| Summary: | This work proposes a new, efficient, economically, and environmentally viable approach for developing cutting-edge energy systems and assisting the anticipated global green transition with maximal renewable integration. The cogeneration of hydrogen and power is driven by biomass, which in turn drives the vanadium chloride cycle and the proton exchange membrane fuel cells. A cooling absorption unit is powered by waste heat recovered using a passive energy improvement technique to improve performance and cut costs. Energy, exergy, exergo-economic, exergo-environmental impacts, and CO2 emission rate of the suggested renewable-based model are analyzed using an engineering equation solver tool. Parametric analysis is also used to assess the impact of key operational factors on main performance indicators. With machine learning, a particle swarm method is implemented in MATLAB to find the optimal operating state with high precision and low computing cost. The results show the importance of multi-objective optimization by pointing out a conflicting change in the performance metrics from different angles by picking up the biomass moisture content and fuel cell current density. According to the optimization results, an acceptable total cost, environmental damage effectiveness, and exergy efficiency of 5 $/h, 0.86, and 55% are achieved through the integration of particle swarm optimizer and artificial neural network method. The results further reveal that the gasification temperature is not sensitive; however, changing the fuel cell utilization factor significantly impacts the system's performance from all sides. Finally, the chord diagram of the irreversibility rate indicates that the fuel cell and gasifier have the highest destruction of 6.4 kW and 2.6 kW under the optimum condition, owing to mixing and chemical reactions. As for the environmental aspect, by optimizing the system, the system's CO2 emission are greatly reduced. © 2023 Hydrogen Energy Publications LLC
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