A Modeling Framework of Dynamic Risk Monitoring for Chemical Processes Based on Complex Networks

To ensure the stable and safe operations, this paper presents a modeling framework of dynamic risk monitoring for chemical processes. Multi-source process data are firstly denoised by the Wavelet Transform (WT). The Spearman's rank correlation coefficient (SRCC) of these data is calculated based on an appropriate time step and time window. An optimal correlation threshold is further applied to transform the SRCC matrix into an adjacency matrix. Accordingly, the model of complex networks (CNs) can be established for characterizing massive, disordered, and nonlinear process data. Network structure entropy is particularly introduced to transform process data into a single time series of relative risk. To illustrate its validity, a diesel hydrofining unit and Tennessee Eastman Process (TEP) are selected as test cases. Results show that the proposed modeling framework can effectively and reasonably monitor the risks of chemical processes in real time.