Early Warning of Internal Leakage in Heat Exchanger Network Based on Dynamic Mechanism Model and Long Short-Term Memory Method

• Main Authors: Wende Tian, Nan Liu, Dongwu Sui, Zhe Cui, Zijian Liu, Ji Wang, Hao Zou, Ya Zhao
• Format: Article
• Language: English
• Published: MDPI AG 2021-02-01
• Series: Processes
• Subjects: internal leakage; dynamic simulation; deep learning; long short-term memory; early warning; risk assessment
• Online Access: https://www.mdpi.com/2227-9717/9/2/378

In the process of butadiene rubber production, internal leakage occurs in heat exchangers due to excessive pressure difference. It leads to the considerable flow of organic matters into the circulating water system. Since these organic matters are volatile and prone to explode in the cold water tower, internal leakage is potentially dangerous for the enterprise. To prevent this phenomenon, a novel intelligent early warning and risk assessment method (DYN-EW-QRA) is proposed in this paper by combining dynamic simulations (DYN), long short-term memory (LSTM), and quantitative risk assessment (QRA). First, an original internal leakage mechanism model of a heat exchanger network is designed and simulated by DYN to obtain datasets. Second, the potential relationships between variables that have a direct impact on the hazards of the accident are deeply learned by LSTM to predict the internal leakage trends. Finally, the QRA method is used to analyze the range and destructive power of potential hazards. The results show that DYN-EW-QRA method has excellent performance.