Machine Learning-Based Modeling and Multi-Objective Optimization of Direct Urea–Hydrogen Peroxide Fuel Cell

Direct urea–hydrogen peroxide fuel cells (DUHPFCs) are promising for sustainable power generation, but their performance is governed by highly nonlinear material and operating interactions. This study develops a machine-learning framework employing a multi-output artificial neural network (ANN) to predict cell voltage, power density (PD), and substrate-based energy efficiency (SEE) of DUHPFCs. The ANN exhibits excellent predictive accuracy, achieving coefficients of determination (R2) above 0.995 and normalized root mean square errors (NRMSE) below 1.75 × 10−2 for all outputs. Model interpretability is enhanced by using Shapley additive explanations and partial dependence plots, which identify current density as the dominant factor affecting DUHPFC performance, followed by temperature and anolyte composition. The ANN is coupled with a multi-objective Pareto-search algorithm optimization (PAO) to resolve the trade-offs among competing performance metrics. Under different optimization objectives, a DUHPFC with an Ni0.2Co0.8/Ni-foam anode is predicted to achieve a maximum PD of 45.6 mW/cm2 with a low SEE of 2.6% or a maximum SEE of 15.2% with a moderate PD of 40.9 mW/cm2. Additionally, a balanced operating regime is identified, achieving a PD of 43.1 mW/cm2 and an SEE of 13.9%. Overall, the proposed framework provides an effective decision-support tool for optimizing DUHPFC performance under competing objectives.