Energy-Efficient Prediction of Carbon Deposition in DRM Processes Through Optimized Neural Network Modeling

Methane dry reforming (DRM) offers a promising route by converting two greenhouse gases into syngas, but catalyst deactivation through carbon deposition severely reduces energy efficiency. While neural networks offer potential for predicting carbon deposition and reducing experimental burdens, conventional random data partitioning in small-sample regimes compromises model accuracy, stability, and generalizability. To overcome these limitations, we conducted a systematic comparison between backpropagation (BP) and radial basis function (RBF) neural network models. Throughout 10 model trials with random trainset splits, the RBF model demonstrated superior performance and was consequently selected for further optimization. Then, we developed a K-fold cross-validation framework to enhance model selection, resulting in an optimized RBF model (RBF-Imp). The final model achieved outstanding performance on unseen test data (MSE = 0.0018, R² = 0.9882), representing a 64% reduction in MSE and a 4.3% improvement in R² compared to the mean performance across 10 independent validations. These results demonstrated significant improvements in the prediction accuracy, model stability, and generalization capability of the small-sample data model, providing intelligent decision-making support for the removal of carbon deposition.