estimated per the usual proportion of bed material and fuel and the residence time of the bed material in the reactor. * Note: In system expansion, co-products are considered alternatives to other products on the market. Thus, in this case, the electricity produced avoided the impact of the electricity produced in Spain (The Spanish energy mix in 2021 was considered to do so). 127 Annex II The energy, materials, and emissions inventory to deal with 1 ton of RDF for gasification is presented in Table AII.1 below: Table AII.1. Energy, materials, and emissions inventory to process 1 ton of MSW / RDF as received. LIFE CYCLE INVENTORY Item Unit Case 0 Case 3 Landfilling FB Gasification Energy consumed Electricity (total) kWh 0 -1914.8 Diesel L 4.9 8.8 Raw Materials - Fuel MSW / RDF (ar) kg 1000 1000 Bed material, sand kg - 2000 Raw Materials - Building Materials Concrete kg 0 3.17 Steel kg 0 1.01 Aluminium kg 0 0.01 Iron kg 0 0.01 Gravel kg 0 113.88 Direct Gas Emission CO2 fossil kg 13 23.5 CO2 biogenic kg 678.9 1512.3 CO2 total Kg 691.9 1535.8 CH4 kg 388.1 - SOX kg - 4.10E-01 NOX kg - 1.11E+01 NH3 kg - - N2O kg 9.00E-02 - Cd kg - 3.00E-03 Hg kg - 2.00E-03 Direct Solid Emission (to soil and / or water) As kg 9.59E-04 9.59E-04 Ba kg 1.10E+00 1.10E+00 Cd kg 3.26E-03 3.26E-04 Cl kg 6.71E+00 6.71E+00 Co kg 6.81E-03 6.81E-03 Cr kg 5.61E-02 5.61E-02 Cu kg 4.12E-02 4.12E-02 Hg kg 2.11E-03 0.00E+00 Mn kg 4.51E-02 4.51E-02 Mo kg 4.12E-03 4.12E-03 Ni kg 6.04E-03 6.04E-03 P kg 7.82E+00 7.82E+00 Pb kg 3.36E-02 3.36E-02 Sb kg 3.02E-02 3.02E-02 V kg 2.78E-03 2.78E-03 Zn kg 2.06E-01 2.06E-01 Benzene kg 0 2.14E-02 Solid Products Flying ash kg 0 3.7 Bottom ash kg 0 140 Energy produced Syngas kWh 0 3500 128 Annex II AII.2.3.3. Life Cycle Impact Assessment The ReCiPe methodology was used to conduct the Life-Cycle Impact Assessment, using the hierarchic point of view. In this methodology, four damage (endpoint) categories are considered: ecosystem quality, human health, resources conservation and climate change. Seventeen impact (midpoint) categories are considered, for instance: agricultural land occupation (ALO), climate change (ECC), freshwater ecotoxicity (FET), freshwater eutrophication (FE), marine ecotoxicity (MET), natural land transformation (NLT), terrestrial acidification (TA), terrestrial ecotoxicity (TET), urban land occupation (ULO), human health climate change (HCC), human toxicity (HT), ionizing radiation (OR), ozone depletion (OD), particulate matter formation (PMF), photochemical oxidant formation (POF), fossil depletion (FD) and metal depletion (MD). The last two categories, FD and MD are related to resources, while the first nine ones are related to ecosystem quality. The remaining midpoints belong to human health (136)(137). AII.2.3.4. Results interpretation From the LCA, a comparison between the impacts of all cases is performed. Interpretation of LCA results gives insights for pros and cons of each alternative considered in this study. It also evidences which aspects from the process mostly contribute to the life cycle environmental impacts. AII.2.4. Techno-Economic Assessment The techno-economic analysis aims to determine the economic performance of the gasification process. For that, capital cost, operational costs, fuels and electricity costs and revenues from electricity was considered. An avoidance tip for CO2 was included and further analysed in sensitivity analysis. For the simplification of the calculations, the following assumptions were made: ▪ Electricity prices were based on the Spanish market. For electric power delivered to the grid, it was set initially as € 0.06 kWh -1. 129 Annex II ▪ CO2 avoidance/emission cost was assumed as € 16 / ton of avoided CO2 as a baseline. (138) ▪ The gate fee for MSW was assumed to be zero as a baseline. ▪ The Capital Costs (CC) of thermochemical plants were taken from literature and are summarized at Table AII.2 below. Capital Costs were updated to the plant throughput of 5 MWe. Operational Costs (COC) were also taken from literature and are detailed at Table AII.2. (130) ▪ At the end life of the plant, there are expenses in the plant decommissioning, but the selling of some parts of the plant may generate incomes. Thus, the decommissioning costs in this work was assumed to be zero. The following Table AII.2 shows the reference used for the calculation of capital costs (Cc) and operational costs (Coc) for a gasification plant of 35MWe of capacity. Table AII.2. Reference for Capital Costs (CC) and O&M costs of the gasification plant for a plant of 35MWe of capacity (130). Reference Facility Fuel (dry) Year Correspondence Currency Capital Costs O&M Aracil et al., 2018 (122) Fluidized Bed Gasification (with ICE*) MSW 2015 Euro/ton Euro 332 58 * ICE: Internal Combustion Engine The Capital Costs were updated the plant capacity considered in this work of 5 MWe using the following Equation 6.1: 𝐶2=𝐶1(𝑆2 𝑆1)𝑛 (Eq. AII.1) Where C2 is the Capital Cost (CC) of the plant with capacity S2 and C1 is the CC of the plant with capacity of S1. The exponent n is taken as 0.6 due to the chemical nature of this process (139). The total associated value (π) for the gasification technology proposed in this study, in €/ton of MSW processed and is calculated using the following equation: π = E + G + CO2 – Transport – Cc - Coc (Eq. AII.2) 130 Annex II Where π is the value (positive or negative) associated with the processing of 1 ton of MSW (€ / ton of MSW). E is the value of the energy (i.e., electricity) created in the process, G is the gate fee obtained from any tipping or disposal fees received for the RDF; CO2 is the revenue of the avoided CO2. Transport is the transportation cost for both the feedstock and the products (i.e., ashes). AII.3 Results and discussions AII.3.1 Comparative LCA results Figure AII.2 below shows the calculated midpoints for the two cases studied here in total points, considering the 1000kg of RDF treated in each case. Figure AII.2. Midpoints for Cases 0 and 1 studied. From Figure AII.2, it is possible to observe that the impact of landfilling is higher than the impact of FB gasification for most midpoints. The highest midpoint in all categories is human toxicity, which is linked to the impact of selected elements that are part of the RDF. Table AII.2 below shows the LCIA end points for the different cases and the different end categories in points per kg of RDF. 0.00E+00 2.00E+02 4.00E+02 6.00E+02 8.00E+02 1.00E+03 1.20E+03 Case 0: Landfilling Case 1: FB gasification 131 Annex II Table AII.2. LCIA end points and total points for the different cases studied Case Ecosystem quality (pts / kg) Human health (pts / kg) Resources conservation (pts / kg) Climate change (pts / kg) Total (pts / kg) 0: Landfilling 1.84E-01 1.32E+00 1.80E-03 4.72E-01 1.97E+00 1: FB gasification 3.06E-02 1.10E+00 3.24E-03 5.48E-02 6.83E-01 Figure AII.3 shows the percentage in the total impact of each case studied, for the four end categories: ecosystem quality, human health, resources conservation and climate change. Figure AII.3. Percentage in the total impact of each case for the four end categories: ecosystem quality, human health, resources conservation and climate change. From Table AII.2 and Figure AII.3 it is possible to observe that human health is the highest category, among the endpoints, followed by climate change, ecosystem quality and resources conservation, for both cases studied. The impact of the human health endpoint is mostly linked to the content of certain elements present in the RDF due to the midpoint category “human toxicity”, accounting for the following elements: As, Ba, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, Sb, V and Zn. For both cases studied, it was considered that those elements were in the soil. Only Cd and Hg for Case 1 (FB gasification) were considered to be partially emitted to the gas phase, since they were not 9% 67% 0% 24% Case 0: Landfilling 3% 92% 0% 5% Case 1: FB gasification ECOSYSTEM QUALITY HUMAN HEALTH RESOURCES CONSERVATION CLIMATE CHANGE 132 Annex II fully recovered in the bottom and flying ashes from the mass balances from Chapter 4. Nevertheless, sensitivity analyses to investigate the partitioning of these elements to the soil and water were performed, since data from the leachability of these elements both in RDF and in the ashes are not available. This will be discussed below, from Figure 6.4 analysis. Figure AII.4. Comparison between the total impact in points of Case 0: landfilling (orange) and Case 1: FB gasification (blue) in the endpoint categories: ecosystem quality, human health, resources conservation and climate change. The error bar in the human health category is associated with the uncertainty of the fate of the abovementioned metals after their disposal. If they are considered to go totally to the water, they cause a lower impact (lower points of the error bars), and if they are considered to go entirely to the soil, they cause a higher impact (higher points of the error bars). The values shown in the bars were calculated considering that 50% of an element went to the soil, while the other 50% went to the water. Moreover, it is important to note that for both cases, and for all elements included, the “unspecified” categories for both soil and water were selected to calculate the midpoints, since the types of soil and water (i.e., agricultural, urban, etc.) were not defined in this study. The fate of an element after the disposal of MSW (Case 0) or ashes (Case 1) is linked to the leachability of the element in water. After the thermochemical conversion the leachability of an element i may change and, for instance, if it increases, it may cause a lower impact, since the associated impact for water is lower than for the soil. Nevertheless, as this was not measured in this study, the sensibility 0.0 200.0 400.0 600.0 800.0 1000.0 1200.0 1400.0 ECOSYSTEM QUALITY HUMAN HEALTH RESOURCES CONSERVATION CLIMATE CHANGE 133 Annex II analysis presented showed the range of impact an element can cause, depending on if they will be maintained in the soil or will be released to the water. If somehow it would be possible to stabilize the ashes so they do not enter in contact with the soil or water, the human health impact would severely decrease. This can be done more easily than stabilizing the MSW, since the amount of ashes is much lower, about 15% of the mass of RDF. Moreover, currently, the lack of metals can bring the idea of mining landfills to recover elements. This could be specially interesting for the ashes, since elements get concentrated there. If the process to recover those metals from the wastes were with few steps and the chemical form in which the elements were found - they may change after thermochemical conversion – were useful to society, they could cause a positive impact instead by replacing conventional mining processes. Nevertheless, these thoughts are given just as potential new solutions and ways of decreasing the environmental impact of solid wastes, since they will not be studied here in depth. Figure AII.5. Comparison between the impact of Case 0: landfilling (orange) and Case 1: FB gasification (blue) in the endpoint categories: ecosystem quality, human health, resources conservation and climate change. In this chart, the impact of landfilling represents 100% for each category and FB gasification is compared to that, also in percentage. From Figure AII.5 it is possible to observe that the impact of landfilling is higher for almost all categories. FB gasification only surpass landfilling in the resources conservation category. Nevertheless, from Figures AII.3 and AII.4, it is noted that resources conservation is the least impactful category for both cases, less than one percent (1%) of the total impact. 100% 100% 100% 100% 17% 83% 180% 12% ECOSYSTEM QUALITY HUMAN HEALTH RESOURCES CONSERVATION CLIMATE CHANGE Case 0: Landfilling Case 1: FB gasification