Article: Improving the energy efficiency of coal fired power plants using deep learning with optimisation algorithm Journal: International Journal of Advanced Mechatronic Systems (IJAMECHS) 2025 Vol.12 No.4 pp.222 - 234 Abstract: Reducing CO<SUB align="right"><SMALL>2</SMALL></SUB> emissions from coal-fired power plants (CFPP) is crucial for sustainable energy production. Continuous emission monitoring systems (CEMS) are commonly used but are costly and complex. This paper proposes the integration of lignite coal with biomass to reduce emissions and enhance efficiency. Additionally, optimising feed water heaters (FWH) minimises heat transients and fuel consumption. Key parameters such as reheat steam temperature, turbine extraction pressure and temperature, and combustion ratio are considered. To further optimise energy efficiency, an artificial neural network (ANN) enhanced with the improved seagull optimisation algorithm (ISOA) is employed. Compared to ANN, GA, and PSO, the proposed approach significantly reduces system errors and improves overall plant efficiency. Experimental results demonstrate an energy efficiency of 98.95%, highlighting the effectiveness of CFPP optimisation with deep learning and advanced optimisation techniques. Inderscience Publishers - linking academia, business and industry through research

Title: Improving the energy efficiency of coal fired power plants using deep learning with optimisation algorithm

Authors: Arvind Kumar Tiwari; Anupama; Arbind Kumar Amar; Chandan Kumar

Addresses: Applied Science Department, Rajkiya Engineering College, Sonbhadra, U.P., India; Wigwe University, Nigeria ' Mathematics at Darbhanga College of Engineering, Darbhanga, Bihar, India ' BP Mandal College of Engineering, Madhepura, Bihar India ' Darbhanga College of Engineering, Darbhanga, Bihar, India

Abstract: Reducing CO₂ emissions from coal-fired power plants (CFPP) is crucial for sustainable energy production. Continuous emission monitoring systems (CEMS) are commonly used but are costly and complex. This paper proposes the integration of lignite coal with biomass to reduce emissions and enhance efficiency. Additionally, optimising feed water heaters (FWH) minimises heat transients and fuel consumption. Key parameters such as reheat steam temperature, turbine extraction pressure and temperature, and combustion ratio are considered. To further optimise energy efficiency, an artificial neural network (ANN) enhanced with the improved seagull optimisation algorithm (ISOA) is employed. Compared to ANN, GA, and PSO, the proposed approach significantly reduces system errors and improves overall plant efficiency. Experimental results demonstrate an energy efficiency of 98.95%, highlighting the effectiveness of CFPP optimisation with deep learning and advanced optimisation techniques.

Keywords: coal-fired power plants; CFPP; artificial neural network; ANN; deep learning; DL; seagull optimisation algorithm; SOA; energy efficiency; EF; ultra-supercritical power plant; coal with biomass; carbon dioxide emissions; feed water heater; thermal efficiency.

DOI: 10.1504/IJAMECHS.2025.149367

International Journal of Advanced Mechatronic Systems, 2025 Vol.12 No.4, pp.222 - 234

Received: 30 May 2024
Accepted: 03 Sep 2024
Published online: 27 Oct 2025 *

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Title: Improving the energy efficiency of coal fired power plants using deep learning with optimisation algorithm

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