Machine learning-based multi-objective optimization of concentrated solar thermal gasification of biomass incorporating life cycle assessment and techno-economic analysis

The combination of solar and biomass energy systems is regarded as a highly promising technology for tackling the challenges related to greenhouse gas emissions from energy generation and the increasing costs of energy production. This research centers on an integrated solar-bioenergy system, which includes a concentrated solar tower, thermal energy storage, and a combined cycle gas turbine. The system was evaluated using a multiobjective optimization approach considering life cycle assessment and cost-benefit analysis. The long shortterm memory recurrent neural network algorithm with 5.1 % average error had been employed to capture the intricate temporal dependencies and dynamics of the system. The scenarios are expanded by using the Monte Carlo approach to address the challenges of limited specialized models and experiments for the system. The optimal solution is determined through the technique for order preference by similarity to ideal solution method. Carbon tax significantly influenced the results of the multi-objective optimization. The optimal configuration of the system could avoid the trade-off phenomenon when treating the carbon tax as revenue. The best scenario of the system with the cumulative reduction in global warming potential amounted to 415,960 tons of CO2-eq and a 30-year net present worth of euro4,298 million. Without considering the carbon tax as revenue, the trade-off is present. The best scenario of the system with the cumulative reduction in global warming potential amounted to 132,615 tons of CO2-eq and net present worth of euro3,042 million. The findings highlight the robust prospects of the system across environmental and economic dimensions.