Energy consumption mode identification and monitoring method of process industry system under unstable working conditions. (January 2023)

• Record Type: Journal Article
• Authors: Zhu, Mingrui; Ji, Yangjian; Zhu, Xiaoyang; Ren, Kai
• Is Part Of: Advanced engineering informatics. Volume 55(2023)
• Publication Date: 2023-01
• Subjects: Unstable working condition -- Energy consumption mode identification -- Hidden Semi-Markov Model -- Dynamic kernel principle component analysis; ECM Energy Consumption Mode -- HSMM Hidden Semi-Markov Model -- HMM Hidden Markov Model -- VBGMM Variational Bayesian Gaussian Mixture Model -- PCA Principle Component Analysis -- KPCA Kernel Principal Component Analysis -- DPCA Dynamic Principal Component Analysis -- DKPCA Dynamic Kernel Principal Component Analysis -- NPCA Nonlinear Principal Component Analysis -- PPCA Probabilistic Principal Component Analysis -- AHP Analytic Hierarchy Process -- RF Random Forest -- DPC Density Peaks Clustering -- NPA Neighbor Phase Association -- MMD Maximum Mean Difference -- ML Machine Learning -- MSPC Multivariate Statistical Process Control -- PLS Partial Least Square -- SFPLS Slow Feature Partial Least Squares -- SVM Supported Vector Machines -- SPE Squared Prediction Error -- MAP maximum a posteriori -- KDE kernel density estimation -- SGS Slag Grinding System -- VRM Vertical Roller Mill -- SPC Specific Power Consumption -- CL Cycling Load -- VMS Vibration of Mill Shell -- PPS Product Particle Size -- HAFP Hot Air Furnace Pressure -- TML Thickness of Material Layer; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2023.101893
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: Process industry systems under unstable working conditions are prone to potential anomalies, deviating from the original transition trajectory, and taking longer than expected to return to stability due to persistent disturbances from uncertainties and experience-based regulation errors. The energy waste caused by this situation has not received sufficient attention, and cannot be addressed by existing energy consumption monitoring methods. Herein, an energy consumption mode (ECM) identification and monitoring method under unstable working conditions is proposed, consisting of ECM identification model and multi-mode dynamic monitoring model, focusing on the variation rules of the correlation between energy consumption and other states of the system. In the ECM identification stage, the ECM correlation parameters that reflect the comprehensive production information are selected. Then, given the transfer characteristics of ECM, a Hidden Semi-Markov Model (HSMM) is constructed to fit the migration between modes and the duration within modes. The Variational Bayesian Gaussian Mixture Model is introduced to improve the HSMM, which solves the problem of lacking prior knowledge of ECM and achieves the automatic classification and online identification of ECM. In the dynamic monitoring stage of multi-ECMs, a series of dynamic kernel principle component analysis models are established, and the corresponding monitoring thresholds are set for each ECM. By calculating the maximum of the posteriori probability and the mode thresholds, the ECMs under unstable conditions can be accurately identified and automatically monitored. Compared with previous methods, the proposed method reduces the false detection rate and missed detection rate of abnormal ECM identification to 1.04% and 1.31% in the actual slag grinding production process, which proves its effectiveness.