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A Method of Optimizing Cell Voltage Based on STA-LSSVM Model

Author

Listed:
  • Chenhua Xu

    (School of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China)

  • Zhicheng Tu

    (School of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China)

  • Wenjie Zhang

    (School of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China)

  • Jian Cen

    (School of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China
    Guangzhou Intelligent Building Equipment Information Integration and Control Key Laboratory, Guangzhou 510665, China)

  • Jianbin Xiong

    (School of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China)

  • Na Wang

    (School of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China)

Abstract

It is challenging to control and optimize the aluminum electrolysis process due to its non-linearity and high energy consumption. Reducing the cell voltage is crucial for energy consumption reduction. This paper presents an intelligent method of predicting and optimizing cell voltage based on the evaluation of modeling the comprehensive cell state. Firstly, the Savitzky–Golay filtering algorithm(SGFA) is adopted to denoise the sample data to improve the accuracy of the experimental model. Due to the influencing factors of the cell state, a comprehensive evaluation model of the cell state is established. Secondly, the model of the least squares supports vector machine (LSSVM) is proposed to predict the cell voltage. In order to improve the accuracy of the model, the state transition algorithm (STA) is employed to optimize the structure parameters of the model. Thirdly, the optimization and control model of the cell voltage is developed by an analysis of the technical conditions. Then, the STA is used to realize the optimization of the front model. Finally, the actual data were applied to the experiments of the above method, and the proposed STA was compared with other methods. The results of experiments show that this method is efficient and satisfactory. The optimization value of average cell voltage based on the STA-LSSVM is 3.8165v, and it can be used to guide process operation. The DC power consumption is 11,971 KW·h per tonne of aluminum, with a reduction in power consumption of 373 KW·h. This result guarantees the reduction of aluminum electrolysis energy consumption.

Suggested Citation

  • Chenhua Xu & Zhicheng Tu & Wenjie Zhang & Jian Cen & Jianbin Xiong & Na Wang, 2022. "A Method of Optimizing Cell Voltage Based on STA-LSSVM Model," Mathematics, MDPI, vol. 10(24), pages 1-20, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4710-:d:1000499
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    References listed on IDEAS

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    1. Madeline Hui Li Lee & Yee Chee Ser & Ganeshsree Selvachandran & Pham Huy Thong & Le Cuong & Le Hoang Son & Nguyen Trung Tuan & Vassilis C. Gerogiannis, 2022. "A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models," Mathematics, MDPI, vol. 10(8), pages 1-23, April.
    2. Shaohong Yan & Yanbo Zhang & Xiangxin Liu & Runze Liu, 2022. "Rock Burst Intensity Classification Prediction Model Based on a Bayesian Hyperparameter Optimization Support Vector Machine," Mathematics, MDPI, vol. 10(18), pages 1-16, September.
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