Development and process simulation of a biomass driven SOFC-based electricity and ammonia production plant using green hydrogen; AI-based machine learning-assisted tri-objective optimization

Abstract

Considering the vital role of hydrogen and ammonia in energy and chemical industry, the renewable energy-based green ammonia and hydrogen production is a good alternative in reducing the carbon emissions. In this respect, present paper aims at development of a novel biomass-fueled integrated energy system for tri-generation of electricity, hydrogen and ammonia. The system is composed of a biomass gasification integrated SOFC unit, combined with a VCl thermochemical unit for H₂ production and a Haber-Bosch reactor for ammonia synthesis. In order to enhance thermodynamic and economic performance, the thermochemical cycle is supposed to be used instead of a water electrolyzer for hydrogen production. This unit produces green hydrogen utilizing the waste heat of the SOFC without consuming additional electricity for water electrolysis. Thermodynamic, economic and environmental modellings are conducted using the EES software, while for triple-criteria optimization an ANN approach is applied based on AI-assisted machine learning via the MATLAB software. A sensitivity evaluation is carried out to examine the influences of key design/operating parameters on the system performance. Then, using gray wolf algorithm, triple-objective optimization is conducted to identify the best practical system operation based on minimum cost along with maximum efficiency and ammonia production. The results under optimized conditions indicate that, the system yields exergy efficiency of 49.2 % with levelized product cost of 25.4 $/GJ, and ammonia production rate of 24.9 kg/day.