A renewable multigeneration system based on biomass gasification and geothermal energy: Techno-economic analysis using neural network and Grey Wolf optimization

Abstract

Environmental challenges such as climate change, air pollution, and resource depletion are intensifying due to the widespread reliance on fossil fuels for energy. Addressing these problems requires a shift toward cleaner, renewable energy sources that can meet growing energy demands while minimizing environmental impact. This paper provides a comprehensive analysis, combining thermodynamic principles and machine learning, of a novel system that includes a biomass gasifier, PEM electrolyzer, geothermal energy source, thermoelectric generators, and a humidification-dehumidification (HDH) desalination unit. The biomass gasifier converts feedstock into syngas, the primary fuel for a combined power cycle. Hydrogen storage is identified as a key factor in the wider adoption of hydrogen as a clean energy source, with efficient storage methods crucial for its use in fuel cells, transportation, and various industrial applications. Geothermal energy is incorporated to supplement the system's energy needs, enhancing sustainability. Additionally, the Kalina cycle recovers waste heat from the gas turbine to generate extra electricity, further boosting the system's efficiency. Data-driven models are utilized in an integrated system to predict system behavior, enabling real-time optimization and adaptive control, and enhancing performance and resource utilization. The combined thermodynamic and machine learning analysis provides insights into the complex interactions and synergies within the integrated renewable energy system. Results demonstrate the feasibility and potential of such systems to meet energy demands sustainably while minimizing environmental footprint. Elicited optimized results are comprised of two scenarios including essential parameters such as exergy efficiency, Ẇ_net (net produced work), and CP_sys (cost of products).The optimized point in the first optimization scenario depicts exergy efficiency, Ẇ_net, and CP_sys of 47.93 %, 5958 kW, and 56.97 $/GJ with the initial parameters. In the second optimization scenario, the optimized point depicts EI, Ẇ_net, and CP_sys of 0.3996 kg/kWh, 5957.88 kW, and 56.90 $/GJ with the initial parameters. In the third optimization scenario, the optimized point depicts EI, exergy efficiency, and ṁ_hydrogen of 0.3996 kg/kWh, 47.97 %, and 56.085 kg/h with the initial parameters.