Prediction of Hydrogen Production from Solid Oxide Electrolytic Cells Based on ANN and SVM Machine Learning Methods

In recent years, the application of machine learning methods has become increasingly common in atmospheric science, particularly in modeling and predicting processes that impact air quality. This study focuses on predicting hydrogen production from solid oxide electrolytic cells (SOECs), a technology with significant potential for reducing greenhouse gas emissions and improving air quality. We developed two models using artificial neural networks (ANNs) and support vector machine (SVM) to predict hydrogen production. The input variables are current, voltage, communication delay time, and real-time measured hydrogen production, while the output variable is hydrogen production at the next sampling time. Both models address the critical issue of production hysteresis. Using 50 h of SOEC system data, we evaluated the effectiveness of the ANN and SVM methods, incorporating hydrogen production time as an input variable. The results show that the ANN model is superior to the SVM model in terms of hydrogen production prediction performance. Specifically, the ANN model shows strong predictive performance at a communication delay time epsilon = 0.01-0.02 h, with RMSE = 2.59 x 10-2, MAPE = 33.34 x 10-2%, MAE = 1.70 x 10-2 Nm3/h, and R2 = 99.76 x 10-2. At delay time epsilon = 0.03 h, the ANN model yields RMSE = 2.74 x 10-2 Nm3/h, MAPE = 34.43 x 10-2%, MAE = 1.73 x 10-2 Nm3/h, and R2 = 99.73 x 10-2. Using the SVM model, the prediction error values at delay time epsilon = 0.01-0.02 h are RMSE = 2.70 x 10-2 Nm3/h, MAPE = 44.01 x 10-2%, MAE = 2.24 x 10-2 Nm3/h, and R2 = 99.74 x 10-2, while at delay time epsilon = 0.03 h they become RMSE = 2.67 x 10-2 Nm3/h, MAPE = 43.44 x 10-2%, MAE = 2.11 x 10-2 Nm3/h, and R2 = 99.75 x 10-2. With this precision, the ANN model for SOEC hydrogen production prediction has positive implications for air pollution control strategies and the development of cleaner energy technologies, contributing to overall improvements in air quality and the reduction of atmospheric pollutants.