Multi-objective bat optimization for a biomass gasifier integrated energy system based on 4E analyses

Abstract

An innovative biomass gasifier integrated plant was proposed for combined heating and power production in the current paper. The plant consists of an s-CO2 cycle, gasifier, combustion chamber, and a domestic water heater for heating purposes. The system was studied from different perspectives, i.e., energetic, exergetic, exergo-economic, economic, and environmental (4E). For this purpose, simulation of the proposed plant was carried out by EES software; then, by utilizing COMFAR III software, the economic sensitivity investigation was conducted to detect the influence of financial parameters on the system's economic features after installation of the plant. Results of economic evaluation unfolded that installing the proposed plant is affordable from the economic point of view. Besides, a sensitivity analysis was conducted to calculate the main performance indicants, including an environmental impact indicator. The proposed system was optimized by a robust Multi-Objective Bat Optimization Algorithm. For determining the final optimum solution, TOPSIS, LINMAP, and Shannon entropy methods were used in the optimization process. Optimization results were also compared to the conventional multi-objective optimization methods to detect the suitable optimization method. The findings of the comparison confirmed that the bat algorithm's performance had been better, based on Taylor and Violin diagrams. Besides, scatter plots of effective parameters are presented to define the suitable operating ranges. The results show that the optimum exergy efficiency, Levelized CO2 emissions, and total product cost are 38.42%, 0.4757 t/MWh, and 7.517 $/GJ obtained. The total product cost was reduced significantly from 10.01 $/GJ to 7.517$/GJ at the expense of a slight diminish in exergetic efficiency of about 2% through the use of the bat algorithm. Also, the annual greenhouse gas emission made by the proposed system was reduced by about 9% after the optimization process.