Fault detection and diagnosis for reactive distillation based on convolutional neural network

Reactive distillation (RD) shows its strength in achieving process intensification. However, the complex phenomena integrated in RD usually leads to various abnormal operating states, e.g. catalyst deactivation. Although control schemes have been designed to tackle some disturbances, diagnosing the operating state online is of vital importance for effectively avoiding serious accidents. In the present work, by using intensified process for formic acid production as benchmark, optimal design with stochastic algorithm was firstly performed and dynamic test was carried out to validate effectiveness of control structure. Then thirteen practical faults were considered and the corresponding response was simulated. By considering features in both spatial and temporal domain, historical dynamic process data with measurement noise was used to formulate samples, based on which deep convolutional neural network was trained and validated. The machine learning information in each layer was visualized using t-SNE and fault diagnosis rate shows the significance of the method. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

Reactive distillation (RD) is effective in process intensification, but can lead to abnormal operating states like catalyst deactivation. This study used a stochastic algorithm for optimal design in formic acid production, tested the control structure dynamically, and demonstrated the importance of online diagnosis for avoiding accidents. By training a deep convolutional neural network with historical process data, the method successfully diagnosed thirteen practical faults and visualized machine learning information for each layer, showing its significance in fault diagnosis.