Diagnosis and Definition of Performance Indicators for the Efficiency of the Steam Generation Process in a Biomass Boiler

Available online at www.rajournals.in RA JOURNAL OF APPLIED RESEARCH ISSN: 2394-6709 DOI:10.47191/rajar/v11i11.15 Volume: 11 Issue: 11 November 2025 International Open Access Impact Factor8.553 Page no.- 1073-1079 1073 C. García-Lopez1, RAJAR Volume 11 Issue 11 November 2025 Diagnosis and Definition of Performance Indicators for the Efficiency of the Steam Generation Process in a Biomass Boiler C. García-Lopez1*, J.A. Romero-Guerrero2 1Posgrado CIATEQ, A.C. Av. del Retablo 150 Col. Constituyentes Fovissste, Querétaro, Qro. C.P. 76150. https://orcid.org/0009-0007-5493-7439 2Departamento Manufactura Virtual y Lean y Cad Cae, CIATEQ A. C., DESCTI, 42163 San Agustín Tlaxiaca, Hgo, México. https://orcid.org/0000-0003-0431-1917 ARTICLE INFO ABSTRACT Published Online: 29 November 2025 Corresponding Author: C. García-Lopez This article proposes a diagnostic approach to evaluate the efficiency of a biomass boiler steam generation process, specifically using sawdust as fuel, based on performance indicators. Four Key Performance Indicators (KPIs) are addressed: thermal efficiency, particulate matter emissions (PME), technical availability, and unscheduled downtime. The measurement methods, critical process parameters, and applicable standards are detailed. Finally, performance indicators are calculated using the information obtained from the research carried out to define a baseline. KEYWORDS: Biomass boiler, sawdust, thermal efficiency, technical availability, particulate matter emissions. I. INTRODUCTION Steam generation from biomass represents a sustainable alternative with a lower carbon footprint compared to fossil fuels. The use of forest residues as a source of biomass for bioenergy generation is a potential alternative, as it produces a less polluting biofuel compared to fossil fuels (AyalaMendivil & Sandoval, 2018). Boilers that use sawdust as fuel are common in industries with wood by-products. However, to maximize their economic and environmental contribution, it is important to monitor and manage their performance actively. Failure to manage critical points in a process can lead to inefficient operations, high maintenance costs, and a suboptimal environmental impact (Tene et al., 2023). This study presents a systematic approach to addressing these challenges by defining a baseline based on performance indicators for the proper operation of biomass boilers. The plant under study is dedicated to the manufacture of hand tools, most of which contain at least some wooden component, resulting in considerable waste from the manufacturing processes. This waste is used to fuel two biomass boilers. The steam generated by the boilers is used for the drying process, which consists of drying the wood in ovens to remove moisture in a controlled and rapid manner. The current steam demand for this process is 15,400 kg/h. The main boiler has a nominal capacity of 9,790 kg/h and is operating at 40% of its nominal capacity, producing 3,916 kg/h. The main problems encountered with the boiler are as follows: • Limited fuel supply • Poor fuel quality • Lack of control in unscheduled shutdowns • Lack of maintenance management II. METHODS Based on the problems and the current operation of the equipment, four KPIs are proposed and defined for the evaluation of boiler performance depending on the criteria. A. Thermal efficiency Represents the fraction of the energy contained in the fuel that is transferred to the steam generated. It is calculated using direct methods (energy balance between steam and fuel) or indirect methods (quantification of losses). Heat losses through the boiler body always occur by conduction, convection, and radiation (IDAE, 2007). The efficiency of an installation can be obtained from the energy balance, considering the energy used and the energy delivered: η=E_aprov/E_ent *100% Equation 1 Where: E_aprov: energy used by the working fluid. E_ent: energy delivered to the system. “Diagnosis and Definition of Performance Indicators for the Efficiency of the Steam Generation Process in a Biomass Boiler” 1074 C. García-Lopez1, RAJAR Volume 11 Issue 11 November 2025 In this method, the energy supplied to the system is considered to be the energy released by the fuel (based on the lower heating value of the fuel) and the energy credits entering the system (sensible heat from the air, water, and fuel). The choice to use the lower heating value instead of the higher heating value is because, in the tested boilers, the water escapes through the chimney in a gaseous state without releasing its heat of condensation (Golato, 2008). The direct method considers the steam generated, the temperature at the boiler inlet and outlet, as well as the fuel consumption and calorific value. Η=Q(H-h)/(q*GCV)*100% Equation 2 Where: Q= Amount of steam generated H= Enthalpy of steam h= Enthalpy of water q= Fuel consumption GCV= Fuel calorific value B. Technical availability This is the percentage of total time the boiler is available to operate, excluding planned shutdowns. The variables to be measured are total operating time and unplanned downtime (duration and cause). To calculate availability, a historical record of failures, operating times, and repair times is required. MTBF and MTTR values are used for analysis based on average operating times and average repair times (Reyes, Linzan, & Ramos, 2021). The availability is defined by the following equation: Availability=MTBF/((MTBF+MTRR)) Equation 3 Where: MTBF: Mean time between failures MTTR= Mean time between repairs C. Unplanned downtime These are unexpected interruptions in a process or equipment that halt production or service. They are caused by unforeseen failures, such as machinery breakdowns, operational errors, or inventory problems, and result in financial losses, delivery delays, and safety risks. This indicator is defined as equipment downtime and can have the following causes: • Equipment failure: Mechanical, electrical, or component failures. • Operational errors: Human errors or operator mistakes. • Inadequate maintenance: Lack of preventive maintenance or a poor maintenance strategy. • Management problems: Poor planning, lack of communication, or inventory problems. • External factors: Supply chain disruptions or unforeseen events. These stops must be recorded and reported by the operator. According to Carrasco (2016), the operator's involvement is fundamental, as they are responsible for product quality and operational reliability. D. Particulate matter emissions (PME) This performance indicator helps us manage the percentage reduction of particulate matter (PM) and other pollutants relative to a baseline. PM is a visible mixture of small particles and gases in the air, generated by combustion. It is a significant pollutant in boilers that must be controlled. NOM-043-SEMARNAT-1993 establishes maximum permissible limits for the concentration of particulate matter (suspended particles), and NOM-085-SEMARNAT-2011 establishes limits for pollutants such as carbon monoxide (CO) and nitrogen oxides (NOx) from indirect heating combustion equipment that uses conventional fuels or their mixtures, to protect air quality. NOM-085-SEMARNAT2011 does not apply in the following cases: • Equipment with a nominal thermal capacity less than 530 MJ per hour (≈15 CC). • Domestic heating and water heating equipment, gas turbines, auxiliary equipment, and backup equipment. • Equipment that uses bioenergy. Bioenergy is a fuel obtained from biomass derived from organic matter from industrial activities (NOM-085-SEMARNAT2011). For biomass boilers, NOM-043-SEMARNAT-1993 applies, and NOM-085-SEMARNAT-2011 is taken only as a reference. The maximum permissible limits for PM and main pollutants are shown in Table 2. Table 2. Maximum permissible emission levels for new equipment. Source: NOM-085-SEMARNAT-2011.. III. RESULTS The sawdust used for boiler operation comes from three different warehouses (A, B, and C), which are processed according to the daily production and operation of those Month Sawdust production (kg) Jan 19,840 Feb 265,600 Mar 236,800 Apr 241,600 May 241,600 Jun 231,680 Jul 301,120 Aug 327,360 Sep 322,880 Oct 282,880 Grand Total 2,471,360 “Diagnosis and Definition of Performance Indicators for the Efficiency of the Steam Generation Process in a Biomass Boiler” 1075 C. García-Lopez1, RAJAR Volume 11 Issue 11 November 2025 warehouses. Total production was estimated at 2,471,360 kg from January to October, as shown in Table 3 and Figure 1. Table 3. Sawdust production in the period JanuaryOctober 2025 Source: own elaboration. Figure 1. Monthly sawdust production. Prepared by the author. Table 4 and Figure 2 show the production by warehouse, indicating that warehouse B generates the most sawdust, with the other two serving only as support. Table 4. Biomass production per warehouse. Warehouse Sawdust production (kg) A 54,080 B 2,257,280 C 160,000 Total 2,471,360 Figure 2. Sawdust production per warehouse. Prepared by the author. Production per shift is shown in Table 5 and Figure 3. There are four shifts, of which shifts 1, 2, and 3 generate a similar amount of sawdust. The fourth shift maintains a low level, as it is a shift where wood product production is low, and it is the shift where only orders that were not completed during the week are completed. Table 5. Biomass production per warehouse. Shift Sawdust production(kg) 1 829,440 2 752,640 3 847,040 4 42,240 Total 2,471,360 Source: own elaboration. Figure 3. Sawdust production per shift. Prepared by the author. The plant has four boilers, two fueled by gas and two by biomass. The boiler to be analyzed is manufactured by HURST and is a water-tube boiler with a reciprocating grate for burning plant biomass (wood shavings, sawdust, wood chips), with the specifications shown in Table 6. Table 6. Technical specifications of biomass boiler. Source: own elaboration. This boiler is operating at 40% of its nominal capacity due to a limited fuel supply, generating 3,916 kg/hr of steam. The second biomass boiler produces 3,130 kg/hr operating at its nominal capacity, and the gas boilers produce 4,102 kg/hr each. The average sawdust consumption is 535 kg/hr, which depends on the steam demand required for the kiln drying Month Sawdust production (kg) Jan 19,840 Feb 265,600 Mar 236,800 Apr 241,600 May 241,600 Jun 231,680 Jul 301,120 Aug 327,360 Sep 322,880 Oct 282,880 Grand Total 2,471,360 Parameter 100% 75% Total consumption (kg/hr) 15,400 11,550 Theoretical installed capacity (kg/hr) 21,910 16,432 Actual installed capacity (kg/hr) 11,558 1,000,000 800000 600000 400000 200000 829440 752640 847040 42240 1 2 3 4 “Diagnosis and Definition of Performance Indicators for the Efficiency of the Steam Generation Process in a Biomass Boiler” 1076 C. García-Lopez1, RAJAR Volume 11 Issue 11 November 2025 process. According to the technical specifications, each kiln consumes 700 kg/hr of steam. With a total of 22 kilns operating in the plant, requiring a total of 15,400 kg/hr. Table 7 shows a summary of the consumption and capacities of the installed equipment in two possible scenarios, with 100% and 75% usage. Table 7. Equipment consumption and capacity. Source: own elaboration. A. Thermal efficiency The following considerations are taken into account for calculating thermal efficiency: • The direct method is used, considering steam generation as known data obtained from the control panel • The average value of fuel consumption per hour is taken • Only dry wood is considered • A lower heating value (PCI) of 4,539 kcal/kg is considered (Table 8) • The water temperature at the boiler inlet is 70 ºC • The temperature of the saturated steam at the boiler • outlet is 130 ºC Table 8. Average PCI and PCS values of the main fuels. Source: IDAE, 2007. Based on the above information, the required variables for calculating thermal efficiency are obtained, yielding a thermal efficiency of 89.74% (Table 9). Table 9. Calculation of thermal efficiency. Source: own elaboration. B. Technical Availability To determine equipment availability, two essential data points are required: the mean time between failures (MTBF) and the mean time to repair (MTTR). This data can be obtained using process management software such as SAP, specifically its "Plant Maintenance" module. Table 10 shows all boiler failures in 2024 and 2025, including the start date, start time, end date, and end time for each maintenance order, enabling calculation of the days between failures and repair times. Table 10. Boiler failures in 2024 and 2025. Source: own elaboration. Equipment Failure description Order start date Start time of order End date of order End time of order Days between failures Repairs hours 1-CAL-09 MC - INT. REPARACIÓN DE MOTOR 10HP #367 04/06/2024 10:30:00 04/06/2024 21:30:00 0 11 1-CAL-09 MS - CAMBIO DE SENSOR DE OXIGENO 28/08/2024 12:00:00 28/08/2024 22:00:00 84 10 1-CAL-09 MP - 1-CAL-09 - MENSUAL 10/10/2024 22:30:00 11/10/2024 2:30:00 32 4 1-CAL-09 MS - INSTALACION DE MEDIDORES DE AGUA 29/10/2024 13:00:00 29/10/2024 17:00:00 19 2 1-CAL-09 CVOXPVO - 6026711 TUBO CONDUIT DE PARO D 21/11/2024 17:00:00 21/11/2024 20:00:00 22 3 1-CAL-09 OR - MONITOREO Y OPERACIÓN DE EQUIPO 01.10.2024 6:00:00 01.11.2024 2:00:00 1-CAL-09 CVOXPVO - 5932830 CAMBIO DE TORNILLO DE 27/12/2024 9:30:00 27/12/2024 19:35:00 0 10 1-CAL-09 CVOXPVO - 5932830 CORREGIR FUGA EN TUBER 27/12/2024 9:40:00 27/12/2024 19:45:00 0 10 1-CAL-09 CVOXPVO - 6026711 FALTA TENSOR EN CADEN 27/12/2024 15:10:00 27/12/2024 17:10:00 0 2 1-CAL-09 OR - MONITOREO Y OPERACIÓN DE EQUIPO 01.05.2024 1:30:00 31.05.2024 23:59:00 1-CAL-09 CVOXPVO - 5946562 CALIBRAR SENSOR DE OXI 28/12/2024 9:00:00 28/12/2024 11:00:00 1 2 1-CAL-09 CVOXPVO - 5946562 REPARACION DE CAMISA D 28/12/2024 16:30:00 28/12/2024 18:40:00 0 2,16 1-CAL-09 CVOXPVO - 6039748 TUBO CONDUIT DE PARO D 28/12/2024 8:20:00 28/12/2024 18:25:00 0 10 1-CAL-09 MS - REVISAR SENSOR DE AGUA 30/03/2025 11:00:00 30/03/2025 12:00:00 92 1 1-CAL-09 CVOXPVO - 6098821 CAMBIO DE SENSOR DE TE 09/04/2025 10:24:22 09/04/2025 12:00:00 39 1,5 1-CAL-09 CVOXPVO - 6098821 CAMBIO DE SENOSR DE ME 16/05/2025 6:00:03 16/05/2025 10:00:00 37 4 1-CAL-09 MP - 1-CAL-09 - MENSUAL 0:00:00 0:00:00 1-CAL-09 MS-FABRICACION DE TOLVA CAL HURTS 05/06/2025 12:10:00 30/06/2025 14:00:00 19 1,84 1-CAL-09 Sensores Metering 20/06/2025 11:00:00 20/06/2025 11:30:00 15 0,5 1-CAL-09 MC - 6146690 CAMBIO DE SENSORES METERING 24/06/2025 12:09:28 24/06/2025 11:10:00 4 1 1-CAL-09 MS-INSTALACION DE TOLVA CAL HURTS 01/07/2025 17:30:00 17/07/2025 13:30:00 7 40 1-CAL-09 CVOXPVO - 6098821 CAMBIO DE SENSOR DE OX 10/07/2025 9:00:00 10/07/2025 10:30:00 9 1,5 1-CAL-09 MPD 1-CAL-09 OTT 293 03.07.2025 14:49:24 07.07.2025 0:00:00 1-CAL-09 MS-FABRICACION DE TOLVA CAL HURTS 17/07/2025 18:00:00 18/07/2025 16:00:00 7 22 1-CAL-09 Falla en sensores 25/07/2025 8:00:00 25/07/2025 12:00:00 12 4 1-CAL-09 Falla en PLC 25/07/2025 9:29:48 25/07/2025 12:00:00 0 1,5 1-CAL-09 CAMBIO DE SENSOR DE METERING JAM DAÑADO (RESERVA) 01/08/2025 14:00:00 01/08/2025 15:30:00 6 1,5 1-CAL-09 CVOXPVO - 6168268 ACTV/REPARACIÓN EN CHA 11/08/2025 11:00:00 12/08/2025 1:00:00 10 2 1-CAL-09 CVOXRL - 6205091 CAMBIO DE ACEITE EN DEP 17/10/2025 12:00:00 17/10/2025 14:00:00 6 2 17,54 6,02 Total Specification Value Nominal capacity 5,060,000 kcal/h (600 B.H.P) Max. saturated steam generated 9,790 kg/h Total boiler efficiency 84% minimum Fuel Density PCI PCS kg/m3 kcal/ kg k Wh/kg t e/kg MJ /kg MJ/k g Anthr acite 875 8,19 4 9.5 3 8 .19 34. 30 34.70 Dry wood - 4,53 9 5.2 8 4 .54 19. 00 - Wet wood - 3,44 4.0 0 3 .44 14. 40 - Parameter Variable Value Steam generated (kg/hr) Q 3756 Enthalpy of vapor (KJ/kg) at 130 ºC H 2720.5 0 Enthalpy of water (KJ/kg) at 70 ºC h 292.98 Fuel consumption (kg/hr) q 535 Calorific value of the fuel (kcal/kg) GCV 4539 boiler efficiency % Ƞ 89.74 “Diagnosis and Definition of Performance Indicators for the Efficiency of the Steam Generation Process in a Biomass Boiler” 1077 C. García-Lopez1, RAJAR Volume 11 Issue 11 November 2025 C. Unplanned downtime Unplanned downtime must be recorded and managed to identify and mitigate its potential causes. This is done through a logbook, either physical or digital, recording the date, reason for the downtime, and the equipment downtime. Table 11 and Figure 6 show the unscheduled downtime for the last few months, totaling 79 hours. Table 11. Downtime per month. Source: own elaboration. Figure 6. Time off per month. Source: Prepared by the author. The main incidents (Table 12) are due to power outages and the repair of scrapers (discharge mechanism). Power outages depend on an external supplier, and although they cannot be controlled, a response protocol can be developed for when this problem occurs. Table 12. Boiler incidents. Source: own elaboration. Figure 7. Incidents in biomass boiler. Source: Prepared by the author. Table 13 shows that the third shift is where the greatest impact of downtime occurs, with 38.5 hours. This is due to various factors, such as a lack of personnel and supervision during this shift. Table 13. Boiler incidents. Shift Downtime (hrs) 1 19 2 21.5 3 38.5 Total 79 Source: own elaboration. Figure 8. Incidents in biomass boiler per shift. Source: Prepared by the author. D. Environmental performance To measure the energy performance of a steam boiler, pollutant emissions such as total suspended particles, carbon monoxide, and nitrogen oxides are measured. If the equipment and trained personnel to perform these measurements are unavailable, it is necessary to hire an external provider. Month Downtime (hrs) Apr 19 May 46.5 Jul 10 Aug 3.5 Total 79 Incidents Time stoppage (hrs) % Accumulated CFE power outage strike 36 22% Repair of squeegees 14.5 48% The boiler shut down due to a lack of sawdust to feed 13 70% Safety stops are not released for start-up 4 74% Stoppage due to filter change 3.5 83% Out of service due to a blockage in the ducts 3 87% The boiler is out of service for not meeting conditions safe food 3 96% Stoppage for area cleaning 2 100% Total 79 “Diagnosis and Definition of Performance Indicators for the Efficiency of the Steam Generation Process in a Biomass Boiler” 1078 C. García-Lopez1, RAJAR Volume 11 Issue 11 November 2025 Table 14 shows the parameters, methods, and standards used by Servicios de Consultoría y Verificación Ambiental, SA de CV (SECOVAM). Table 14. Identification of equipment, evaluation dates, evaluation method, and standards with which it is compared. Source: SECOVAM, 2025. The results of the evaluation carried out on the two biomass boilers are shown in Table 15. For the concentration of suspended particles, the comparison with the standard was made using the average of both runs. Table 15. Results of total suspended particles assessment. Source: SECOVAM, 2025. CO and NOx measurements are evaluated for reference purposes only, as the equipment uses non-conventional fuel (Table 16). Table 16. Results of the evaluation of CO and NOx assessment in biomass boilers. Source: SECOVAM, 2025. V. CONCLUSIONS This diagnostic guide calculating and manages KPIs in sawdust-fired biomass boilers. By implementing this diagnosis, plants can: 1. Establish a robust baseline: Quantify current performance for future comparisons. 2. Identify areas for improvement: Through continuous deviation analysis and root cause analysis. 3. Ensure regulatory compliance: Guarantee operation within emission and safety limits. The benchmark for generating improvements is based on the constant comparison of calculated KPIs with the objectives predefined by management. Any negative deviation triggers the process and implementation of corrective and preventive actions, ensuring continuous progress toward operational excellence and sustainability. REFERENCES 1. Ayala-Mendivil, N., & Sandoval, G. (2018). Bioenergía a partir de residuos forestales y de madera. Madera y bosques, 24(SPE). 2. Cárcel Carrasco, F.J. (2016). Características de los sistemas TPM y RCM en la ingeniería del mantenimiento. 3C Tecnología: glosas de innovación aplicadas a la pyme, 5(3), 68-75. DOI: <http://dx.doi.org/10.17993/3ctecno.2016.v5n3e19. 68-75/>. 3. Golato, M. A., Franck Colombres, F. J., Aso, G., Correa, C. A., & Paz, D. (2008). Metodología de cálculo de la eficiencia térmica de generadores de vapor. Revista industrial y agrícola de Tucumán, 85(2), 17-31. 4. IDAE. (2007). Guía técnica sobre procedimiento de inspección periódica de eficiencia energética para calderas. Instituto para la Diversificación y Ahorro de la Energía. 5. NOM-085-SEMARNAT-2011.(2011). Contaminación atmosférica - Niveles máximos permisibles de emisión de contaminantes de las fuentes fijas que usan combustibles fósiles o alternos - Especificaciones de combustión, requisitos y métodos de prueba. Diario Oficial de la Federación, México. 6. NOM-043-SEMARNAT-1993. (1993). Límites máximos permisibles de emisión a la atmósfera de partículas sólidas provenientes de fuentes fijas. Diario Oficial de la Federación, México. 7. Reyes, L. G. Q., Linzan, Á. R. A., & Ramos, P. A. R. (2021). Determinación de Indicadores de Confiabilidad, Mantenibilidad y Disponibilidad. Caso de Estudio: Industria de Elaboración de Conservas de Atún. Revista Cubana de Ingeniería, 12(2), e276-e276. No. Control number Equipment identificaton and avaluation date Evaluated parameters Evaluation method Official Mexican standard that establishes the maximum permissible limits 1 144PFF25-07 P1C Boiler No. 9 600 CC (June 23, 2025) 2 144PFF25-09 P1C Boiler No. 7 (June 23, 2025) Particulas suspendidas totales Monoxido de carbono Oxidos de nitrogeno NMX-AA-009-1993SCFI NMX-AA-010-SCFI2001 NMX-AA-054-1978 NMX-AA-035-1976 US EPA Test Method 3A 06-1990 US EPA Test Method 10 09-1996 US EPA Test Method 7E2009 NOM-043-SEMARNAT-1993 NOM-085-SEMARNAT-2011 (for reference only) No. Equipment Parameter Particle concentration, referred to STP and dry base (mg/m3N) LMPE established in NOM-043-SEMARNAT1993 (mg/m3N) Conclusion 1 P1C Boiler No. 9 600 CC 120.414 810.080 OK 2 P1C Boiler No. 7 67.142 588.535 OK Particulas suspendidas totales No. Equipment Parameter Concentration of the pollutant determined for the chimney (ppmV) Concentration refers to 5% O2 and dry base (ppmV) LMPE established in NOM-085SEMARNAT-2011 (ppmV) Conclusion CO 324.474 874.431 Not applicable Not applicable NOx 35.012 94.354 Not applicable Not applicable CO 231.147 622.923 Not applicable Not applicable NOx 6.655 17.934 Not applicable Not applicable P1C Boiler No. 9 600 CC 1 2 P1C Boiler No. 7 “Diagnosis and Definition of Performance Indicators for the Efficiency of the Steam Generation Process in a Biomass Boiler” 1079 C. García-Lopez1, RAJAR Volume 11 Issue 11 November 2025 8. SECOVAM. (2025). Informe de resultados de emisiones contaminantes a la atmosfera. Servicios de consultoría y verificación ambiental. No. de informe: 152 SUC2025-A. 9. Tene, E., López, J., & Chavez, O. (2023). Análisis del Sistema Integrado de Gestión y su impacto en la rentabilidad de las medianas y grandes empresas del sector industrial de Cotopaxi. Polo del Conocimiento: Revista científico-profesional, 8(3), 3037-3053.