Multi-objective optimization of boiler combustion efficiency and emissions using genetic algorithm and recurrent neural network in 660-MW coal-fired power plant

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.327063

Keywords:

boiler efficiency, co-firing, artificial neural network, genetic algorithm, net zero emission

Abstract

Indonesia has demonstrated a firm commitment to achieving Net Zero Emissions (NZE) by 2060. The implementation of diverse strategies, such as the application of biomass co-firing technology in coal-based steam power plants, demonstrates this commitment. This study focuses on the 660 MW supercritical coal-fired boiler as the object of investigation. The key problem addressed in this research is the unstable combustion performance due to the dynamic and nonlinear interactions among operational variables under biomass co-firing conditions. These fluctuations can negatively impact boiler efficiency, CO2 emissions, and the plant’s capability factor. The study proposes a dynamic multi-objective optimization framework using a Recurrent Neural Network (RNN), Response Surface Methodology (RSM), and a Multi-Objective Genetic Algorithm (MOGA) to enhance performance reliability and support Indonesia’s transition to cleaner energy sources.

The findings indicate that the RNN model exhibited superior prediction accuracy compared to the RSM, with a Root Mean Square Error (RMSE) value of 0.1523% for boiler efficiency, 1.6993% for CO2 emissions, and 0.5284% for the capability factor. The MOGA optimization exhibited an enhancement in boiler efficiency from 86.6793% to 87.32%, a reduction in CO2 emissions from 114.213 mg/Nm3 to 53.972 mg/Nm3, and an augmentation in the capability factor from 87.9% to 89.32%. Furthermore, coal consumption is reduced to 51,524 tons per hour, which can generate operational cost savings of IDR 1.34 billion per day.

The RNN and MOGA-based approaches have been demonstrated to be more effective than RSM for optimizing boiler combustion. This method is important for developing a strategy to improve the efficiency of the combustion process in boilers in coal-fired power plants. It will also help support the transition to clean energy and achieve the NZE 2060 target

Author Biographies

Mohamad Arwan Efendy, Universitas Indonesia

Master’s Candidates in Mechanical Engineering

Department of Mechanical Engineering

Ahmad Syihan Auzani, Universitas Indonesia

Doctoral

Department of Mechanical Engineering

Sholahudin Sholahudin, Universitas Indonesia

Doctoral

Department of Mechanical Engineering

References

  1. World Energy Outlook 2023. IEA. Available at: https://www.iea.org/reports/world-energy-outlook-2023
  2. Demirbaş, A. (2003). Sustainable cofiring of biomass with coal. Energy Conversion and Management, 44 (9), 1465–1479. https://doi.org/10.1016/s0196-8904(02)00144-9
  3. Eriksson, O., Finnveden, G., Ekvall, T., Björklund, A. (2007). Life cycle assessment of fuels for district heating: A comparison of waste incineration, biomass- and natural gas combustion. Energy Policy, 35 (2), 1346–1362. https://doi.org/10.1016/j.enpol.2006.04.005
  4. Gil, M. V., Rubiera, F. (2019). Coal and biomass cofiring. New Trends in Coal Conversion, 117–140. https://doi.org/10.1016/b978-0-08-102201-6.00005-4
  5. Nawaz, Z., Ali, U. (2020). Techno-economic evaluation of different operating scenarios for indigenous and imported coal blends and biomass co-firing on supercritical coal fired power plant performance. Energy, 212, 118721. https://doi.org/10.1016/j.energy.2020.118721
  6. Damstedt, B., Pederson, J. M., Hansen, D., Knighton, T., Jones, J., Christensen, C. et al. (2007). Biomass cofiring impacts on flame structure and emissions. Proceedings of the Combustion Institute, 31 (2), 2813–2820. https://doi.org/10.1016/j.proci.2006.07.155
  7. De, S., Assadi, M. (2009). Impact of cofiring biomass with coal in power plants – A techno-economic assessment. Biomass and Bioenergy, 33 (2), 283–293. https://doi.org/10.1016/j.biombioe.2008.07.005
  8. Roni, M. S., Chowdhury, S., Mamun, S., Marufuzzaman, M., Lein, W., Johnson, S. (2017). Biomass co-firing technology with policies, challenges, and opportunities: A global review. Renewable and Sustainable Energy Reviews, 78, 1089–1101. https://doi.org/10.1016/j.rser.2017.05.023
  9. Śladewski, Ł., Wojdan, K., Świrski, K., Janda, T., Nabagło, D., Chachuła, J. (2017). Optimization of combustion process in coal-fired power plant with utilization of acoustic system for in-furnace temperature measurement. Applied Thermal Engineering, 123, 711–720. https://doi.org/10.1016/j.applthermaleng.2017.05.078
  10. Yao, Z., Romero, C., Baltrusaitis, J. (2023). Combustion optimization of a coal-fired power plant boiler using artificial intelligence neural networks. Fuel, 344, 128145. https://doi.org/10.1016/j.fuel.2023.128145
  11. Nunes, L. J. R., Matias, J. C. O., Catalão, J. P. S. (2016). Biomass combustion systems: A review on the physical and chemical properties of the ashes. Renewable and Sustainable Energy Reviews, 53, 235–242. https://doi.org/10.1016/j.rser.2015.08.053
  12. Setiawan, A. A. R., Sofyan Munawar, S., Ishizaki, R., Putra, A. S., Ariesca, R., Sidiq, A. N. et al. (2024). Optimizing biomass supply for cofiring at power plants to minimize environmental impact: A case of oil palm empty fruit bunches in West Java. Fuel, 367, 131359. https://doi.org/10.1016/j.fuel.2024.131359
  13. Wang, H., Yan, Y., Li, Z., Cao, Z., Fu, Y., Zhou, Z., Zhao, D. (2025). Carbon mitigation potential and economic benefits of biomass co-firing in coal-fired power plants: A case study in Nanjing, China. Energy, 314, 134262. https://doi.org/10.1016/j.energy.2024.134262
  14. Bhuiyan, A. A., Blicblau, A. S., Islam, A. K. M. S., Naser, J. (2018). A review on thermo-chemical characteristics of coal/biomass co-firing in industrial furnace. Journal of the Energy Institute, 91 (1), 1–18. https://doi.org/10.1016/j.joei.2016.10.006
  15. Tokarski, S., Głód, K., Ściążko, M., Zuwała, J. (2015). Comparative assessment of the energy effects of biomass combustion and co-firing in selected technologies. Energy, 92, 24–32. https://doi.org/10.1016/j.energy.2015.06.044
  16. Xu, W., Huang, Y., Song, S. (2024). On-line combustion optimization framework for coal-fired boiler combining improved cultural algorithm, deep learning, multi-objective evolutionary algorithm with improved case-based reasoning technology. Fuel, 358, 130225. https://doi.org/10.1016/j.fuel.2023.130225
  17. Muhammad Ashraf, W., Moeen Uddin, G., Muhammad Arafat, S., Afghan, S., Hassan Kamal, A., Asim, M. et al. (2020). Optimization of a 660 MWe Supercritical Power Plant Performance – A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency. Energies, 13 (21), 5592. https://doi.org/10.3390/en13215592
  18. Box, G. E. P., Wilson, K. B. (1951). On the Experimental Attainment of Optimum Conditions. Journal of the Royal Statistical Society Series B: Statistical Methodology, 13 (1), 1–38. https://doi.org/10.1111/j.2517-6161.1951.tb00067.x
  19. Montgomery, D. (2019). Design and Analysis of Experiments. Wiley, 688.
  20. Jensen, W. A. (2017). Response Surface Methodology: Process and Product Optimization Using Designed Experiments 4th edition. Journal of Quality Technology, 49 (2), 186–188. https://doi.org/10.1080/00224065.2017.11917988
  21. Skovgaard, L. T. (2000). Applied regression analysis. 3rd edn. N. R. Draper and H. Smith, Wiley, New York, 1998. No. of pages: xvii+706. Price: £45. ISBN 0‐471‐17082‐8. Statistics in Medicine, 19 (22), 3136–3139. https://doi.org/10.1002/1097-0258(20001130)19:22<3136::aid-sim607>3.3.co;2-h
  22. Antony, J. (2014). Design of Experiments for Engineers and Scientists. Elsevier. https://doi.org/10.1016/c2012-0-03558-2
  23. Galintin, O., Rasit, N., Hamzah, S. (2020). Production and Characterization of Eco Enzyme Produced from Fruit and Vegetable Wastes and its Influence on the Aquaculture Sludge. Biointerface Research in Applied Chemistry, 11 (3), 10205–10214. https://doi.org/10.33263/briac113.1020510214
  24. Garg, S., Shariff, A. M., Shaikh, M. S., Lal, B., Suleman, H., Faiqa, N. (2017). Experimental data, thermodynamic and neural network modeling of CO2 solubility in aqueous sodium salt of l -phenylalanine. Journal of CO2 Utilization, 19, 146–156. https://doi.org/10.1016/j.jcou.2017.03.011
  25. Ji, Y., Kang, Z., Liu, X. (2021). The data filtering based multiple‐stage Levenberg–Marquardt algorithm for Hammerstein nonlinear systems. International Journal of Robust and Nonlinear Control, 31 (15), 7007–7025. https://doi.org/10.1002/rnc.5675
Multi-objective optimization of boiler combustion efficiency and emissions using genetic algorithm and recurrent neural network in 660-MW coal-fired power plant

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Published

2025-06-27

How to Cite

Efendy, M. A., Auzani, A. S., & Sholahudin, S. (2025). Multi-objective optimization of boiler combustion efficiency and emissions using genetic algorithm and recurrent neural network in 660-MW coal-fired power plant. Eastern-European Journal of Enterprise Technologies, 3(8 (135), 23–33. https://doi.org/10.15587/1729-4061.2025.327063

Issue

Vol. 3 No. 8 (135) (2025): Energy-saving technologies and equipment

Section

Energy-saving technologies and equipment

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Copyright (c) 2025 Mohamad Arwan Efendy, Ahmad Syihan Auzani, Sholahudin Sholahudin

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