An advanced heat design-CO2 capture network for an oxy-biogas fuel combustion cycle combined with a CAES-based method for peak shaving: An artificial intelligent-driven optimization

This study presents a novel heat design-CO2 capture network utilizing a multi-stage parallel-series approach. The network incorporates an oxy-biogas fuel combustion process integrated with a modified gas turbine cycle, a supercritical CO2 cycle in integration with a heating provider and an organic flash cycle, a closed Brayton cycle coupled with a modified organic flash cycle, and a compressed air energy storage system for peak load management. The proposed design is simulated and evaluated using thermodynamic, economic, and sustainability-exergoenvironmental standpoints. Furthermore, a thorough sensitivity analysis is carried out to identify system's operational conditions, revealing that the combustion chamber outlet temperature is the most critical variable influencing performance variables, with an average sensitivity index of 0.469. Subsequently, an artificial intelligence-driven multi-objective optimization process is conducted, incorporating artificial neural networks, NSGA-II methodology, and TOPSIS decision-making. The objective functions defined for the optimization include the system's exergetic round-trip efficiency, net present value, and specific cost of products, yielding optimum values of 47.18 %, 11.01 M$, and 35.11 $/GJ, respectively. Under optimal conditions, the system achieves a net electricity of 1670 kW and a heat load of 162.9 kW, resulting in a round-trip efficiency of 53.66 %. The potential for CO2 capture is assessed at 0.277 kg/s. Furthermore, the corresponding exergoenvironmental impact improvement and payback period are quantified at 0.971 and 8.62 years, respectively.