Deep learning approach for energy efficiency prediction with signal monitoring reliability for a vinyl chloride monomer process

Abstract

A major concern for a real-time operation is the reliability of measurements. Especially for the petrochemical industry, which reveals complexity and uncertainty, the measurement fault causes consequences on safety, profitability, and utility management. Degraded signal quality not only leads to improper control action, but it also creates more challenges for real-time energy efficiency management by reducing model performance and wasting more utility than standard operating practice. To improve system reliability and establish an effective energy efficiency monitoring tool, the combined framework for fault detection identification and energy efficiency prediction (FDI-EEP) based on a deep learning approach is proposed in this study. The FDI-EEP model uses the fault detection and identification result as a co-predictor for estimating energy efficiency aimed at improving the performance and reproducibility of the model and studying the effect of these faults on the downstream data-driven framework. Since process information is time-dependent, the long-short term memory layer is deployed on both networks to avoid gradient vanishing problems. A case study on the vinyl chloride monomer process datasets demonstrates that the proposed model precisely detected the measurement uncertainty and accurately performed the prediction task compared to other machine learning and prediction-based data cleaning methods.