Enhanced heat recovery in a biogas power plant through a multi-stage approach utilizing Brayton, organic flash, and SCO₂ cycles; thermal-economic optimization utilizing ANNs, NSGA-II, and LINMAP.

This study presents a novel multi-heat recovery design integrated with a modified gas turbine power plant featuring a biogas-fueled oxyfuel combustion process and a CO2 capture unit. The proposed method utilizes a multi-stage parallel-series heat recovery approach, integrating a closed Brayton cycle with a modified organic flash cycle alongside a supercritical CO2 plant paired with a heating provider and an organic flash cycle. Engineering equation solver software is employed to model the suggested configuration, allowing for analyzing its thermodynamic, sustainability, and financial performance metrics. Additionally, an artificial intelligence-aided optimization process is implemented, utilizing artificial neural networks, NSGA-II methodology, and LINMAP decision-making techniques. The optimization focuses on exergy efficiency and payback period as the objective functions. Results indicate an improvement in exergy efficiency by 5.22 percentage points over the baseline model, achieving a value of 42.15%. The payback period has also been reduced by 9.31%, demonstrating a value of 2.63 years. Under optimal conditions, the system can produce an electrical output of 1402 kW and a heating load of 206.7 kW. Furthermore, calculations demonstrate a CO2 capture potential of 0.278 kg/s, a sustainability index of 1.73, a total net present value of 18.48 M$, and a total unit production cost of 34.83 $/GJ.