Exergoeconomic Analysis and Multi-Objective Optimization of an Integrated CHP System Based on Syngas-Fueled Planar Solid Oxide Fuel Cell

Abstract

Exergoeconomic assessment of an energy conversion system based on energy-exergy analysis and appropriate economic principles, is essential to identify the costs of the inefficiencies both for the whole integrated system and for individual energy components. The current study contributes to an exergoeconomic analysis focusing on the steady-state performance of a biomass-fed combined heat and power (CHP) system including a two-stage auto-thermal biomass gasifier, a direct internal reforming planar solid oxide fuel cell (DIR-PSOFC) and a micro-gas turbine (mGT). A one-dimensional model of the DIR-PSOFC is used to investigate the temperature gradient within the solid structure of the fuel cell under different operating conditions. In order to assess the effect of the main system input parameters on the performance of the cogeneration system, a comprehensive parametric analysis is carried out. The results show that the highest rate of exergy destruction takes place in the gasifier with an amount of 39.23%, followed by the afterburner and the SOFC due to the highly irreversible nature of the process of these components. The system input exergy supplied by biomass is 525.7 kW, of which 53.2% is wasted in the system components and the exergy efficiency of the total CHP system is determined to be 49.72%. Furthermore, the results indicate that the highest exergy destruction cost rate is related to the afterburner with 2.39 ($⁄h). Based on the results of the sensitivity analysis, the trends of the performance parameters demonstrate some conflicts with the variation of the operating parameters, which implies the necessity of an optimization procedure. In all the operating conditions considered, the temperature difference along the cell length is kept below the maximum allowable temperature gradient, which is 150 K. Two-step multi-objective optimization has been conducted by use of non-dominated sorting genetic algorithm technique. Significant and newsworthy relationships between the optimal operating parameters and the considered design variables have been unveiled using the Pareto-based multi-objective optimization procedure.