Abstract
In a chemical process, abnormal conditions may lead to process fluctuations or unplanned shutdowns, resulting in serious economic losses and even safety accidents. Early prediction of abnormal conditions can provide sufficient response time for operators to maintain the smooth operation of the device. This paper proposes an early prediction method for abnormal conditions in chemical processes combining physical knowledge and the data-driven model, which effectively enhances the model's generalizability and interpretability. Firstly, the key variable of abnormal conditions is determined based on physical knowledge. Then, the Spearman ranking correlation coefficient (SRCC) is utilized to extract feature variables related to the key variable. Next, a multivariate time series forecasting model combining long short-term memory (LSTM) and gated recurrent unit (GRU) is constructed to predict future trends of key variable data. Finally, taking the abnormal condition of crude oil with water in the crude unit (CU) as an example, the proposed method is successfully applied, showing better prediction performance and providing operators with sufficient time to take action.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Journal of Loss Prevention in the Process Industries
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.