Mechanism-based deep learning for tray efficiency soft-sensing in distillation process

Shaochen Wang, Wende Tian, Chuankun Li, Zhe Cui and Bin Liu

Reliability Engineering and System Safety, 2023, vol. 231, issue C

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.

Keywords: Tray efficiency; Soft sensing; Mechanism model; Deep learning; Distillation process (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:231:y:2023:i:c:s0951832022006275

DOI: 10.1016/j.ress.2022.109012

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