A pre-warning system of abnormal energy consumption in lead smelting based on LSSVR-RP-CI

• Published in: Journal of Central South University Vol. 26; no. 8; pp. 2175 - 2184
• Main Authors: Wang, Hong-cai, Fang, Hong-ru, Meng, Lei, Xu, Feng-xiang
• Format: Journal Article
• Language: English
• Published: Changsha Central South University 01.08.2019; Springer Nature B.V
• Subjects: Adaptive algorithms; Confidence intervals; Correlation coefficients; Energy conservation; Energy consumption; Energy management; Engineering; Genetic algorithms; Industrial engineering; Metallic Materials; Metallurgy; Model accuracy; Periodic variations; Predictions; Regression analysis; Regression models; Smelting; Statistical analysis; Support vector machines; Time series; Warning systems; lead smelting; 铅冶炼; 递归图; least square support vector regression (LSSVR); 能耗; energy consumption; recurrence plots (RP); confidence intervals (CI); 最小二乘支持向量回归; 置信区间
• ISSN: 2095-2899; 2227-5223
• DOI: 10.1007/s11771-019-4164-x

The pre-warning of abnormal energy consumption is important for energy conservation of industrial engineering. However, related studies on the lead smelting industries which usually have a huge energy consumption are rarely reported. Therefore, a pre-warning system was established in this study based on the intelligent prediction of energy consumption and the identification of abnormal energy consumption. A least square support vector regression (LSSVR) model optimized by the adaptive genetic algorithm was developed to predict the energy consumption in the process of lead smelting. A recurrence plots (RP) analysis and a confidence intervals (CI) analysis were conducted to quantitatively confirm the stationary degree of energy consumption and the normal range of energy consumption, respectively, to realize the identification of abnormal energy consumption. It is found the prediction accuracy of LSSVR model can exceed 90% based on the comparison between the actual and predicted data. The energy consumption is considered to be non-stationary if the correlation coefficient between the time series of periodicity and energy consumption is larger than that between the time series of periodicity and Lorenz. Additionally, the lower limit and upper limit of normal energy consumption are obtained.