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The Decision Algorithm of Cement Mill Operation Index Based on Improved Differential Evolution Algorithm

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Abstract

An improved differential evolutionary cement mill operation index decision algorithm based on constraint control and selection strategy is proposed to address the problem that the operation index is usually decided by manual experience in the cement mill operation process, which causes unqualified cement specific surface area and excessive cement mill power consumption. The algorithm uses an improved differential evolutionary algorithm to solve the long short-term memory network (LSTM) power consumption prediction model and specific surface area prediction model based on PCA and XGBoost. Constraints are set in the solution process to obtain the optimal solution that satisfies the quality index and the power consumption index. The optimal solution is used to guide the scheduling of each piece of equipment in the production process and to make decisions on the operating index of the cement mill grinding process so that the specific surface area is qualified and the power consumption is reduced.

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Funding

This work was supported by the National Natural Science Foundation of China (grant no. 62073281), the Hebei Provincial Natural Science Foundation (grant no. F2019203385), the Hebei Provincial Science and Technology Plan Project (grant no. 19211602D), the Second Batch of Youth Top-notch Talent Support Program in Hebei Province (grant no. 5040050).

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Authors and Affiliations

  1. Department of Information Engineering, Hebei College of Chemical and Pharmaceutical Technology, 050026, Shijiazhuang, China

    Chong Liu

  2. School of Electrical Engineering, Yanshan University, 066000, Qinhuangdao, China

    Xunian Yang, Lizhao Zheng & Xiaochen Hao

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  1. Chong Liu
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  2. Xunian Yang
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Correspondence to Xiaochen Hao.

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Chong Liu, Yang, X., Zheng, L. et al. The Decision Algorithm of Cement Mill Operation Index Based on Improved Differential Evolution Algorithm. Aut. Control Comp. Sci. 56, 533–545 (2022). https://doi.org/10.3103/S0146411622060049

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  • DOI: https://doi.org/10.3103/S0146411622060049

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