Energy efficiency and savings analysis with multirate sampling for petrochemical process using convolutional neural network-based transfer learning

Abstract

Energy efficiency in the petrochemical industry is crucial in reducing energy consumption and environmental impact. An accurate energy efficiency model will provide valuable insight for supporting operational adjustment decisions. In practice, due to inconsistent sampling intervals in the petrochemical industry, the traditional approach for obtaining energy efficiency may be unreliable and difficult to handle these multirate data characteristics. Therefore, in this paper, a multi-channel convolutional neural network model integrating a model parameter-based transfer learning approach is proposed to improve the prediction of energy efficiency under inconsistent sampling intervals. The multi-channel structure aims to recognize a different pattern from the dataset by convolving the information along the time dimension. Concurrently, transfer learning allows the model to learn a new pattern of input after the model is fully trained. Finally, the performance for energy efficiency prediction and saving analysis is validated by applying it to the vinyl chloride monomer production case study. The result shows that the proposed model outperformed traditional models and typical convolutional neural network structures in terms of accuracy and reproducibility, with an r-square of 0.97. The utilization of transfer learning prevents a significant drop in performance and enhances adaptability in model learning on real-time energy tracking. Moreover, the energy gap analysis of the prediction result identified a significant energy-saving potential, which would decrease annual energy consumption by 7.25% on average and a 5,709-ton reduction in carbon dioxide emissions.