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

基于 LSSVR-RP-CI 的铅冶炼异常能耗预警系统

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  • Renewable energy and system engineering
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Abstract

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.

摘要

异常能耗预警是工业过程节能的重要内容。然而, 对于通常能耗巨大的铅冶炼行业的相关研究 却鲜有报道。因此, 本研究建立了基于智能能耗预测和异常能耗识别的预警系统。采用自适应遗传算 法优化最小二乘支持向量回归(LSSVR)模型, 对铅冶炼过程的能耗进行预测。分别通过递归图 (RP)分 析和置信区间(CI)分析, 定量确定能耗的平稳程度和正常范围, 实现异常能耗的识别。通过比较实际 数据与预测数据, 发现LSSVR 模型的预测精度可达 90%以上。如果周期时间序列与能耗的相关系数 大于周期时间序列与洛伦兹时间序列的相关系数, 则认为能耗是非平稳的。此外, 得到了正常能耗的 上限和下限。

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Authors and Affiliations

  1. School of Energy Science and Engineering, Central South University, Changsha, 410083, China

    Hong-cai Wang  (王洪才) & Hong-ru Fang  (方鸿儒)

  2. School of Automation, Wuhan University of Technology, Wuhan, 430070, China

    Lei Meng  (孟磊)

  3. Hubei Key Laboratory of Advanced Technology of Automotive Components, Wuhan University of Technology, Wuhan, 430070, China

    Feng-xiang Xu  (徐峰祥)

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  1. Hong-cai Wang  (王洪才)
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  2. Hong-ru Fang  (方鸿儒)
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  3. Lei Meng  (孟磊)
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  4. Feng-xiang Xu  (徐峰祥)
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Correspondence to Lei Meng  (孟磊) or Feng-xiang Xu  (徐峰祥).

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The authors declare that they have no conflict of interests regarding the publication of this paper.

Foundation item: Project(2015SK1002) supported by Key Projects of Hunan Province Science and Technology Plan, China

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Wang, Hc., Fang, Hr., Meng, L. et al. A pre-warning system of abnormal energy consumption in lead smelting based on LSSVR-RP-CI. J. Cent. South Univ. 26, 2175–2184 (2019). https://doi.org/10.1007/s11771-019-4164-x

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  • DOI: https://doi.org/10.1007/s11771-019-4164-x

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