4E and risk assessment of a novel integrated biomass driven polygeneration system based on integrated sCO2-ORC-AD-SOFC-SOEC-PEMFC-PEMEC

Abstract

This study presents an innovative multi-generation system that produces valuable products such as power, fresh water, hydrogen, and oxygen by burning two different fuels in a biomass burner. Fresh water production in this system is done with the help of an adsorption desolation unit. The evaluations on this system have been done considering two types of biomass fuel from the four perspectives: energy, exergy, exergoeconomic, and exergoenvironmental analysis. In this study, the possible risks of the system have been evaluated using the numerical risk method and process stream index. In addition, by simulating the risks in the system with the help of PHAST software, a more accurate assessment of risk and accident modeling has been done. Furthermore, the integrated system is optimized with seven different optimization algorithms in two modes of four and five objectives. The results show that the polygeneration efficiency for municipal solid waste is 51.84% and for olive pits is 41.05%. The development of the exergoeconomic indicates that the total cost rate of the entire system for municipal solid waste and olive pits equals 0.181 US$/s and 0.156 US$/s, respectively. In evaluating the environmental effects of this system, the total ecological impact rate for municipal solid waste and olive pits equal 128.50 Pts/h and 119.70 Pts/h, respectively. The results obtained from the system risk assessment using the process stream index method show that the stream entering the turbine of one of the proposed organic Rankine cycles (stream 80) is the most dangerous stream among other steams. Also, the results obtained from the numerical risk analysis of the system show that the system will have a lower risk in the case of using municipal solid waste than in the case of using olive pits. Also, this system has been optimized with the help of seven different algorithms in two modes of four and five objectives. The objective functions used in this evaluation are polygeneration efficiency, total cost rate, total environmental impact rate, Levelized cost of electricity, and total risk. The optimization results show that the widespread use of two algorithms, multiobjective particle swarm optimization and Thompson sampling efficient multiobjective optimization, can create a higher percentage of improvement in the objective functions in both four and five objective modes.