Stationary simulation of an alkaline electrochemical plant in DWSIM

Equation Chapter 1 Section 1 Master's Thesis Chemical Engineering Stationary simulation of an alkaline electrochemical plant in DWSIM Author: Alejandra Márquez Marvizón Supervisor: Pedro García Haro Department of Chemical and Environmental Engineering University of Seville Sevilla, 2024 iii Stationary simulation of an alkaline electrochemical plant in DWSIM iii Master's Thesis Chemical Engineering Stationary simulation of an alkaline electrochemical plant in DWSIM Author: Alejandra Márquez Marvizón Supervisor: Pedro García Haro Associate Professor Department of Chemical and Environmental Engineering Higher Technical School of Engineering University of Seville Sevilla, 2024 v Stationary simulation of an alkaline electrochemical plant in DWSIM v Master's Thesis: Stationary simulation of an alkaline electrochemical plant in DWSIM Author: Alejandra Márquez Marvizón Supervisor: Pedro García Haro The committee appointed to evaluate the above-mentioned project, composed of the following members: Chairperson: Members: Secretary: Hereby agrees to award the grade of: Sevilla, 2024 The Secretary of the Committee To my family To my teachers vii Stationary simulation of an alkaline electrochemical plant in DWSIM vii Acknowledgments I would like to express my deepest gratitude to all those who contributed to the completion of this thesis. First, I would like to thank my thesis supervisor, Pedro García Haro, for his guidance on this project, but above all, for his teachings and professionalism throughout my university career, and especially during the year he was my mentor in my research collaboration with the department. It was undoubtedly an enriching experience that introduced me to the world of renewable hydrogen, which has now become the focus of my professional career. Secondly, I would like to thank all my colleagues at Lean Hydrogen, especially Eugenio Trillo and Marta Romero, for giving me my first professional opportunity in this exciting field. Thank you for believing in me, for everything you have taught me over this past year—both technically and personally—and for sharing your enthusiasm and motivation for engineering and hydrogen. Thirdly, I want to thank my family, who have always supported me unconditionally and taught me to pursue all my goals and ambitions. Thank you, Mom, Dad, Ana, and my little sister, Rocío. I also want to thank Manuel, now as part of my family, for being by my side since the beginning of our university journey and up until now. Finally, I would like to thank my fellow classmates, who have been a fundamental support during these seven years of university. They know better than anyone the hard work and dedication it has taken to reach this point. ix Stationary simulation of an alkaline electrochemical plant in DWSIM ix FIGURES Figure 1-1: Hydrogen use by sector and by region, historical and in the Net Zero (3). 24 Figure 1-2: Hydrogen production by technology, 2020-2022 (3). 24 Figure 1-3: Low-emission hydrogen production by technology route, maturity and region based on announced projects and in the NZE, 2030. (3) 25 Figure 1-4: Map of announced low-emission hydrogen production projects (3). 26 Figure 1-5: Evolution of electrolyzer installed capital costs based on deployment from announced projects and in the Net Zero Emissions by 2050 Scenario, 2021- 2030, and the share of capital cost in the levelized cost of hydrogen production. (3) 27 Figure 1-6: Levelized cost of hydrogen production by technology in 2021, 2022 and in the Net Zero Emissions by 2050 Scenario in 2030. (3) 28 Figure 1-7: Technology readiness levels (US scale) of production of low-emission hydrogen and synthetic fuels, and infrastructure. (3) 29 Figure 1-8: Technology readiness levels (US scale) of hydrogen end use by sector. (3) 30 Figure 1-9: Spanish goals for 2030 based on the Hydrogen Roadmap.(4) 31 Figure 1-10: Map of projects registered in the Project Census classified by activity (5). 32 Figure 1-11: Cumulative public funding per year and per entity. (5) 32 Figure 1-12: Primary energy sources, feed stocks and routes for hydrogen production. (6) 33 Figure 1-13: Main colours of hydrogen in function of its source and emissions (original creation). 34 Figure 1-14: Electrolyzer stack, Balance of Plant (BoP) and power source. (8) 35 Figure 1-15: Thermodynamics of water electrolysis (Adapted from (9)): (left) influence of temperature on the thermodynamic parameters of water electrolysis at 1 bar; (right) dependence of reversible voltage and thermoneutral voltage on temperature at 1 bar. 36 Figure 1-16: Polarization curve of an electrolytic cell and overpotentials. (Adapted from (10)) 38 Figure 1-17: Faraday efficiency according to the current density at different temperatures.(12) 39 Figure 1-18: Effects of operating pressure in the polarization curve of electrolyzers. (8) 40 Figure 1-19: Alkaline electrolyzer scheme. (7) 41 Figure 1-20: Cross section of a bipolar, atmospheric pressure electrolysis unit with alkaline electrolyte (Brown Boveri) a) First anode with pre-electrode; b) Diaphragm; c) Preelectrode of the cathode; d) Cathode for the first cell compartment and anode for the second cell compartment-bipolar middle electrode; e) Cell frame with insulation for the middle electrode; f) Distribution pipe for electrolyte; g) Inlet pipe for electrolyte; h) Take-off system for electrolyte and dissolved gases; i) Overflow pipe and collecting vessel for electrolyte and hydrogen (an analogous system is used for O2); j) Electrolyte filter; k) Circulation pump; l) End plate. (15) 42 Figure 1-21: Alkaline electrolysis stacks and BoP (16). 43 Figure 1-22: Water Treatment Plant block scheme (Original Creation). 43 Figure 1-23: Conductivity of NaOH and KOH solutions (9). 44 Figure 1-24: Hydrogen Purification System (Original Creation). 44 Figure 1-25: PEM electrolyzer scheme (7). 45 Figure 1-26: PEM electrolysis stack and BOP (18). 46 xvii Stationary simulation of an alkaline electrochemical plant in DWSIM xvii Figure 1-27: SOEC electrolyzer scheme (7). 46 Figure 1-28: Anion Exchange Membrane electrolysis scheme (14). 47 Figure 2-1: Electrolysis Stack Model Scheme. (Adapted from (9)) 49 Figure 2-2: Reversible Voltage vs Temperature. 53 Figure 2-3: Cell’s Voltage dependence on current density and temperature. 53 Figure 2-4: Faraday Efficiency dependence on current density and temperature. 54 Figure 2-5: HTO dependence on current density and temperature for P=1,013 bar. 54 Figure 2-6: HTO dependence on pressure for a temperature of 85 °C. 55 Figure 2-7: Effect of the number of cells on the production of hydrogen. 59 Figure 2-8: Effect of the number of cells on efficiencies. 60 Figure 2-9: Effect of number of cells in inlet water to stack. 61 Figure 2-10: Effect of number of cells in generated heat. 61 Figure 2-11: Effect of inlet temperature in the hydrogen production. 62 Figure 2-12: Effect of inlet temperature in efficiencies (%). 63 Figure 2-13: Effect of inlet temperature on inlet water to stack. 63 Figure 2-14: Effect of inlet temperature on generated heat. 64 Figure 2-15: Effect of active area on hydrogen production. 65 Figure 2-16: Effect of active area on efficiencies. 65 Figure 2-17: Effect of active area in inlet water to stack. 66 Figure 2-18: Effect of active area on the generated heat. 66 Figure 2-23: Effect of the outlet temperature in the hydrogen production. 67 Figure 2-24: Effect of the outlet temperature in efficiencies. 67 Figure 2-25: Effect of outlet temperature in inlet water to stack. 68 Figure 2-26: Percentual change in the KPIs when the main parameters vary a -30% (low case) and a +30% (high case) from the base case. 69 Figure 2-27: Optimization of active area to maximize global efficiency. 70 Figure 2-28: Optimization of number of cells to maximize global efficiency. 70 Figure 2-29: Variation of inlet parameters with the optimization, in relation with the base case. 71 Figure 2-30: Variation of KPIs with the optimization, in relation with the base case. 71 Figure 2-31: Electrolysis BoP Simulation Process Diagram in DWSIM. 72 Figure 2-32: SEP-O2 material balance with the base case stack. 73 Figure 2-33: SEP-H2 material balance with the base case stack. 74 Figure 2-34: TRAP_H2 material balance with the base case stack. 74 Figure 2-35: TRAP_O2 material balance with the base case stack. 74 Figure 2-36: Pumps a) PUMP-1: Inlet water pump b) PUMP-2: Oxygen-side recirculated water pump c) PUMP- 3: Hydrogen-side recirculated water pump. 75 Figure 2-37: PUMP-1 parameters configuration window. 75 Figure 2-38: Specification block configuration of PUMP-2. 76 Figure 2-39: Coolers. a) COOLER-1: Oxygen side cooler. b) COOLER-2: Hydrogen-side cooler. 76 Figure 2-40: Parameters configuration window of COOLER-1. 77 Figure 2-41: Specification block configuration of COOLER-1. 77 Figure 2-42: MIX material balance with the base case stack. 77 Figure 2-43: Parameters configuration window of Recycle Blocks. 78 Figure 2-44: Input Box Blocks 78 Figure 2-45: Consumed water in kg/h and % for the base and optimized cases. 79 Figure 2-46: BoP electrical energy consumption in the base case. 79 Figure 2-47: BoP electrical energy consumption in the optimized case. 80 xix Stationary simulation of an alkaline electrochemical plant in DWSIM xix Notation AEH2 Spanish Hydrogen Association AEM Anion exchange membrane ALK Alkaline BoP Balance of plant CAPEX Capital expenditure CCUS Carbon capture, utilisation and storage CDTI Centro para el Desarrollo Tecnológico y la Innovación CF Capacity factor COP Coefficienct of Performance DI water Deionized water DVB Divinylbenzene EU European Union FID Final investment decisions GHR Global Hydrogen Review HRS Hydrogen Refueling Station HTO Hydrogen to Oxigen diffusion IDAE Instituto para la Diversificación y el Ahorro de la Energía IEA International Energy Agency IRA Inflation Reduction Act KPI Key Parameter Indicator LCOH Levelised cost of hydrogen LHV Lower Heating Value LOHC Liquid Organic Hydrogen Carriers LPO Loans Programme Office MEA Membrane-Electrode Assembly NZE Net Zero Emissions by 2050 Scenario PEM Proton exchange membrane PPA Power Purchase Agreements ROS Reverse Osmosis System SOEC Solid oxide electrolyzer SOFC Solid oxide fuel cell TRL Technology readiness levels 1 THEORETICAL FRAMEWORK In this section, a theoretical framework for electrolysis will be developed, providing a comprehensive overview of the fundamental principles and processes involved. This includes an examination of the main properties of hydrogen, underlying chemical reactions, and various types of electrolysis technologies among others. Additionally, the relevance of hydrogen in the current economy will be contextualized, highlighting its growing importance as an energy carrier and its potential to drive the transition to a more sustainable energy system. This framework will serve as the foundation for understanding the subsequent analysis and optimization of electrolysis systems in the study. 1.1 Properties of hydrogen 1.1.1 Physical-chemical properties Hydrogen (H) is the simplest and smallest chemical element, consisting of a single proton and a single electron. Under normal conditions, hydrogen exists in the form of a diatomic gas, i.e., H2. At room temperature and pressure, it is a colorless, odorless flammable gas that is lighter than air. It has a high energy content per unit of mass (energy density), much higher than traditional fuels. However, it is a very light gas, with a density of only 0.09 kg/m3, what results in a low energy density by volume. Table 1-1 presents the physical properties of hydrogen and its comparison with the ones of natural gas, the most common gas in the energy sector (1). The Wobbe index measures the interchangeability of fuel gases: the closest the indexes, the easier to substitute without significantly affecting the performance of the combustion equipment. It is calculated by dividing the higher heating value (HHV) of the gas by the square root of its relative density compared to air at standard conditions. Table 1-1: Physical properties of hydrogen with respect to natural gas (adapted from (1)). Property H2 Compared to natural gas Density (gas) 0,089 kg/Nm3 (0°C, 1 bar) 1/10 Density (liquid) 70,79 kg/m3 (-253°C, 1 bar) 1/6 Boiling point -253 °C -90 °C Energy density (mass) 120 MJ/kg x2 Energy density (volume) 10,8 MJ/Nm3 1/3 Wobbe Index 11,29 kWh/Nm3 5/6 While hydrogen itself is not a toxic compound, it is a very small molecule with high diffusion coefficients, which increases the risk of leaks and weakening of materials used in its installations, such as steel pipes, connectors, and seals. It is also highly flammable due to its ignition conditions and the low temperature required for ignition to occur. However, this risk is partially mitigated by its high buoyancy and diffusivity, which facilitates its rapid dispersion in the air. Due to its colorless and odorless flame, fires and leaks are difficult to detect. In the following section, the main hazards of hydrogen are developed. Understanding them is of vital importance in the design of processes and safe installations, as hydrogen is a challenging gas to handle. Fortunately, hydrogen has been used in refineries and industry for long, and specific handling and design protocols are Theoretical framework 22 22 available and regulated by international agencies. Some of the most relevant codes are listed below. • NFPA 2 – Hydrogen Technologies Code. • UNE-ISO 14687:2019 – Hydrogen as Fuel. Product Specifications. • ISO 22734:2019 – Hydrogen Generators Using Water Electrolysis – Industrial, Commercial, and Residential Applications. • ISO/TR 15916:2015 – Basic Considerations for the Safety of Hydrogen Systems. • CGA G-5.5 – Hydrogen Vent Systems. • CGA G-5.6 – Hydrogen Pipeline Systems. • ASME B31.12 – 2019 – Hydrogen Piping and Pipelines. • ASME B31.8 – 2018 – Gas Transmission and Distribution Piping Systems. • UNE EN 181001:2010 – Hydrogen Technologies. Terminology. 1.1.2 Hazards of hydrogen As it was commented above, properties of hydrogen make it a tricky gas to deal with. Table 1-2 shows its chemical properties and also of natural gas. Table 1-2: Comparison of chemical properties of hydrogen and natural gas (adapted from (2)). Property H2 Natural gas Diffusion coefficient in air (cm2/s) (20°C, 1 atm) 0,61 0,16 Viscosity (g/cm-s x 10-5) (20°C, 1 atm) 0,083 0,651 Specific heat at constant pressure (J/(g·K)) 14,89 2,22 Ignition energy in air (mJ) 0,02 0,29 Ignition limits in air (vol %) 4,0 – 75,0 5,3 – 15,0 Auto ignition temperature (ºC) 585 540 Flame temperature in air (ºC) 2045 1875 Thermal energy radiated from flame to surroundings (%) 5-10 10-33 Detonation limits (vol % in air) 13-65 6,3-13,5 Maximum burning velocity (m/s) 2,6 0,43 The main hazards of hydrogen following the Health and Safety Executive 2010 (2) are described hereunder. • Propensity to leak: Due to its low viscosity hydrogen is prone to leak. Even if the system has previously been considered leak-tight with other gases like nitrogen, it will often be found to leak profusely when used with hydrogen. On another hand, hydrogen's extremely high diffusivity and buoyancy mean that leaks in well-ventilated areas are less likely to form flammable mixtures nearby. It disperses more rapidly than other fuel gases but may concentrate in elevated areas in enclosed spaces. In poorly ventilated or enclosed spaces, hydrogen concentration can rapidly become dangerous, especially near 23 Stationary simulation of an alkaline electrochemical plant in DWSIM 23 potential ignition sources. Preventing risks due to leakages of hydrogen can be done by locating all potential sources of ignition well below the level of the equipment from which hydrogen may leak and accumulate and ensuring adequate ventilation and safe discharge of the exhaust. • Propensity to cause embrittlement: Hydrogen can cause embrittlement of high strength steels, titanium alloys and aluminum alloys with cracking and catastrophic failure of the metals at stress below the yield stress. This is most commonly related to the carbon content of metallic alloys. Pure, unalloyed aluminum, however, is highly resistant to embrittlement. The industry standard for components in hydrogen service is grade 316 stainless steels. Cupro-nickel is also suitable for hydrogen service and copper can be used for low-pressure applications. • Propensity to ignite: Hydrogen readily forms explosive mixtures with air, with a wide flammability range from 4% to 75% (v/v). In addition, it has a very low ignition energy, making it highly susceptible to ignition even from small sparks, such as those from certain types of clothing. However, despite its wide flammability range, the lower explosive limit (LEL) is considered more significant than the width of its flammable range for hazard ranking, and it is similar to other gases like natural gas. Finally, spontaneous ignition of hydrogen has been observed upon sudden release from pressurized containers, but the exact mechanism behind these ignitions is not fully understood. • Consequences of a fire/explosion: Hydrogen burns with a hot, pale, almost invisible flame, making it difficult to detect in daylight. Despite its low heat radiation compared to hydrocarbons, the differences in heats of combustion, burning rate, and flame size make that, for example, the radiation from a flame above a burning pool of liquid, per unit area of pool surface, is about the same for liquid hydrogen and liquid natural gas (about 20 kW per square foot of pool surface). On another hand, the rapid burning rate of hydrogen reduces the total energy radiated for equal volumes consumed, potentially reducing the risk of secondary ignition and burns, but it makes it difficult to confine or stop hydrogen flames and explosions. Another downside of a higher burning velocity of hydrogen is that for a given scenario hydrogen would result in higher explosion pressures and rates of pressure rise than other fuels. • Possibility of detonation: Mixtures of hydrogen and air are more prone to detonate compared to mixtures of air with other common flammable fuels. Detonations are significantly more destructive and perilous than ordinary explosions known as deflagrations. However, the rapid dispersion properties of hydrogen mean that such detonations are only likely to happen in enclosed or crowded spaces. 1.2 Clean hydrogen status and outlook 1.2.1 International hydrogen status and outlook This section describes the international hydrogen landscape, focusing on the low-emission one, as reported by the IEA (International Energy Agency) in its “Global Hydrogen Review 2023” (3). Firstly, hydrogen utilization and production are examined. Although global hydrogen consumption is on the rise, the demand remains primarily focused on traditional sectors such as refining and the chemical industry, with most of the production still reliant on fossil fuels without emissions reduction measures. Moving on to the discussion on trade, there are positive developments to note. The number of announced projects continues to increase, mainly using ammonia as hydrogen carrier. However, there are areas where progress is scarce, particularly in hydrogen infrastructure and technological innovation. Regarding investment and innovation, supply chain disruptions and inflation have put some initiatives at risk. However, policy trends underscore the increasing number of governments announcing policies and strategies to support the expansion of low-emission hydrogen. 1.2.1.1 Current hydrogen market Global hydrogen use reached 95 Mt in 2022, increasing in all major consuming regions except Europe, where a reduction of nearly 6% was reported compared to the previous year. This can be explained by the reduction in the activity of chemical industries because of the sharp increase in natural gas prices, resulting from the Russia Theoretical framework 24 24 and Ukraine conflict. In contrast, North America and the Middle East observed strong growth (around 7% in both cases). In terms of total hydrogen consumption however, China stands out with about 30% of the whole world’s consumption (see Figure 1-1). Globally, hydrogen consumption has increased, but not as a result of hydrogen policies but rather of global energy trends. This is reflected in the fact that practically all the growth took place in traditional applications (mainly refining and chemical sector with 41 Mt and 53 Mt consumption respectively) and has been met by increasing production based on unabated fossil fuels. Figure 1-1 shows the evolution of global hydrogen consumption from 2020 to 2022 and the 2030 prediction following the NZE scenario. Figure 1-1: Hydrogen use by sector and by region, historical and in the Net Zero (3). Regarding hydrogen production, it reached almost 95 Mt in 2022, an increase of 3% compared to 2021. Natural gas without carbon capture accounted for 62% of global production, while unabated coal, mainly located in China, was responsible for 21% of global production. The remaining percentage was met principally by byproduct hydrogen produced in refineries and the petrochemical industry. Low-emission hydrogen production in 2022 was less than 1 Mt (0.7% of global production), and almost entirely from fossil fuels with CCUS. Production from water electrolysis continued to be relatively small, still below 100 kt H2 in 2022, which represents 35% growth compared to the previous year. In terms of installed capacity, by the end of 2022, the global installed water electrolyzer capacity for hydrogen production reached almost 700 MW, a 20% increase compared to the previous year. Alkaline electrolyzers accounted for 60% of the installed capacity, followed by proton exchange membrane (PEM) electrolyzers with around 30%. In terms of geographical distribution, China and Europe each accounted for one-third of the installed capacity at the end of 2022, while the combined share for the United States and Canada was 10%. Figure 1-2: Hydrogen production by technology, 2020-2022 (3). 25 Stationary simulation of an alkaline electrochemical plant in DWSIM 25 1.2.1.2 Clean hydrogen outlook 1.2.1.2.1 Hydrogen Production Projects Based on the projects monitored by the IEA in its Global Hydrogen Review 2023 (3), annual production of lowemission hydrogen could surpass 20 Mt by 2030. The corresponding number in the Global Hydrogen Review 2022 (GHR 2022) was 16 Mt H2 by 2030, indicating a more than 30% rise in announced low-emission hydrogen production projects within just one year. Half of the projects collected are currently undergoing feasibility studies, and almost the other half (45%) are in the very early stages of development. Only 4% of the announced projects, primarily associated with existing hydrogen applications in refineries and the chemical industry, are either under construction or have reached a final investment decision. Figure 1-3 (left) shows graphically the amount of hydrogen expected to be produced by electrolysis and CCUS (carbon capture, utilization, and storage) based on the projects announced, and their state. Electrolyzer projects dominate the landscape of announced hydrogen production projects, with over 70% of low-emission hydrogen production expected to be derived from electrolysis by 2030. According to the current data on announced projects, Europe and Australia account for almost 30% and 20%, respectively, in terms of production, of all planned electrolytic hydrogen projects by 2030. In Europe, Spain, Denmark, Germany, and The Netherlands are the leading countries, accounting for almost 55% of Europe’s electrolytic hydrogen production planned. Figure 1-3 (right) shows the hydrogen production by 2030 by different regions based on announced projects. Global installed electrolyzer capacity could reach 175 GW by 2030, an increase of 30% compared to the announcements in the GHR 2022, and it increases to 420 GW when projects at early stages of development are also taken into consideration. The expansion of installed electrolyzer capacity is also marked by a shift towards larger projects being announced. While the average size of electrolyzer plants currently stands at around 12 MW, it is anticipated to increase to several hundred MW within a few years, and even reach 1 GW by 2030. Projects at the gigawatt scale are expected to account for over 75% of the announced capacity for 2030. Figure 1-3: Low-emission hydrogen production by technology route, maturity and region based on announced projects and in the NZE, 2030. (3) Theoretical framework 32 32 Figure 1-10: Map of projects registered in the Project Census classified by activity (5). Regarding the source of energy production, 56% of projects are electrolysis from renewable electricity by direct connection, and around 35% involve the use of Power Purchase Agreements (PPAs). The type of electrolysis used depends on the scale, being alkaline electrolysis the predominant for power over 100 MW, and PEM for small and medium-size projects. SOEC and AEM are only used in a few low-power projects. Total investment obtained from the 123 projects collected in the Census accounts for 21 000 M€, while the total estimated investment in the roadmap is 8900 M€ during the period 2020-2030. Based on the little number of projects in an advanced stage, it seems like the investment will be closer to the estimation of the roadmap. Even though not all projects planned are finally carried out, interest in the sector is evident. In relation to investment, public funding is an important part for achieving the development of hydrogen. Figure 1-11 shows the distribution of public aid received by the projects in the Census, which comes mainly from CDTI (Centro para el Desarrollo Tecnológico y la Innovación), IDAE (Instituto para la Diversificación y el Ahorro de la Energía) and the Clean Hydrogen Partnership. Figure 1-11: Cumulative public funding per year and per entity. (5) CDTI focuses its contribution on projects with less developed technologies, accounting for about 2% of the total investment. IDAE is expected to provide most of the funding, around 53%, although part of this percentage is coordinated with other public financing programs. Programs managed directly by the European Commission also play a significant role, contributing approximately 20% of the funding. Additionally, other grants such as those from IPCEI or regional programs contribute almost 25% of the total public funding. 33 Stationary simulation of an alkaline electrochemical plant in DWSIM 33 1.3 Production of hydrogen 1.3.1 Production methods and classifications Nowadays, 62% of the hydrogen produced internationally is from natural gas (95% in Spain) using steam reforming. However, there are numerous ways of producing hydrogen, as shown in Figure 1-12. The diagram presents a comprehensive overview of the various hydrogen production pathways, highlighting the wide range of feedstocks and primary energy sources that can be employed. From traditional fossil fuels such as natural gas and coal to renewable sources like biomass and water using solar, hydro or wind energy. It is interesting to note how the same feedstock, like natural gas, can be processed through different routes (steam reforming, partial oxidation, and autothermal reforming), reflecting the complexity and versatility of hydrogen production routes. Figure 1-12: Primary energy sources, feed stocks and routes for hydrogen production. (6) Different classifications of the production methods can be made. Regarding the nature of the method, routes can be thermic, electrochemical, or biological (see Table 1-3). Table 1-3: Hydrogen production route depending on its nature. Method Route Thermal Steam Reforming Thermolysis Partial Oxidation Gasification Electrochemical Electrolysis Photoelectrolysis Biological Photobiological Fermentation Theoretical framework 34 34 Anaerobic digestion Another classification can be made based on its environmental impact, where numerous ‘colors’ have been described to distinguish the different routes. The three main colours though are gray, blue and green, from the most to the least environmental impact. Gray hydrogen The term gray hydrogen is used to define hydrogen generated using fossil resources and releasing carbon dioxide into the atmosphere produced during the reforming reaction, such as that generated from natural gas. According to the recommendation of CertifHy (a platform of companies proposing an Origin Certificate for the hydrogen market in the European Union), gray hydrogen is defined as: "hydrogen generated from non-renewable sources that emits more than 4.37 kgCO2eq/kgH2 (36.4 CO2eq/MJ). Without delving into the specifics of different reforming technologies, gray hydrogen production can be considered to have emissions exceeding 10-11 kg CO2eq/kgH2 generated. Of these emissions, two-thirds come from the reforming chemical reaction itself plus the shift reaction, and one-third from burning fuel to attain the necessary temperature for reforming. (7) Blue hydrogen In blue hydrogen, a significant portion of carbon dioxide emissions are captured. Carbon dioxide capture is unequivocally defined as the physical-chemical separation of the hydrogen stream, transportation, and storage or fixation for a long period, which, according to environmental regulations, should be equal to or greater than 100 years. According to CertifHy blue hydrogen is defined as "hydrogen generated from non-renewable sources that emits less than 4.37 kgCO2eq/kgH2 (36.4 CO2eq/MJ)." (7) Green Hydrogen Green hydrogen is defined as low-carbon hydrogen obtained from renewable sources. This definition implies, like the blue hydrogen, that the process must have a lower environmental impact than the current one. Therefore, the technical definition of green hydrogen is: "hydrogen generated from renewable sources that emits less than 4.37 kgCO2eq/kgH2 (36.4 CO2eq/MJ)." (7) In the EU however, green hydrogen includes only the hydrogen produced by electrolysis that complies with emissions restrictions, and the biological one, excluding the hydrogen produced from biomass, even though it is considered renewable and may comply with emissions restrictions. Figure 1-13: Main colours of hydrogen in function of its source and emissions (original creation). When comparing the origin of hydrogen (grey, blue, or green), the cradle-to-gate approach is used, which considers all the impacts of the hydrogen generation process until it is delivered to the customer. Therefore, all processes, even the greenest ones, have associated emissions, whether in the construction, transportation, operation, or dismantling phases of the facilities where hydrogen is obtained. 35 Stationary simulation of an alkaline electrochemical plant in DWSIM 35 European and national targets seek to shift hydrogen production away from fossil fuels, and water electrolysis seems to be the method with the highest potential. Therefore, it is the one developed deeper in this study. The following sections describe the different electrolysis technologies: alkaline (ALK), polymeric membrane (PEM), solid oxide (SOEC) and anion membrane (AEM), focusing on the two first, as are the ones with the higher TRL. 1.3.2 Water electrolysis Electrolysis has been used for more than 100 years for hydrogen production, using hydropower energy as power source, therefore renewable. The Greek term "lysis" means breakdown, so water electrolysis is described as the process by which water (H2O) is decomposed into oxygen (O2) and hydrogen (H2) using a continuous electric current, power source, or battery connected via two electrodes (anode and cathode) to the water. Reaction 1 represents the basic electrolysis reaction, or water dissociation. 2𝐻2𝑂→𝑂2+2𝐻2 Reaction 1 Nowadays there are four different types of electrolysis, that differ from each other in the electrolyte material used. Of these, only two are commercial at large scale, ALK and PEM. The other two are in laboratory or preindustrial scale (i.e., SOEC and AEM). The electrolyzer, understood as a functional and operational system that, when connected to an AC power source and a water supply under specific conditions is capable of producing hydrogen and oxygen, is compound of the electrolysis stack or stacks, where the main reaction occurs, and the Balance of Plant (BoP), which refers to all the supporting components and auxiliary systems, other than the generating unit itself. These are compressors, transformers, rectifiers, separators or purifying systems among others. Therefore, when referred to ‘electrolyzer’, it is important to clarify if it is being talked about the stack or the whole system, as efficiency and other parameters will defer from one another. Figure 1-14 (8) shows an electrolysis stack and its BoP. Figure 1-14: Electrolyzer stack, Balance of Plant (BoP) and power source. (8) The following subsections describe the thermodynamic of water electrolysis, its key parameters, and the different kinds of electrolysis stacks. 1.3.2.1 Thermodynamics of electrolysis Electrolysis is a non-spontaneous process (∆G > 0) and therefore requires an external energy input, which can be determined using the First Law of Thermodynamics, according to Equation 1. ∆𝐺(𝑇,𝑃)=∆𝐻(𝑇,𝑃)−𝑇∆𝑆(𝑇,𝑃) Equation 1 The total energy required for water electrolysis to occur is defined by the reaction enthalpy (∆H), which includes a portion of thermal origin (T·ΔS) representing the irreversibilities of the system, and another portion of electrical Theoretical framework 36 36 origin (∆G, Gibbs free energy). Figure 1-15 (left) represents the three mentioned terms in function of temperature. It is observed that the electrical demand decreases as the temperature increases, with the reduction being much more significant when the process occurs with steam rather than liquid water (starting from 100°C). (9) Figure 1-15: Thermodynamics of water electrolysis (Adapted from (9)): (left) influence of temperature on the thermodynamic parameters of water electrolysis at 1 bar; (right) dependence of reversible voltage and thermoneutral voltage on temperature at 1 bar. 1.3.2.1.1 Reversible voltage As defined earlier, water is decomposed into hydrogen and oxygen by the application of a continuous electric current between the anode and the cathode. The minimum potential difference between the electrodes for the electrolysis reaction to occur is known as the reversible voltage and can be obtained by Equation 2, where 𝑧 is the number of electrons shared in the reaction, and 𝐹the Faradaic constant, equal to 96484 C/mol. 𝑉𝑟𝑒𝑣 =∆𝐺 𝑧·𝐹 Equation 2 In standard conditions, 25°C and 1 atm, the reversible voltage is equal to 𝑉𝑜𝑟𝑒𝑣=1.23 V. The value of the reversible voltage under conditions other than standard is given by the Nernst Equation (Equation 3). 𝑅 is the ideal gas constant, equal to 8314 J/molK, 𝑇(K) is the temperature, and 𝐾 the equilibrium constant. 𝑉𝑟𝑒𝑣 =𝑉𝑜𝑟𝑒𝑣 +𝑅𝑇 𝑧𝐹𝑙𝑛𝐾 Equation 3 1.3.2.1.2 Thermoneutral voltage When the electrolytic process is carried out under adiabatic conditions (without heat generation or absorption), the enthalpy of the total reaction (∆H) must be supplied by electrical current. The required voltage is called the thermoneutral voltage (𝑉𝑡𝑛), which, analogously to the reversible voltage, is related to the enthalpy through Equation 4. 𝑉𝑡𝑛 =∆𝐻 𝑧·𝐹=∆𝐺+𝑇∆𝑆 𝑧·𝐹 =𝑉𝑟𝑒𝑣 +𝑇∆𝑆 𝑧·𝐹 Equation 4 In standard conditions, the thermoneutral voltage is 𝑉𝑜𝑡𝑛 =1.48 V. Figure 1-15 (right) shows the evolution of both reversible and thermoneutral voltage with temperature. The reversible voltage decreases significantly with temperature, while the thermoneutral voltage remains almost constant due to the fact that the variation in Gibbs free energy (ΔG) with temperature changes is compensated by the variation in thermal energy (T⋅ΔS). Figure 1-15 (right) also allows dividing the temperature-cell potential plane into three different zones where an electrolysis cell can operate depending on the applied voltage between the cell electrodes and the operating 37 Stationary simulation of an alkaline electrochemical plant in DWSIM 37 temperature. Zone 1: If a voltage lower than the reversible voltage (𝑉𝑐𝑒𝑙𝑙 <𝑉𝑟𝑒𝑣) is applied, electrolysis does not occur and no current flows between the cell electrodes. Zone 2: When a voltage is applied between the reversible voltage and the thermoneutral voltage (𝑉𝑡𝑛 <𝑉𝑐𝑒𝑙𝑙 <𝑉𝑟𝑒𝑣) for a given pressure and temperature, electrolysis occurs as long as thermal energy (𝑄𝑎𝑝𝑝𝑙𝑖𝑒𝑑) equal to the difference between the thermoneutral voltage and the applied voltage is supplied, according to Equation 5. If no heat is supplied, the system cools down and stops. 𝑄𝑎𝑝𝑝𝑙𝑖𝑒𝑑 =(𝑉𝑡𝑛 −𝑉𝑐𝑒𝑙𝑙)·𝐼 Equation 5 Zone 3: In this region, when a voltage higher than the thermoneutral voltage is applied, water electrolysis occurs, and the excess applied energy is degraded into thermal energy, which must be dissipated by the cooling system. The generated heat (𝑄𝑔𝑒𝑛) is defined by the Equation 6. 𝑄𝑔𝑒𝑛 =(𝑉𝑐𝑒𝑙𝑙 −𝑉𝑡𝑛)·𝐼 Equation 6 1.3.2.1.3 Real voltage When an electrolysis cell starts operating, the voltage is always higher than the reversible voltage and even higher than the thermoneutral voltage due to the irreversibilities of the process. Therefore, commercial electrolyzers typically operate in Zone 3 as depicted in Figure 1-15. In general, the voltage of an electrolytic cell (𝑉𝑐𝑒𝑙𝑙) is determined by the sum of the contributions from the reversible voltage and a series of overpotentials. These overpotentials, caused by process irreversibilities, can be classified into activation overvoltage, ohmic overvoltage, and concentration overvoltage. Activation overvoltage (𝑉𝑎𝑐𝑡): Due to the activation energy of the electrochemical reactions occurring at the electrodes. It represents a loss due to the limited rate of charge transfer. Choosing an appropriate catalyst and increasing the operating temperature can decrease the value of this overvoltage. (9) Ohmic overvoltage (𝑉𝑜ℎ𝑚): Due to ionic resistance in the electrolyte, electrical resistance in the electrodes and collectors, and contact resistance. It is proportional to the current density (according to Ohm's law) and depends on the type of material used, stack geometry, and operating temperature. (9) Concentration overvoltage (𝑉𝑐𝑜𝑛): Due to limitations in mass transport, which occur especially at high current densities. The greatest influence is given by limitations in the transport of the hydrogen and oxygen products, as if they are not removed as quickly as they are produced, their concentration at the reaction site increases, thereby decreasing the reaction kinetics. It is usually detected by formation of bubbles, that limit the mass transport. Concentration losses can be negligible at low currents. (9) Therefore, the total potential of an electrolysis process is given by the algebraic sum of all the above-mentioned overpotentials according to Equation 7. 𝑉𝑐𝑒𝑙𝑙 =𝑉𝑟𝑒𝑣 +(𝑉𝑎𝑐𝑡 +𝑉𝑜ℎ𝑚+𝑉𝑐𝑜𝑛) Equation 7 1.3.2.1.4 Polarization curve (I-V) The relationship between voltage and current intensity of an electrochemical cell provides the V-I characteristic curve or polarization curve. These characteristic curves allow for the analysis of an electrolytic cell's behaviour, as they enable the study of the electrolysis reaction kinetics and determine various operating points. Additionally, the I-V curve reveals the effect of overvoltages due to irreversibilities and allows for the determination of process efficiency. Figure 1-16 shows a general polarization curve for an electrolytic cell. It is observed that for intermediate current densities, the curve's behaviour is linear because ohmic overpotentials predominate. However, for very low current densities, a much steeper slope is observed due to activation overpotentials. At high current densities, concentration overpotentials predominate, and the system's efficiency is significantly reduced, potentially causing severe damage to system components. Theoretical framework 38 38 Figure 1-16: Polarization curve of an electrolytic cell and overpotentials. (Adapted from (10)) Another factor that can be analyzed is the effect of temperature. The higher the temperature, for the same current density, the lower the voltage is required for the electrolytic cell to work, therefore, higher efficiency is obtained. Ullberg developed an experimental expression for the modeling of the polarization curve, expressed in Equation 8. (9) 𝑉𝑐𝑒𝑙𝑙 =𝑉𝑟𝑒𝑣 +𝑟·𝑖+𝑠·log⁡(𝑡·𝑖+1) Equation 8 The dependence of the voltage of the cell on the overpotentials is defined by the parameters "s" and "t," related to the activation overvoltage, and the parameter "r" related to the ohmic overvoltage. The parameter "s" can be assumed constant for any operating conditions, while "r" and "t" depend on the temperature (T in °C), according to the coefficients proposed by Ulleberg.(9) 𝑟=𝑟1+𝑟2𝑇 Equation 9 𝑡=𝑡1+𝑡2 𝑇+𝑡3 𝑇2 Equation 10 1.3.2.2 Key parameters The key parameters considered in this section are efficiency, and the operating parameters that mainly affect it, namely operating pressure, operating temperature and current density. 1.3.2.2.1 Efficiency Efficiency refers to the electrical power required to produce a certain amount of hydrogen per unit of time. It can be expressed in various ways, such as a percentage (relative to the lower heating value of hydrogen) or as specific consumption, in kWh/kg hydrogen, indicating how much energy in kilowatt-hours is needed to produce one kilogram of hydrogen. A systematic examination of the efficiency of an electrolysis plant yields many specifications for a wide range of system boundaries. In practical applications of this technology, metrics such as stack, system, and plant efficiency are frequently discussed and referenced. All three of these represent the products of various subefficiencies, and sometimes are confused and not clearly defined. The most frequently used parameters are presented below. (11) 39 Stationary simulation of an alkaline electrochemical plant in DWSIM 39 Faraday efficiency Faraday efficiency expresses the extent to which electrical current is converted into the desired reaction, specifically the production of hydrogen. It is calculated as the ratio of the hydrogen produced to the theoretical quantity of hydrogen that could be produced (12). 𝜂𝑓=𝑛󰇗𝑒𝑥𝑝 𝑛󰇗𝑡ℎ Equation 11 Equation 11 shows the expression for the Faraday efficiency 𝜂𝑓, where 𝑛󰇗𝑒𝑥𝑝(mol/s) is the experimental mole flow of hydrogen, and 𝑛󰇗𝑡ℎ (mol/s) the theoretical one, that can be obtained from Equation 12. The cells of the stacks are electrically connected in series, which means that through each cell passes the same current. Therefore, to obtain the total amount of hydrogen produced, current I (A) must be multiplied by the number of cells 𝑁𝑐𝑒𝑙𝑙, and divided by 𝑧, the number of electrons exchanged in the reaction, equal to two, and 𝐹, the Faradaic constant, equal to 96484 C/mol. (12,13) 𝑛󰇗𝑡ℎ =𝐼𝑛𝑐𝑒𝑙𝑙 𝑧𝐹 Equation 12 Since the 1990s, a few works have been published in the literature on the analysis and the modelling of Faraday’s efficiency. The first empirical model was developed by Hug et al. and can provide good accuracy (12). It is modelled by five parameters and shows that Faraday efficiency mainly depends on temperature and current (Equation 13). In Figure 1-17 Faraday efficiency is plotted in function of current density and temperature. The higher the current density, the higher the faraday efficiency, which closely approximates100%. Regarding temperature, its effect is clear for low current densities, and affects efficiency negatively, but as current density increases its effect fades away. 𝜂𝑓=𝐵1+𝐵2𝑒𝑥𝑝𝐵3+𝐵4𝑇+𝐵5𝑇2 𝐼 Equation 13 Figure 1-17: Faraday efficiency according to the current density at different temperatures.(12) Voltage efficiency Voltage efficiency is defined as the fraction between the thermoneutral voltage, and the real voltage applied to the electrolysis cell. 𝜂𝑉=𝑉𝑡𝑛 𝑉𝑐𝑒𝑙𝑙 Equation 14 This is a very useful design parameter for optimizing electrolysis equipment, which can be increased by minimizing the electrical resistances of each component of the cell. This efficiency quantifies how well Theoretical framework 40 40 electrolysis is carried out, as the irreversibilities that occur in this process manifest themselves in the form of overpotentials. From a practical standpoint, it is possible to operate an electrolyzer with voltage efficiencies greater than 100%: if the electrolyzer operates at a voltage lower than the thermoneutral voltage, provided that thermal energy is supplied to the system.(9) Global efficiency The overall efficiency is defined as the ratio between the energy contained in the produced hydrogen and the total energy supplied to the electrolysis cell, including both the electrical consumption and the thermal component. This efficiency refers to the cell, not the whole electrolysis system (stacks + BoP) and is sometimes confused with the system’s one. However, in practice this efficiency is not usually used, as what is more relevant for the cost of the system is the electrical energy, not the thermal one. The most used formula to calculate the efficiency (𝜂𝑔) of an electrolyzer is expressed in Equation 15, where 𝑚⁡󰇗 (𝑘𝑔 𝑠) is the hydrogen mass flow produced by the electrolysis stack, 𝐿𝐻𝑉(𝑘𝐽 𝑘𝑔) is the low heating value of hydrogen, 𝐼(𝐴) is the current, 𝑉𝑐𝑒𝑙𝑙(𝑉) is the cell’s voltage, 𝑁𝑐𝑒𝑙𝑙 is the number of cells in one stack, and 𝑃𝐵𝑜𝑃(𝑘𝑊) is the electric power required by the BoP. (8) It can also be referred to the stack only (𝜂𝑠𝑡𝑎𝑐𝑘), and not the BoP, if 𝑃𝐵𝑜𝑃 is not taken into consideration. 𝜂𝑔=𝑚󰇗𝐿𝐻𝑉 𝐼𝑉𝑐𝑒𝑙𝑙𝑁𝑐𝑒𝑙𝑙 +𝑃𝐵𝑜𝑃 Equation 15 1.3.2.2.2 Operating pressure Electrolyzers are capable of compressing hydrogen and delivering it at a certain pressure (depending on the manufacturers and technologies). Operating at pressure results in an increase in cell voltage and therefore a deterioration in efficiency of the stack. However, as it can be seen in Figure 1-18, the increase in the voltage is really low, meaning that the efficiency of compression of the electrolytic cell is very high (higher than the mechanical compressors). Therefore, analyzing the electrolyzer as part of a global system, it is highly recommended to operate at high pressures because this will reduce the consumption of subsequent compressors, thereby improving the overall efficiency of the system (in some cases, it may even eliminate the need for a compressor altogether if the final application does not require high pressures in its process, such as in the industrial and chemical sectors, among others). Figure 1-18: Effects of operating pressure in the polarization curve of electrolyzers. (8) 1.3.2.2.3 Operating temperature A high operating temperature, due to the thermodynamics of the electrolysis process, is beneficial for the efficiency of the stack, as the more energy is supplied to the process in the form of heat, the less energy needs to 41 Stationary simulation of an alkaline electrochemical plant in DWSIM 41 be supplied in the form of electricity. Therefore, one of the main lines of research for manufacturers is to increase the temperature without reducing the system's lifespan to improve its efficiency. (8) The effect of efficiency improvement is greater when transitioning from feeding liquid water to feeding water in the form of steam, as the energy required for the phase change (evaporation heat) no longer needs to be supplied. Consequently, the total energy required for electrolysis is significantly reduced (see Figure 1-15). In this sense, the so-called high temperature electrolysis processes are being studied, being the most closed to commercial level the SOEC. These processes are specially interesting when coupled with waste heat-producing technologies (such as those used in ammonia production, methanol production or steel production). Waste heat is used for producing the steam that feeds the electrolyser, achieving a high degree of thermal integration. 1.3.2.2.4 Current density Current density is defined as the amperes flowing per square meter of active area of the stack membranes. It is one of the key parameters of different electrolysis technologies, each of which operates within a characteristic range (for example ALK electrolyzers work within 0,2-0,8 A/cm2 and PEM ones within 1-3 A/cm2(9)). Higher current density allows higher hydrogen production rates for the same size of electrolyzer, which at the end is translated into more compact equipment. However, it also results in a decrease in efficiency due to the increase in overvoltages. (8) 1.3.2.3 Types of electrolysis In this section the four main types of electrolysis are described (ALK, PEM, SOEC, and AEM), conducting a more in-depth study of those already at the industrial level, namely PEM and ALK. Electrolyzers are usually classified based on the working temperature, as near ambient temperature electrolyzers, which includes ALK, PEM, and AEM, and high temperature electrolyzers, that includes SOEC and may also include proton conducting ceramic electrolyser. However, this last technology has a very low TRL and is not analyzed here. 1.3.2.3.1 Alkaline electrolysis (ALK) Alkaline electrolyzers represent the most developed water electrolysis technology to date, primarily in industrialscale MW applications (there were over 400 industrial water electrolyzers in use already in 1902 (14)). Figure 1-19: Alkaline electrolyzer scheme. (7) An ALK cell (see Figure 1-19) consists of two electrodes (cathode and anode) fully immersed in an electrolyte, usually of 20-40% potassium hydroxide (KOH), with a microporous diaphragm separator. If a potential is applied to the electrodes of an electrolysis cell the following reactions occur at the electrodes, where l is liquid, g is gaseous, and aq is dissolved in water. 𝐶𝑎𝑡ℎ𝑜𝑑𝑒:⁡⁡⁡⁡2𝐻2𝑂⁡(𝑙)+2𝑒−→𝐻2(𝑔)+2𝑂𝐻−(𝑎𝑞) Reaction 2 𝐴𝑛𝑜𝑑𝑒:⁡⁡⁡⁡⁡2𝑂𝐻−(𝑎𝑞)→1 2𝑂2(𝑔)+𝐻2𝑂⁡(𝑙)+2𝑒− Reaction 3 Theoretical framework 48 48 Load range % 15–100 5–130 30–125 5–100 H2 purity % v/v 99.9–99.9998 99.9–99.9999 99.9 99.9–99.999 Voltage efficiency (LHV) % 50–68 50–68 75–85 52–67 Electrical efficiency (stack) kWh kgH2 -1 47–66 47–66 35–50 51.5–66 Electrical efficiency (system) kWh kgH2 -1 50–78 50–83 40–50 57–69 Lifetime (stack) h 60 000 50 000– 80 000 <20 000 >5000 Stack unit size kW 1000 1000 5 2.5 Electrode area cm2 10 000– 30 000 <3000 200 <300 Cold start (to nom. load) min <50 <20 >600 <20 49 Stationary simulation of an alkaline electrochemical plant in DWSIM 49 2 ELECTROLYSIS SIMULATION The aim of this section is to develop a mathematical model of a steady state alkaline electrolyzer that results in hydrogen and oxygen production, utilities consumption, and energy losses based on the electrolysis power, geometric parameters of the electrolyzer (number of cells and active area), and operating conditions (inlet and outlet temperature and pressure). The simulation is divided into two, one consisting of the simulation of the electrolysis stack, and the other consisting of the BoP simulation. The electrolysis stack is simulated by a Custom Model using Python, where semi-empirical relations have been used to model the electrochemical parameters. Therefore, the coefficients governing the equations of the stack can be obtained experimentally. On another hand, the BoP simulation has been performed in the DWSIM flowsheet interface, with standard units. The model allows to analyze the influence of the main parameters affecting the steady-state performance of electrolysis systems, accounting for the difference existing between the electrolysis stack and the whole system (including the BoP). In this way, it may serve as a digital twin capable of optimizing the design (number of cells and active area) and operating conditions. 2.1 Electrolyzer Stack simulation The electrolyzer stack simulation can be divided into two blocks, the electrochemical model and the material and energy balance. Figure 2-1 shows the inputs and outputs of each block and the whole electrolysis stack model. Figure 2-1: Electrolysis Stack Model Scheme. (Adapted from (9)) 2.1.1 Electrochemical model 2.1.1.1 Electrochemical model expressions The electrochemical model gives as results the cell voltage, the faraday efficiency and the hydrogen to oxygen diffusion (HTO). Based on the electrochemical principles explained in previous sections, the cell voltage in the stack would be the sum of the reversible voltage for the electrolysis of water at the operating temperature and pressure, and the overpotentials (Equation 16). 𝑉𝑐𝑒𝑙𝑙 =𝑉𝑟𝑒𝑣(𝑇𝑜𝑝,𝑃𝑜𝑝)+∑𝑉𝑖⁡⁡⁡⁡⁡⁡⁡⁡𝑖=𝑎𝑐𝑡,𝑜ℎ𝑚,𝑐𝑜𝑛 Equation 16 The reversible voltage depends mainly on temperature, diminishing when temperature increases. This effect can Electrolysis simulation 50 50 be empirically modelled by the equation of LeRoy (9), shown in Equation 17. Pressure has little effect on the voltage, therefore, although some models have been developed to account for its effect, it is not going to be considered in this study. 𝑉𝑟𝑒𝑣(𝑇)=1.5184−1.54·10−3𝑇𝑜𝑝+9.523·10−5𝑇𝑜𝑝 ·ln(𝑇𝑜𝑝)+9.84 ·10−8𝑇𝑜𝑝2 Equation 17 The total cell voltage depends on the current density, and has been empirically modelled by Ulleberg (9), according to Equation 8 showed in the section ‘Polarization curve (I-V)’, where the coefficients r and t are dependent on temperature. In the study developed by Monica Delgado (9) it is proposed a model that takes into account the effect of pressure. However, it is concluded these effects are not relevant. Regarding the Faraday efficiency, Ulleberg proposed an equation to model it as a function of the current density (𝑖) and temperature (Equation 18). As before, the effect of pressure can be neglected. 𝜂𝐹=𝑓2𝑖2 𝑓1+𝑖2 Equation 18 𝑓1=𝑓11+𝑓12𝑇𝑜𝑝 𝑓2=𝑓21+𝑓22𝑇𝑜𝑝 Finally, it must be considered the hydrogen that, by diffusion, exits the electrolysis cell with the oxygen, which is called HTO (Hydrogen To Oxygen). The HTO is the percentage (%vol) of hydrogen in the oxygen stream (anode) leaving the electrolysis stack, as it is shown in Equation 19. The oxygen that exits through the hydrogen semi-cell (cathode) has been neglected though (9). 𝐻𝑇𝑂= 𝑛𝐻2,𝑎𝑛𝑜𝑑𝑒 𝑛𝐻2,𝑎𝑛𝑜𝑑𝑒 +𝑛𝑂2·100 Equation 19 The purity of the gases can be theoretically calculated using diffusion coefficients and gas solubility values. However, these parameters are not always easy to estimate. For this reason, Hug proposed a semi-empirical model to calculate the purity of the produced oxygen as a function of temperature, where the terms from 'C1' to 'C9' are constants representing the temperature influence. However, in the studies made by Mónica Delgado (9) and Andrea Pancotto (19) pressure is said to significantly influence the hydrogen to oxygen diffusion, so the model includes it, and it is assumed the same relation as with temperature, but with coefficients from ‘e1’ to ‘e9’ (Equation 20).(9) 𝐻𝑇𝑂=[𝐶1+𝐶2𝑇𝑜𝑝 +𝐶3𝑇𝑜𝑝2+(𝐶4+𝐶5𝑇𝑜𝑝 +𝐶6𝑇𝑜𝑝2)𝑒𝑥𝑝(𝐶7+𝐶8𝑇𝑜𝑝 +𝐶9𝑇𝑜𝑝2 𝑖)] +[𝑒1+𝑒2𝑃𝑜𝑝 +𝑒3𝑃𝑜𝑝2+(𝑒4+𝑒5𝑃𝑜𝑝 +𝑒6𝑃𝑜𝑝2)𝑒𝑥𝑝(𝑒7+𝑒8𝑃𝑜𝑝+𝑒9𝑃𝑜𝑝2 𝑖)] Equation 20 The values for the coefficients governing the different equations have been obtained from the literature, although, in order to perform an accurate simulation, they should be obtained experimentally for the specific 51 Stationary simulation of an alkaline electrochemical plant in DWSIM 51 model that wants to be simulated. Table 2-1 summarizes the equations used to model the electrochemical behaviour of an ALK electrolyzer with its parameters’ values. Values of the parameters for each equation correspond to a specific model of alkaline electrolyser. Table 2-1: Electrochemical model resume. Reversible Voltage (Equation 17) (9) 𝑉𝑟𝑒𝑣(𝑇𝑜𝑝)=𝑎1+𝑎2𝑇𝑜𝑝+𝑎3𝑇𝑜𝑝 ·ln(𝑇𝑜𝑝)+𝑎4𝑇𝑜𝑝2 a1 1.5184 V a2 −1.54·10−3 V K-1 a3 9.523·10−5 V K-1 a4 9.84·10−8 V K-2 Polarization Curve (Equation 8) (20) 𝑉𝑐𝑒𝑙𝑙(𝑇𝑜𝑝,𝑖)=𝑉𝑟𝑒𝑣 +(𝑟1+𝑟2𝑇𝑜𝑝)·𝑖+𝑠·log⁡[(𝑡1+𝑡2 𝑇𝑜𝑝 +𝑡3 𝑇𝑜𝑝2)·𝑖+1] r1 8.05 · 10−5 Ω m2 r2 −2.5 · 10−7 Ω m2 °C-1 s 0.185 V t1 1.002 m2 A-1 t2 8.424 m2 °C A-1 t3 247.3 m2 °C2 A-1 Faraday Efficiency (Equation 18) (9) 𝜂𝐹(𝑇𝑜𝑝,𝑖)=(𝑓21+𝑓22𝑇𝑜𝑝)𝑖2 𝑓11+𝑓12𝑇𝑜𝑝+𝑖2 f11 4.79·105 A2 m-4 f12 -2.95·103 A2 m-4 °C-1 f21 1.0396 - f22 -1.04·10-3 °C-1 Hydrogen to Oxygen (HTO) (Equation 20) (9) 𝐻𝑇𝑂(𝑇𝑜𝑝,𝑃𝑜𝑝,𝑖)=[𝐶1+𝐶2𝑇𝑜𝑝 +𝐶3𝑇𝑜𝑝2+(𝐶4+𝐶5𝑇𝑜𝑝 +𝐶6𝑇𝑜𝑝2)𝑒𝑥𝑝(𝐶7+𝐶8𝑇𝑜𝑝+𝐶9𝑇𝑜𝑝2 𝑖)] +[𝑒1+𝑒2𝑃𝑜𝑝 +𝑒3𝑃𝑜𝑝2+(𝑒4+𝑒5𝑃𝑜𝑝 +𝑒6𝑃𝑜𝑝2)𝑒𝑥𝑝(𝑒7+𝑒8𝑃𝑜𝑝+𝑒9𝑃𝑜𝑝2 𝑖)] Electrolysis simulation 52 52 C1 0.09901 - C2 -0.00207 °C-1 C3 1.31064·10-5 °C-2 C4 -0.08483 - C5 0.00179 °C-1 C6 -1.13390·10-5 °C-2 C7 1481.45 A m-2 C8 -23.60345 A m-2 °C-1 C9 -0.25774 A m-2 °C-2 e1 3.71417 - e2 -0.93063 bar-1 e3 0.05817 bar-2 e4 -3.72068 - e5 0.93219 bar-1 e6 -0.05826 bar-2 e7 -18.38215 A m-2 e8 5.87316 A m-2 bar-1 e9 -0.46425 A m-2 bar-2 2.1.1.2 Analysis of electrochemical parameters of the model dependence on current density, temperature and pressure In this section it is analysed the behaviour of the main electrochemical parameters modelled (𝑉𝑟𝑒𝑣, 𝑉𝑐𝑒𝑙𝑙, ⁡ 𝜂𝐹 ,⁡ 𝐻𝑇𝑂) with temperature, current density and pressure. Firstly, Figure 2-2 shows the dependence of the reversible voltage with temperature. As explained in section 1.3.2.1 Thermodynamics of electrolysis, it is the minimum voltage that must be applied for the reaction to occur, and it decreases with temperature. For standard conditions 𝑉𝑟𝑒𝑣=1.23 V, as it can be observed in the graph. 53 Stationary simulation of an alkaline electrochemical plant in DWSIM 53 Figure 2-2: Reversible Voltage vs Temperature. On another hand, Figure 2-3 represents the behaviour of the cell’s voltage with current density and temperature. The cell’s voltage is composed of the reversible voltage and the overpotentials due to irreversibilities. As the current density increases, the Joule’s losses increase, and so does the cell’s voltage. On another hand, because of the effect of temperature in the reversible voltage explained before, the cell’s voltage decreases with temperature. Thermodynamically, this is explained by the sum of two effects. Firstly, the total energy required for the reaction to occur diminishes with temperature for the considered range (see Figure 1-15) and, secondly and principally, the total energy is supplied as electricity and heat so, when temperature increases (heat increases), the electrical energy required (and, therefore, the cell’s voltage) decreases. It can be concluded that the current density has a negative effect on the voltage efficiency and the temperature has a positive one. Figure 2-3: Cell’s Voltage dependence on current density and temperature. Figure 2-4 represents the dependence of the Faraday Efficiency on current density and temperature. In this case, an increase in current density leads to an increase in the Faraday efficiency, while an increase in temperature decreases the Faraday Efficiency. On the contrary than it occurred with the voltage, an increase in the current 1.16 1.17 1.18 1.19 1.20 1.21 1.22 1.23 1.24 1.25 1.26 010 20 30 40 50 60 70 80 90 100 Vrev (V) Temperature (°C) Reversible Voltage (V) 0.00 0.50 1.00 1.50 2.00 2.50 3.00 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Vcell (V) Current Density (A/cm2) Cell's Voltage (V) Top=10°C Top=40°C Top=70°C Top=100°C Electrolysis simulation 54 54 density has a positive effect on the Faraday Efficiency and an increase in temperature a negative one. Figure 2-4: Faraday Efficiency dependence on current density and temperature. Finally, it is studied the influence of the temperature, pressure and current density on the HTO. Figure 2-5 shows how the HTO varies with temperature for an operating pressure of 1.013 bar. An increase in current density reduces the HTO, and an increase in temperature increases it. Therefore, the HTO behaves conceptually equal to the Faraday Efficiency, an increase in the current density is beneficial and an increase in temperature detrimental. This parameter is particularly crucial for safety because the lower explosion limit (LEL) of a hydrogen-oxygen gas mixture occurs at around 4% (v/v) hydrogen in oxygen. Consequently, most water electrolyzers initiate a safety shutdown when they reach 50% of this lower explosion limit. (21). Figure 2-5: HTO dependence on current density and temperature for P=1,013 bar. Figure 2-6 shows the dependence of HTO on pressure. At low current densities HTO is highly influenced by pressure, being lower while higher the pressure. However, for current densities approaching 0.7 A/cm2 and higher the effect of pressure becomes negligible. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Faraday Efficiency (%) Current density (A/cm2) Faraday efficiency (%) Top=10°C Top=40°C Top=70°C Top=100°C 0.00% 0.50% 1.00% 1.50% 2.00% 2.50% 3.00% 3.50% 4.00% 0.2 0.3 0.4 0.5 0.6 0.7 0.8 HTO (%, v/v) Current density (A/cm2) HTO (%) P=1.013 bar T=55°C T=70°C T=85°C T=100°C 55 Stationary simulation of an alkaline electrochemical plant in DWSIM 55 Figure 2-6: HTO dependence on pressure for a temperature of 85 °C. Higher pressures were not used to obtain the experimental model and, therefore, the expression is only applicable up to 10 bar. Other coefficients should be obtained for each specific electrolyzer technology. Table 2-2: Effect of current density, temperature and pressure on the main electrochemical parameters. Parameter Current Density Temperature Pressure Cell’s Voltage Detrimental Beneficial Not considered Faraday Efficiency Beneficial Detrimental Not considered HTO Beneficial Detrimental Beneficial Table 2-2 shows the effect of current density, temperature and pressure on the main parameters of the electrochemical model. There is an opposite contribution of current density and temperature on the cell’s voltage (related to the voltage efficiency) and the Faraday’s voltage. Therefore, the effect of current density and temperature on the total efficiency of the stack is not clear. It is going to be deeply explored with the DWSIM custom model. In addition, it must also be studied not only the stack efficiency but the BoP system’s one. 0.00% 0.50% 1.00% 1.50% 2.00% 2.50% 3.00% 0.2 0.3 0.4 0.5 0.6 0.7 0.8 HTO (%, v/v) Current Density (A/cm2) HTO (%) T = 85°C P=10 bar P=1,013 bar Electrolysis simulation 56 56 2.1.2 Material and energy balances Theoretical hydrogen produced by an electrolysis system is determined by Faraday's law, which relates the number of product moles to the current intensity associated with the electrochemical process. However, the actual hydrogen production rate is often lower than the theoretical, or in other words, the electrical power required for the same hydrogen production is usually higher due mainly to parasitic currents and compound recombination within the electrochemical cells, defined through Faradaic efficiency, which has been modelled in Equation 18. Therefore, the hydrogen produced can be calculated by Equation 21, which is the product of the theoretical hydrogen mole flow given by Equation 12 and the Faraday efficiency. 𝑛𝐻2⁡𝑐𝑎𝑡ℎ𝑜𝑑𝑒 =𝜂𝐹𝐼 𝑧𝐹𝑛𝑐𝑒𝑙𝑙 Equation 21 However, the total hydrogen that has reacted is not the one obtained in the cathode, but it must be added the hydrogen that leaves through the anode, given by the HTO (Equation 19). Total hydrogen flow is therefore given by Equation 22. 𝑛𝐻2⁡ =𝑛𝐻2⁡𝑐𝑎𝑡ℎ𝑜𝑑𝑒 +⁡𝑛𝐻2,𝑎𝑛𝑜𝑑𝑒 Equation 22 On another hand, oxygen production is directly related to the hydrogen mole flow by stoichiometry, as shown in Equation 23. 𝑛𝑂2 =1 2𝑛𝐻2 Equation 23 Finally, water consumption is also obtained by stoichiometry, as shown in Equation 24. 𝑛𝐻2𝑂⁡𝑒𝑠𝑡𝑞 =𝑛𝐻2 Equation 24 However, the water fed to the electrolysis stack is not the consumed one, but there is a huge recirculation of water to manage the heat produced. The recirculated water is obtained from the energy balance, to limit the outlet temperature to the specified value. 𝑛𝐻2𝑂⁡𝑠𝑡𝑎𝑐𝑘 =𝑛𝐻2𝑂⁡𝑒𝑠𝑡𝑞 +𝑛𝐻2𝑂⁡𝑟𝑒𝑐𝑦𝑐𝑙𝑒 Equation 25 The total anode stream will be the sum of the oxygen produced, the hydrogen diffused (HTO) and the recirculated water that leaves through the anode. It is assumed that half of the recirculated water exits through the anode and the other half through the cathode. Therefore, the total anode mole flow can be calculated by Equation 26. On another hand, the cathode mole flow would be the sum of the produced hydrogen and the recirculated water, as shown in Equation 27. 𝑛𝑎𝑛𝑜𝑑𝑒 =𝑛𝑂2+𝑛𝐻2,𝑎𝑛𝑜𝑑𝑒+𝑛𝐻2𝑂⁡𝑟𝑒𝑐𝑦𝑐𝑙𝑒 2 Equation 26 𝑛𝑐𝑎𝑡ℎ𝑜𝑑𝑒 =𝑛𝐻2⁡𝑐𝑎𝑡ℎ𝑜𝑑𝑒 +𝑛𝐻2𝑂⁡𝑟𝑒𝑐𝑦𝑐𝑙𝑒 2 Equation 27 In addition, the model also gives the inlet water to the BoP, which is equal to the stoichiometric one plus the water that leaves with the hydrogen and oxygen streams in saturated conditions. This quantity depends on the operating conditions, but it is usually about 5-10% of the stoichiometric water. 57 Stationary simulation of an alkaline electrochemical plant in DWSIM 57 As explained in the section 1.3.2.1 Thermodynamics of electrolysis, when the applied voltage is higher than the thermoneutral one, the electrolysis process comes with a heat generation, despite it being an endothermic reaction. Generally, commercial electrolyzers work at a voltage considerably higher than the thermoneutral voltage, therefore, a heat given by Equation 28 will be produced. 𝑄𝑔𝑒𝑛 =𝑛𝑐𝑒𝑙𝑙(𝑉𝑐𝑒𝑙𝑙 −𝑉𝑡𝑛)·𝐼 Equation 28 Part of the heat generated will be lost through the stack walls (𝑄𝑙𝑜𝑠𝑠), and another part will be transferred to the stack’s electrolyte, making temperature increase (𝑄𝑒𝑥𝑐𝑒𝑠𝑠). Therefore, the recirculated electrolyte must be refrigerated before being introduced into the stack again. Heat management is very important to avoid damaging the equipment due to temperature. 𝑄𝑔𝑒𝑛 =𝑄𝑙𝑜𝑠𝑠 +𝑄𝑒𝑥𝑐𝑒𝑠𝑠 Equation 29 Heat losses are supposed to be a 10% of all the heat generated, and by Equation 30 inlet temperature 𝑇𝑖𝑛, outlet temperature 𝑇𝑜𝑢𝑡, recirculated water 𝑛𝐻2𝑂⁡𝑟𝑒𝑐𝑦𝑐𝑙𝑒and 𝑄𝑒𝑥𝑐𝑒𝑠𝑠 can be related, where 𝑐𝑝𝐻2𝑂 (kJ/mol°C) has been assumed to be the specific heat of water (neglecting the effects of the electrolyte). 𝑇𝑜𝑢𝑡 =𝑇𝑖𝑛+𝑄𝑒𝑥𝑐𝑒𝑠𝑠 𝑛𝐻2𝑂⁡𝑟𝑒𝑐𝑦𝑐𝑙𝑒𝑐𝑝𝐻2𝑂 Equation 30 The operating temperature used in the electrochemical model has been taken as the mean of the inlet and outlet temperature. And, finally, pressure lost in the stack has been assumed to be 0.3 bar. The code of the custom alkaline electrolysis model can be found Anex A: Code for the Custom Electrochemical Model. 2.1.3 Sensitivity Analysis of the Stack In the present section a sensitivity analysis of the main parameters affecting the electrolysis stack performance is done. The main objective is to understand how the different variables relate to each other, which ones have a greater effect on the operation of the electrolyzer, and how they can be optimized. In order to evaluate the performance of the stack a series of KPIs have been defined, namely hydrogen production, efficiencies, inlet water to stack and generated heat. Table 2-3 shows the inlet parameters and chosen KPIs. The influence these parameters may have in the electrolyzer performance are discussed hereunder. First, hydrogen production is the main objective of the electrolyzer and the primary source of revenue. Oxygen may also be sold to increase revenues, but in practice, few projects secure a buyer, and it ends up being vented to the atmosphere. In addition, production of oxygen is directly proportional to production of hydrogen by stoichiometry, so it has not much interest to be analysed. On another hand, faraday efficiency, voltage efficiency and global efficiencies are analysed to understand the contribution of the inlet parameters to the different types of losses. Finally, inlet water affects the dimension of the equipment and therefore CAPEX, as the larger the water flow, the bigger the equipment needs to be. In addition, it will also affect the OPEX of the BoP, as more energy would be needed for the pumps and refrigerating system. Inlet water is directly related to the generated heat, so it has been decided to be studied as well to gain a better understanding of the process. Table 2-3: Main Inlet parameters and KPIs Inlet parameters KPIs Description Variable Description Variable Number of cells Ncell H2 production (kg/h) WH2 cathode Electrolysis simulation 64 64 Figure 2-14: Effect of inlet temperature on generated heat. 2.1.3.3 Active Area The active area has been varied between 0.7-1.3 m2 to study how it influences the main KPIs. Figure 2-15 shows the evolution of hydrogen production with respect to the active area and Figure 2-16 the efficiencies. Again, mathematical relationship between the active area and hydrogen production is not direct, as current density depends on the active area according to Equation 33, and therefore Faraday efficiency and the cell’s voltage also do. At the end, it happens an equivalent situation as with the number of cells: an increase in the active area produces a decrease in current density, and therefore a decrease in the Faraday efficiency and increase in the voltage efficiency. In the range studied voltage efficiency is dominant in the global efficiency and, therefore, the hydrogen production increases with the active area, until it reaches a maximum of 17.50 kg/h in 1.32 m2. 65 Stationary simulation of an alkaline electrochemical plant in DWSIM 65 Figure 2-15: Effect of active area on hydrogen production. Figure 2-16: Effect of active area on efficiencies. On another hand, inlet water to stack decreases with the active area due to the decrease in the generated heat, as it happened with the number of cells. This effect is more significant than the increase in consumption due to stoichiometry. Electrolysis simulation 66 66 Figure 2-17: Effect of active area in inlet water to stack. Finally, Figure 2-18 shows the evolution of the generated heat with the active area. As it was stated before, an increase in the active area produces a decrease in the current density, and the consequent decrease in Joule’s losses and cells’ voltage. Therefore, the difference between the cell’s voltage and thermoneutral voltage also decreases, generating less heat. Figure 2-18: Effect of active area on the generated heat. 2.1.3.4 Pressure In order to study the effect of pressure in the main KPIs a sensitivity analysis varying pressure between 4.9 bar and 9.1 bar has been done. As the model only considers the influence of pressure in the HTO, all KPIs stay 67 Stationary simulation of an alkaline electrochemical plant in DWSIM 67 constant with pressure. In reality, an increase in pressure slightly raises the reversible voltage needed for hydrogen production, but the influence of this parameter is much smaller than that of temperature, so it is often neglected.(9) 2.1.3.5 Outlet temperature To conclude the sensibility analysis, the influence of outlet temperature in the KPIs has been studied, varying it from 52.5 to 97.5. Figure 2-19 and Figure 2-20 show the evolution of hydrogen production and efficiencies with the outlet temperature. The effect of increasing the outlet temperature is the same as increasing the inlet temperature, as the electrochemical parameters are calculated at the mean. Therefore, voltage efficiency increases while Faraday efficiency decreases, gaining more importance the Faraday efficiency each time. A maximum of 17.37 kg/h can be found at an outlet temperature of 134 °C. Figure 2-19: Effect of the outlet temperature in the hydrogen production. Figure 2-20: Effect of the outlet temperature in efficiencies. Electrolysis simulation 68 68 Regarding inlet water to the stack, the higher the outlet temperature the less amount of water is needed for the heat management. Figure 2-21: Effect of outlet temperature in inlet water to stack. 2.1.3.6 Conclusion In the previous sections, the influence of each individual parameter in the electrolysis stack behaviour has been studied. As a conclusion, a comparison of the effects of each parameter in the selected KPIs is made. Figure 2-22 shows the percentual change in the KPIs when the parameters studied vary a -30% (low case) and a +30% (high case) from the base case. The aim of the sensitivity analysis is to identify which parameters affect the most the KPIs, to optimize the design and operation of the electrolyzer. As discussed above, the main variables affecting the profitability of the equipment are hydrogen production (or global efficiency) and inlet water to stack. The other parameters studied serve purely academic purposes. Starting with hydrogen production, the parameters that most affect it are the number of cells and active area, practically in the same percentage. In the range of study, an increase in the number of cells and active area have led to an increase in hydrogen production, but, as it has been analysed, it exists a maximum, from which incrementing both parameters is detrimental for the global efficiency of the stack, due to the decrease in faraday efficiency. On another hand, temperatures and pressure have little effect in comparison to the previous parameters, but it can be concluded that temperature is beneficial for the voltage efficiency, as it reduces the electrical energy required, and pressure may be a bit detrimental, but is generally neglected. With regards to inlet water, the parameters that most affect it are inlet and outlet temperature, although what really matters is the difference between them. In this sense, the higher the difference the less the inlet water required, but the higher the outlet temperature, that has detrimental effects on the materials’ corrosion and lifespan of the stack if too high. Therefore, a compromise must be found between a sufficiently high temperature, which benefits the voltage efficiency of the electrolyzer and reduces the inlet water flow but does not negatively affect the lifespan. On another hand, active area and number of cells have a similar effect, both reducing inlet water when increased. The reason is that voltage efficiency increases, thus leading to a reduction in heat generated and less water needed to control the temperature increase (in the range studied). 69 Stationary simulation of an alkaline electrochemical plant in DWSIM 69 Figure 2-22: Percentual change in the KPIs when the main parameters vary a -30% (low case) and a +30% (high case) from the base case. 2.1.4 Optimization of the electrolysis stack An optimization of the electrolyzer stack has been carried out using the multivariable optimization tool offered in DWSIM. The optimization focused on maximizing overall efficiency (equivalent to maximizing hydrogen production for a constant power) by varying the active area and the number of cells in the stack. With regards to temperature, the higher the mean of the inlet and outlet temperature, the higher the efficiency. The outlet has been set at 100°C, and it has been decided to leave a 20°C difference within the inlet, so recirculated water is not excessive (the smaller the temperature difference, the more recirculation water is needed). Once the temperature has been increased, the active area has been optimized. Figure 2-23 shows the main parameters of the configuration window for the optimization. It was defined to run a maximization kind of optimization, using the Constrained Brent Resolution method. The independent variable, active area, was limited between 1 and 5. Global efficiency in the base case was 57.36%, and after the optimization of the active area it increased up to 58.32%. -12 0 -7 0 -2 0 3 0 8 0 cell in °C A m2 P bar out °C Hydrogen produc on High case o case -12 0 -7 0 -2 0 3 0 8 0 cell in °C A m2 P bar out °C araday E ciency High case o case -12 0 -7 0 -2 0 3 0 8 0 cell in °C A m2 P bar out °C oltage E ciency High case o case -12 0 -7 0 -2 0 3 0 8 0 cell in °C A m2 P bar out °C lobal E ciency High case o case -12 0 -7 0 -2 0 3 0 8 0 cell in °C A m2 P bar out °C Inlet Water to Stack High case o case -12 0 -7 0 -2 0 3 0 8 0 cell in °C A m2 P bar out °C enerated Heat High case o case Electrolysis simulation 70 70 Figure 2-23: Optimization of active area to maximize global efficiency. Next, the optimization of the number of cells was carried out with the same configuration and resolution method. In this case, the number of cells was constrained between 50 and 400. The result of the optimization was 193, which gives an efficiency of 58.43%. Figure 2-24: Optimization of number of cells to maximize global efficiency. A final iteration was conducted to optimize the area again, obtaining the previous result. Therefore, the 71 Stationary simulation of an alkaline electrochemical plant in DWSIM 71 optimization can be considered complete. Table 2-6: Inlet parameters and KPIs of the base case and after the optimization has been conducted. Inlet parameter Base case Optimization KPI Base case Optimization W (MW) 1 1 WH2 cathode (kg/h) 17.3438 17.6693 Ncell 168 193 𝞰𝑭⁡ (%) 94.1925 89.7701 Tin (°C) 50 80 𝞰𝑽 (%) 72.4614 77.4581 A (m2) 1 1.36129 𝜼𝒔𝒕𝒂𝒄𝒌 (%) 57.3571 58.4336 P (bar) 7 7 WH2O (kg/h) 8689.08 8890.48 Tout (°C) 75 100 Qgen (kW) 275.386 225.419 Results are also presented graphically in Figure 2-25, where variation from the base case of the inlet parameters can be observed, and Figure 2-26, representing the variations of the KPIs. Global efficiency has increased by 2%, by increasing a 7% de voltage efficiency, but decreasing the Faraday efficiency a 5%. On another hand, inlet water to stack has also increased by 2%, and generated heat has been reduced an 18%. Figure 2-25: Variation of inlet parameters with the optimization, in relation with the base case. Figure 2-26: Variation of KPIs with the optimization, in relation with the base case. 0% 10% 20% 30% 40% 50% 60% 70% Ncell Tin (°C) A (m2) P (bar) Tout (°C) Variation in inlet parameters after optimization -20% -15% -10% -5% 0% 5% 10% WH2 cathode (kg/h) f(%) v(%) stack(%) WH2O (kg/h) Qgen (kW) Variation in KPIs after optimization Electrolysis simulation 72 72 2.2 Electrolyzer BoP Simulation In the present section, the simulation of the BoP of the alkaline electrolyzer is explained. Figure 2-27 shows the Process Diagram of the simulation on the DWSIM’s interface. The custom model, developed in the previous section, is featured at the graph's centre. An alkaline electrolyzer schematic has been incorporated. The produced hydrogen and oxygen are carried as bubbles in the recirculating water to the separation tanks, exiting these vessels saturated with water. A condensate trap is modelled as a final stage to minimize the moisture content of both gases. The system's feed enters through the oxygen separation tank, where it is combined with the recirculated water. The water from both separation tanks is cooled and finally recirculated to the stack. Coolers give the energy that has to be removed in the process, and for calculating the required cooling water flowrate it has been developed another simple customized model called ‘CoolingWaterFlowRate’. The code for the customized model can be seen in Anex A: Code for the Custom Electrochemical Model. Figure 2-27: Electrolysis BoP Simulation Process Diagram in DWSIM. All BoP subsystems, except for the one previously mentioned CoolingWaterFlowRate, have been modelled using the various standard unit operations that DWSIM offers for process construction. By inputting appropriate system boundary conditions, individual component efficiencies, and required operating parameters, the software can perform mass and energy balances across all components, including thermodynamic data and physical properties of the species involved in the process. To conduct the simulation in DWSIM, the Peng-Robinson 1978 (PR78) Advanced thermodynamic method has been employed. Table 2-7: List of standard units of the BoP. TAG Unit type Description SEP-O2 Gas-Liquid Separator Tank Tank where the anode’s recirculated water is separated from the oxygen produced in the stack. It also receives the inlet water to the electrolyzer. SEP-H2 Gas-Liquid Tank where the cathode’s recirculated water is separated from 73 Stationary simulation of an alkaline electrochemical plant in DWSIM 73 Separator Tank the hydrogen produced in the stack. TRAP-O2 Steam Trap Steam trap to increase the purity of oxygen stream TRAP-H2 Steam Trap Steam trap to increase the purity of hydrogen stream PUMP-1 Pump Pump to increase the pressure of the inlet water to the operating pressure of the system. PUMP-2 Pump Pump to recover the pressure losses of the oxygen-side recirculated water. PUMP-3 Pump Pump to recover the pressure losses of the hydrogen-side recirculated water. COOLER-1 Cooler Cooler of the oxygen-side recirculated water. COOLER-2 Cooler Cooler of the hydrogen-side recirculated water. MIX Mixer Mixer of the oxygen-side and hydrogen-side recirculated water. The following section describes the process units, and the parameters defined for simulation. 2.2.1 Process Units simulation 2.2.1.1 Separator tanks As mentioned earlier, the system incorporates two separator tanks (SEP-O2 and SEP-H2), dedicated to each output stream from the stack. The primary purpose of these tanks is to isolate the gas generated within the stack from the accompanying electrolyte, which has been simulated as water. A flash separator unit is employed for the simulation of this separation process. Duty exchanged has been set to zero by not connecting any energy stream. Figure 2-28: SEP-O2 material balance with the base case stack. Figure 2-28 shows the material balance of the separator. Pressure and temperature in the tank are 6.7 bar and 73.17°C respectively. WATER_IN_SEP and O2_STACK enter the separator in liquid and two-phase, respectively, and are separated into a liquid stream (RH2O_O2) and a vapor stream (O2_OUT_SEP). It can be seen how, through the gas phase O2_OUT_SEP it exits oxygen with a purity of 98.19% v/v, the rest being mainly moisture. Electrolysis simulation 80 80 Figure 2-43: BoP electrical energy consumption in the optimized case. On another hand, the pump’s consumption can be neglected in comparison with the stack and cooling system consumption. With regards to the consumption in the base and optimized case, it can be observed that the optimized case not only has higher hydrogen production (what was optimized), but it also has lower electrical consumption of the BoP. This is due to the reduction in the generated heat with the increase in voltage efficiency, which reduces the power needed for the cooling system. Finally, global efficiency of the system is calculated according to Equation 15, where PBop refers to the power of the BoP excluding the stack. The base case has a global efficiency of 53.1%, which corresponds to 62.75 kWh/kg pure hydrogen. Meanwhile, the optimized case has an efficiency of 55.4%, corresponding to 60.21 kWh/kg. As a reference, reported system’s efficiencies of alkaline electrolyzers based on Table 1-4 are between 50–78 kWh/kg, so there is potential for improvement by changing parameters that are not taken into account in this model (such as materials of components of the stack or integration of components among others) which would retrieve different electrochemical model’s parameters. Table 2-8 presents the efficiencies obtained for the stack and the complete system for the two simulated scenarios. Table 2-8: Global efficiencies of stack and electrolysis system for the base case and optimized scenario. Base case Optimized scenario Global efficiency of the stack (%) 57.3 58.3 Global efficiency of the system (%) 53.1 55.4 81 Stationary simulation of an alkaline electrochemical plant in DWSIM 81 3 CONCLUSIONS In conclusion, this master's thesis successfully developed a steady-state simulation of an electrolysis plant using the DWSIM software, that serves as a digital twin of a specific model of alkaline electrolysis stack, or as tool to qualitatively analyse the influence of the main geometric and operating parameters in the performance of electrolysis. A custom model for the stack was created in Python, based on semi-empirical electrochemical equations, while the remainder of the Balance of Plant (BoP) was simulated using DWSIM's standard units. The tool allows for the analysis of the influence of key design and operational parameters—such as active area, number of cells, operating temperature, and pressure—on the system's performance, and effectively distinguishes between the performance of the stack and those of the BoP. As it is based on empirical equations, the results are only valid for the specific geometry of the model simulated, and therefore, the analysis made when changing the active area and number of cells must be interpreted qualitatively, and not quantitatively. This is because, when changing the geometry, the parameters describing the electrochemical behaviour will also change. The key findings from the stack-level analysis reveal that within the simulated range, the most influential parameters on stack efficiency are the active area and the number of cells. Both have an optimal value that maximizes efficiency. A useful way to assess overall efficiency is by examining Faraday efficiency and voltage efficiency. Faraday efficiency tends to decrease as the active area and the number of cells increase, whereas voltage efficiency rises with both. Prior to reaching the optimal levels for these parameters, voltage efficiency plays a larger role, meaning that increasing the active area and number of cells improves overall efficiency. This is evident in the baseline scenario discussed in this thesis. However, after exceeding the optimal point for active area and cell count, Faraday efficiency becomes more significant, and further increases in these parameters reduce overall efficiency. In addition, the decrease in Faraday efficiency carries with it the increase of the HTO, a critical parameter in terms of security of the plant, not being allowed to exceed 2%. For inlet water flow, the critical parameter is the temperature differential between inlet and outlet; a larger differential decreases water demand but risks compromising stack longevity due to elevated outlet temperatures. Thus, optimizing these variables requires balancing enhanced performance with the preservation of system integrity. From a broader perspective, the balance of plant (BoP) energy consumption accounts for roughly 10% of the total plant consumption, mainly due to the cooling requirements of the stack. The effect of the stack's global efficiency on BoP consumption depends on whether voltage or Faraday efficiency dominates. When voltage efficiency is predominant, higher global stack efficiency reduces heat generation, which lowers the need for cooling, as seen in the baseline scenario. On the other hand, when Faraday efficiency is the key factor, increasing global stack efficiency leads to greater cooling demands, having a smaller effect on overall system efficiency. The optimization conducted on the stack increased global efficiency of the stack by 2% and reduced generated heat by 18% by increasing the number of cells, active area and inlet and outlet temperature. From a wholesystem perspective, this resulted in an increase in the overall system efficiency from 53.1% in the base case to 55.4% in the optimized scenario. The increased in overall system efficiency was caused not only because the production of hydrogen in the stack increased, but also because the cooling needs decreased thanks to the decrease in generated heat (caused by an increase in voltage efficiency). Regarding limitations of the model, first, as the electrochemical model depends on experimental parameters, it cannot be used for designing-optimization purposes. Additionally, the simulation does not account for system dynamics, which are of utmost relevance in the analysis of electrochemical systems coupled with intermittent renewable energies. Finally, it incorporates some simplifications, such as neglecting the electrolyte in heat transmission, the effects of pressure on reversible voltage, or some BoP subsystems that may further decrease the efficiency of the system, namely the power-conversion unit or water treatment plant. Future work could focus on improving the model by incorporating dynamic behaviour, refining the physical and chemical assumptions or incorporating more units of the BoP. It could also be tested on a real electrolysis stack to analyse its accuracy, and address experimentally up to what extent the parameters obtained for a specific geometry could be used for others. In this sense, different electrolysis stacks could be fabricated varying the number of cells and active area, and the empirical parameters could be obtained for each one. By comparing the results attained, it could be determined the influence of the geometric parameters in the electrochemical model Conclusions 82 82 for the scale studied, and perhaps be able to use the model for designing-optimization purposes. Additionally, beyond its immediate application to electrolysis, the custom model of the stack offers potential for adaptation to other electrochemical systems. For instance, it could be used as a foundation for simulating electrochemical compressors, which share similar underlying principles. This technology is at much lower TRL levels compared to electrolysis, but it holds great potential in the emerging hydrogen economy. 83 Stationary simulation of an alkaline electrochemical plant in DWSIM 83 4 ANEX A: CODE FOR THE CUSTOM ELECTROCHEMICAL MODEL import clr import System as System from System import Array from DWSIM.Thermodynamics import * import math Water=ims1 in_comp=Water.GetOverallComposition() W_stack=ies1 W=W_stack.GetEnergyFlow() #kW T=(Tin+Tout)/2 #ºC Tin_K=Tin+273.15 #K Tout_K=Tout+273.15 #K #Electrochemical model------------------------------------------------------------------------------------------------------ #Reversible Voltage Vrev=f(T) Vrev=1.5184-1.5421E-3*(T+273.15)+9.523E-5*(T+273.15)*math.log(T+273.15)+9.84E-8*(T+273.15)**2 #V #Cell voltage and current density Vcell=f(Vrev,T,i); W=f(Vcell,i,A,Ncell) i=3000 #A/m2 error=1000 while error>10**(-7): Vcell=Vrev+(8.05*10**(-5)-2.5*10**(-7)*T)*i+0.185*math.log((1.002+8.424/T+247.3/(T**2))*i+1,10) #V Vstack=Vcell*Ncell #V Wcal=Vstack*i*A/1000 #kW error=(W-Wcal)**2 i=W*1000/(Vstack*A) #A/m2 #Faraday efficiency RendF=f(i, T) RendF=(1.0396-1.04e-3*T)*i**2/(4.79e5-2.95e3*T+i**2) RendF_100=RendF*100 Anex A: Code for the Custom Electrochemical Model 84 84 #HTO HTO=f(i,T,P) HTO=9.9E-02-2.07E-03*T+0.0000131064*T**2+(-8.48E-02+0.00179*T-1.13E- 05*T**2)*math.exp((1481.45-23.60345*T-0.25774*T**2)/i)+3.71417-0.93063*P+0.05817*P**2+(- 3.72068+0.93219*P-0.05826*P**2)*math.exp((-18.38215+5.87316*P-0.46425*P**2)/i) #Material Balance O2 and H2---------------------------------------------------------------------------------------- #O2 and H2 molar flows error=1000 nH2cath=RendF*Ncell*i*A/(2*96484) #mol/s nH2anod=0.01 while error>10**(-7): nH2=nH2cath+nH2anod nO2=nH2/2 #mol/s nH2anod_cal=HTO*nO2/(1-HTO) #mol/s error=(nH2anod_cal-nH2anod)**2 nH2anod= nH2anod_cal #Hydrogen and oxygen production WH2_cath=nH2cath*2/1000 #kg/s FH2_cath=WH2_cath*11.126 #Nm3/s WO2=nO2*32/1000 #kg/s FO2=WO2/1.4291 #Nm3/s #Energy balance----------------------------------------------------------------------------------------------------------- Vtn=1.48 Qgen=Ncell*(Vcell-Vtn)*i*A #W Qgen_kW=Qgen/1000 Qloss=0.1*Qgen #W Qloss_kW=Qloss/1000 Qexcess=Qgen-Qloss #W DP=0.3 Pout=P-DP #Material Balance H2O----------------------------------------------------------------------------------------------------- #Water esteq nH2O_estq=nH2 #mol/s 85 Stationary simulation of an alkaline electrochemical plant in DWSIM 85 #Recycling water cpH2O=75.348 #J/molK nH2O_recycle=Qexcess/(cpH2O*(Tout-Tin)) #mol/s #Total water nH2O=nH2O_estq+nH2O_recycle #mol/s nH2O_anod=nH2O_recycle/2 nH2O_cath=nH2O_anod F_recycle=nH2O_recycle/nH2O_estq WH2O_estq=nH2O_estq*18.01528*3600/1000 #kg/h FH2O_estq=WH2O_estq/997.13 #m3/h WH2O=nH2O*18.01528*3600/1000 #kg/h FH2O=WH2O/997.13 #m3/h WH2O_recycle=nH2O_recycle*18.01528*3600/1000 #kg/h FH2O_recycle=WH2O_recycle/997.13 #m3/h WH2O_cath=nH2O_cath*18.01528*3600/1000 #kg/h #Composition: Hydrogen, Water, Oxygen n_anod=nO2+nH2anod+nH2O_anod n_cath=nH2cath+nH2O_cath #Anode x_O2_anod=nO2/n_anod x_H2_anod=nH2anod/n_anod x_H2O_anod=nH2O_anod/n_anod comp_anod=[x_H2_anod,x_H2O_anod,x_O2_anod,0] comp_anod_array=Array[float](in_comp) for j in range(in_comp.Length): comp_anod_array[j] = comp_anod[j] #Cathode x_H2_cath=nH2cath/n_cath x_O2_cath=0 x_H2O_cath=nH2O_cath/n_cath comp_cath=[x_H2_cath,x_H2O_cath,x_O2_cath,0] comp_cath_array=Array[float](in_comp) for j in range(in_comp.Length): Anex A: Code for the Custom Electrochemical Model 86 86 comp_cath_array[j] = comp_cath[j] #Efficiencies RendV=1.48/Vcell*100 #Voltage yield % RendG=RendV*RendF #Global yield % Energy_consumption_W=W/WH2_cath/3600 #kWh/kgH2 Energy_consumption_F=W/FH2_cath/3600 #kWh/Nm3H2 Global_efficiency=(33.33333*WH2_cath*3600/W)*100 #% #Assign results to streams------------------------------------------------------------------------------------------------ #Inlet Water to STACK Water.Clear() Water.SetPressure(P*10**5) Water.SetTemperature(Tin_K) Water.SetMolarFlow(nH2O) Water.SetOverallComposition(in_comp) #Inlet Water to Electrolyser Inlet_Water=oms3 Inlet_Water.SetMolarFlow(nH2O_estq*R_water_in_estq) #Hydrogen (Cathode) Cathode=oms1 Cathode.Clear() Cathode.SetPressure(Pout*10**5) Cathode.SetMolarFlow(n_cath) Cathode.SetOverallComposition(comp_cath_array) Cathode.SetTemperature(Tout_K) #Oxygen (Anode) Anode=oms2 Anode.Clear() Anode.SetPressure(Pout*10**5) Anode.SetMolarFlow(n_anod) Anode.SetOverallComposition(comp_anod_array) Anode.SetTemperature(Tout_K) 87 Stationary simulation of an alkaline electrochemical plant in DWSIM 87 #Qloss Qloss_stream=oes1 Qloss_stream.SetEnergy lo Qloss_kW Anex B: Code for the CoolingWaterFlowRate Custom model 88 88 5 ANEX B: CODE FOR THE COOLINGWATERFLOWRATE CUSTOM MODEL import clr import System as System from DWSIM.Thermodynamics import * Power=ies1 Cold_in=ims1 Cold_out=oms1 Q=Power.GetEnergyFlow() Composition=Cold_in.GetOverallComposition() #Temperatures T_in_cold=30+273.15 #K T_out_cold=40+273.15 #K #Pressures P_in=4*10**5 #MPa DP=0.1*10**5 #MPa P_out=P_in-DP #MPa #Mass Flow cp=4.18 #J/gK m_cold=Q/(cp*(T_out_cold-T_in_cold)) #kg/s #Set Properties of streams Cold_in.SetMassFlow(m_cold) Cold_in.SetTemperature(T_in_cold) Cold_in.SetPressure(P_in) Cold_out.SetOverallComposition(Composition) Cold_out.SetTemperature(T_out_cold) Cold_out.SetMassFlow(m_cold) Cold_out.SetPressure(P_out) 89 Stationary simulation of an alkaline electrochemical plant in DWSIM 89 6 ANEX C: MATERIAL AND ENERGY BALANCES OF THE ELECTROLYSIS PLANT FOR THE BASE AND OPTIMIZED CASE The following tables show the results of the material and energy balances of the entire electrolysis plant for the base and optimized case. Table 6-1: Energy consumption of the base case electrolysis stack and BoP. Object Energy Flow Units W_STACK 1000.00 kWe W_PUMP-3 0.10 kWe W_PUMP-1 0.04 kWe Q_COOLER-2 157.33 kWth Q_COOLER-1 151.70 kWth Table 6-2: Energy consumption of the optimized stack electrolysis stack and BoP. Object Energy Flow Units W_STACK 1000.00 kWe W_PUMP-3 0.10 kWe W_PUMP-1 0.04 kWe Q_COOLER-2 129.43 kWth Q_COOLER-1 115.26 kWth References 96 96 21. Brauns J, Turek T. Experimental evaluation of dynamic operating concepts for alkaline water electrolyzers powered by renewable energy. Electrochim Acta. 2022 Feb 1;404.