PSO-Optimized Data-Driven and Mechanism Hybrid Model to Enhance Prediction of Industrial Hydrocracking Product Yields Under Data Constraints

The accurate prediction of hydrocracking product yields is crucial for optimizing resource allocation and improving production efficiency. However, the prediction of product flowrates in hydrocracking units often faces challenges due to insufficient data and weak correlations between input and output variables. This study proposes a hybrid framework combining a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model, mechanism modeling, and Particle Swarm Optimization (PSO) to address these issues. The CNN-LSTM captures spatiotemporal dependencies in operational data, while the mechanism model incorporates domain-specific physical constraints. The hybrid model is structured in both series and parallel configurations, with PSO optimizing key hyperparameters to enhance its predictive performance. The results demonstrate significant improvements in prediction accuracy, with determination coefficients (R2s) reaching 0.896 (kerosene), 0.879 (residue), 0.899 (heavy naphtha), and 0.78 (light naphtha). Shapley Additive Explanations (SHAP) and Mutual Information Coefficient (MIC) analyses highlight the mechanism model’s role in improving feature interpretability. This study underscores the efficacy of integrating kinetics modeling, deep learning, and metaheuristic optimization for complex industrial processes under data constraints, offering a robust approach to enhance hydrocracking yield prediction.