Machine learning-assisted prediction and optimization of solid oxide electrolysis cell for green hydrogen production

Abstract

The solid oxide electrolysis cell (SOEC) holds great promise to efficiently convert renewable energy into hydrogen. However, traditional modeling methods are limited to a specific or reported SOEC system. Therefore, four machine learning models are developed to predict the performance of SOEC processes of various types, operating parameters, and feed conditions. The impact of these features on the SOEC's outputs is explained by the Shapley additive explanations and partial dependency plot analyses. The preferred model is integrated with a genetic algorithm to determine the optimal values of each input feature. Results show the improved extreme gradient enhanced regression (XGBoost) algorithm is the core of the machine learning model of the process since it has the highest R2 (> 0.95) in the three outputs. The electrolytic cell descriptors have a greater impact on the system performance, contributing up to 54.5%. The effective area, voltage, and temperature are the three most influential factors in the SOEC system, contributing 21.6%, 16.6%, and 13.0% to its performance. High temperature, high pressure, and low effective area are the most favorable conditions for H2 production rate. After conducting multi-objective optimization, the optimal current intensity and hydrogen production rate were determined to be 1.61 A/cm2 and 1.174 L/(h·cm2).

Keywords

• Solid oxide electrolysis cell
• Machine learning
• H2 production rate
• Feature importance analysis
• System optimization