Convolutional Neural Network for Chemical Product Quality Prediction

Quality prediction of chemical products is a key link in ensuring stable production processes, improving product qualification rates, and reducing production costs. Traditional quality prediction methods rely on mechanistic models or statistical regression, making it difficult to handle the highly nonlinear, strongly coupled, and dynamically time-varying characteristics of chemical process data. Convolutional neural network (CNN), as a powerful deep learning model, can automatically extract spatial features from data, providing a new technological path for predicting the quality of chemical products. This study systematically explores the CNN based method for predicting chemical product quality, with a focus on analyzing the applications of CNN, CNN-LSTM hybrid model, CNN Attention mechanism, and Graph Convolutional Neural Network (GCN) in chemical quality prediction. By constructing a comprehensive experimental platform and using real chemical datasets (including hydrocracking, ethylene cracking, simulated moving bed processes, etc.), the performance of different models in prediction accuracy, generalization ability, and real-time performance was compared and evaluated. The experimental results show that the CNN based hybrid model significantly outperforms traditional methods, with the CNN-LSTM-Attention model achieving the highest prediction accuracy (R ² up to 0.9882) on multiple datasets. This study provides an effective deep learning solution for predicting the quality of chemical products, promoting the development of chemical production towards intelligence and precision.