Modeling, analysis and prediction of waste biomass gasification integrated with parallel multi-stack solid oxide fuel cell systems for low CO2 emissions: A mechanistic and data-driven approach

Abstract

Developing efficient, eco-friendly power generation systems is crucial for future clean energy policies. Biomass-driven solid oxide fuel cell (SOFC) systems promise clean energy, but ensuring efficient, safe operation remains challenging. Additionally, multi-stack SOFC systems are an effective means to ensure fuel utilization efficiency and enhance system reliability. This study uses aggregate modeling to model the gasification-integrated parallel multi-stack SOFC system (GIMCS). Fifteen biomass-derived fuels are passed into the GIMCS to analyse the effects of gasification temperature, water vapor mass flow rate to biomass mass flow rate (S/B) on syngas fractions, and their impact, along with reaction temperature, on power generation performance. Then, the dataset of the GIMCS (15 different biomass gases as fuels) was used for genetic algorithm backpropagation (GA-BP) model training for operating condition prediction (electrical efficiency, net voltage, and current density of each stack). Additionally, CO₂ emissions from waste biomass gasification were compared to those from power generation via a GIMCS. The findings suggest that the GA-BP model provides highly accurate output estimates (R²>0.991, MAPE<0.238, RMSE<0.234) and that the GIMCS emits less CO₂ than waste biomass gasification. This study supports predicting the performance of GIMCS to enhance waste biomass-to-electricity conversion and optimize system operating parameters for efficient and safe operation.