Advanced optimization techniques for biogas-fueled cogeneration system using soft computing approaches

H. Rostamnejad Takleh, Milad Razbin, Sevda Mehdizadeh, Hediye Hashemzade, Milad Feili, Towhid Parikhani and Hossein Azariyan

Renewable Energy, 2025, vol. 245, issue C

Abstract: This study addresses critical gaps in biogas-based systems by proposing a novel cogeneration framework integrating advanced thermodynamic cycles, including the Kalina cycle, organic Rankine cycle, and vapor compression refrigeration cycle. Key innovations include utilizing the Kalina cycle separator's liquid output as a heat source and replacing the conventional condenser with a thermoelectric generator to reduce exergy loss and enhance power generation. Advanced predictive modeling using response surface methodology (RSM) and artificial neural networks (ANN), coupled with a genetic algorithm, enables dual-objective optimization of energy efficiency (EE) and total cost of the plant (TCOP). The proposed system achieved significant optimization results, demonstrating a TCOP of 64.68 $/MWh and an EE of 51.97 % under optimal conditions (ACPR = 7.17, T4 = 1400 K, YB = 69.80 %, TEVA = 270.36 K) with prediction errors below 2 %. Exergy destruction analysis revealed that the combustion chamber accounted for 54.9 % (1203 kW) of total exergy destruction, followed by the heat recovery unit at 16.67 % (365.2 kW) and the air preheater at 7.57 % (166 kW). Together, these components contributed 79.14 % of total exergy destruction. Other components, such as the thermoelectric generator and air compressor, exhibited moderate destruction rates of 84.04 kW and 116.3 kW, respectively.

Keywords: Multi-objective optimization; Genetic algorithm; Artificial neural network; Cogeneration plant; Biogas (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:245:y:2025:i:c:s0960148125003696

DOI: 10.1016/j.renene.2025.122707

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