Mechanism-based deep learning for tray efficiency soft-sensing in distillation process. (March 2023)

• Record Type: Journal Article
• Authors: Wang, Shaochen; Tian, Wende; Li, Chuankun; Cui, Zhe; Liu, Bin
• Is Part Of: Reliability engineering & system safety. Volume 231(2023)
• Publication Date: 2023-03
• Subjects: Tray efficiency -- Soft sensing -- Mechanism model -- Deep learning -- Distillation process; LSTM long short-term memory -- RTD residence time distribution -- PCA principal component analysis -- PLS partial least square -- DL deep learning -- DNN deep neural network -- FCC fluid catalytic cracking -- MESH material balance, phase equilibrium, fraction summation andenthalpy balance -- GEV generalized extreme value -- MSE mean-square error -- RMSE root mean square error -- MAE mean absolute error -- DCNN deep convolution neural network -- BPNN back propagation neural network -- GRU gated recurrent unit network; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.109012
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Online soft sensing of tray efficiency in distillation system is realized by deep learning method. Mechanism model provides high-quality datasets for deep learning model. Cluster analysis extracts typical working conditions to further improve the value density of training set. Generalized extreme value distribution is used to assess the probability of tray anomalies. Abstract: Distillation is an important unit operation in the chemical industry. However, its process variables fluctuation can frequently cause abnormal conditions, resulting in the reduction of system reliability, and even causing safety accidents. Tray efficiency, as its key operation indicator, has been a long-term implicit variable that cannot be directly monitored so that the operators have insufficient information about the running status of the distillation system. Soft sensing for tray efficiency can greatly improve the safety, stability and reliability of the production system. In this paper, a mechanism-based deep learning method is proposed for the soft sensing of tray efficiency in distillation process. Firstly, based on the statistics of extreme alarm values and distillation process mechanism, the tray efficiency that is prone to anomalies is analyzed. The key trays that need to be monitored are identified. Secondly, the typical working conditions of the distillation system are focused by data clustering as the input of mechanism modeling. Then, the distillation system is simulated to obtain associated datasets of tray efficiency and process measurable variables. Finally, the LSTM-based deep learning model extracts the mechanical characteristics of the distillation system to construct a surrogate model for the tray efficiency soft-sensing by using these datasets.