Multi-objective optimization of sorption enhanced steam biomass gasification with solid oxide fuel cell

•The integration and optimization of the sorption enhanced steam biomass gasification with SOFC are investigated.•Multi-objective optimization (MOO) is used to design the integrated process.•The obtained Pareto solutions for different MOO problems are ranked using NFM and GRA.•Parametric uncertainty analysis is performed to identify least sensitive Pareto solutions. Biomass is one of the encouraging renewable energy sources to mitigate uncertainties in the future energy supply and to address the climate change caused by the increased CO2 emissions. Conventionally, thermal energy is produced from biomass via combustion process with low thermodynamic efficiency. Conversely, gasification of biomass integrated with innovative power generation technologies, such as Solid Oxide Fuel Cell (SOFC), offers much higher conversion efficiency. Typically, energy conversion process has multiple conflicting performance criteria, such as capital and operating costs, annual profit, thermodynamic performance and environment impact. Multi-objective Optimization (MOO) methods are used to found the optimal compromise in the objective function space, and also to acquire the corresponding optimal values of decision variables. This work investigates integration and optimization of a Sorption Enhanced Steam Biomass Gasification (SEG) with a SOFC and Gas Turbine (GT) system for the production of power and heat from Eucalyptus wood chips. The energy system model is firstly developed in Aspen Plus simulator, which has five main units: (1) SEG coupled with calcium looping for hydrogen-rich gas production, (2) hot gas cleaning and steam reforming, (3) SOFC-GT for converting hydrogen into electricity, (4) catalytic burning and CO2 compression, and (5) cement production from the purge CaO stream of SEG unit. Then, the design and operating variables of the conversion system are optimized for annual profit, annualized total capital cost, operating cost and exergy efficiency, using MOO. Finally, for the implementation purpose, two selection methods and parametric uncertainty analysis are performed to identify good solutions from the Pareto-optimal front.