Skip to main content
SpringerLink
Log in

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

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.
Fig. 12.
Fig. 13.

Similar content being viewed by others

REFERENCES

  1. Wurzinger, A., Leibinger, H., Jakubek, S., and Kozek, M., Data-driven modeling and nonlinear model predictive control design for a rotary cement kiln, IFAC-PapersOnLine, 2019, vol. 52, no. 16, pp. 759–764.  https://doi.org/10.1016/j.ifacol.2019.12.054

    Article  Google Scholar 

  2. Genc, Ö., Optimization of a fully air-swept dry grinding cement raw meal ball mill closed circuit capacity with the aid of simulation, Miner. Eng., 2015, vol. 74, pp. 41–50. https://doi.org/10.1016/j.mineng.2015.01.006

    Article  Google Scholar 

  3. Altun, O., Energy and cement quality optimization of a cement grinding circuit, Adv. Powder Technol., 2018, vol. 29, no. 7, pp. 1713–1723.  https://doi.org/10.1016/j.apt.2018.04.006

    Article  Google Scholar 

  4. Liu, J., Wang, Yu., and Zhang, Yo., A novel isomap-SVR soft sensor model and its application in rotary kiln calcination zone temperature prediction, Symmetry, 2020, vol. 12, no. 1, p. 167.  https://doi.org/10.3390/sym12010167

    Article  Google Scholar 

  5. Li, T., Zhang, Zh., and Chen, H., Predicting the combustion state of rotary kilns using a convolutional recurrent neural network, J. Process Control, 2019, vol. 84, pp. 207–214. https://doi.org/10.1016/j.jprocont.2019.10.009

    Article  Google Scholar 

  6. Ma, J.X., Modeling and adaptive real-time optimization of hydrometallurgy cyanidation leaching process, PhD Dissertation, Shenyang: Northeastern Univ., 2015.

  7. Gu, W.J., Modeling and operation of real-time optimization of C2 hydrogenation reactor, Green Pet. Petrochem., 2019, no. 5, pp. 14–21.

  8. Zhong, W.M., Qi, R.B., Du, W.L., et al., Research progress on operation optimization of chemical process, Chem. React. Eng. Technol., 2014, vol. 30, no. 3, pp. 281–288.

    Google Scholar 

  9. Pan, Y., Intelligent control of power plant SCR flue gas denitration system based on mechanism modeling, PhD Dissertation, Beijing: North China Electric Power Univ., 2015.

  10. Hong, J.H., Mechanism modeling and parameter optimization of high-efficiency double-turret ultrathin chip transferring process, PhD Dissertation, Wuhan, China: Huazhong Univ. of Science and Technology, 2018.

  11. Xie, W.M., Li, J., Zhou, F.P., et al., Power consumption prediction of sewage treatment plant based on BP neural network and genetic algorithm, Water Resour. Power, 2018, vol. 36, no. 8, pp. 202–204.

    Google Scholar 

  12. Li, J.N., Gao, X.Z., Chai, T.Y., et al., Data-driven operational optimization control of industrial processes, Control Theory Appl., 2016, vol. 33, no. 12, pp. 1584–1592.

    Google Scholar 

  13. Cao, B.F., Xie, Y.F., Yang, C.H., et al., Integrated modeling for production index of bauxite flotation based on multi-source data, Control Theory Appl., 2014, vol. 31, no. 9, pp. 1252–1261.

    Google Scholar 

  14. Dai, W., Wang, X.W., Lu, X.L., et al., Case-based reasoning and reinforcement learning integrated set-point optimization method for grinding process, Control Theory Appl., 2019, vol. 36, no. 1, pp. 53–64.

    Google Scholar 

  15. Wang, H.Y., Research on energy consumption prediction of forging line based on support vector machine, PhD Dissertation, Jinan, China: Shandong Univ., 2017.

  16. Ding, J.L., Research on optimal decision-making of operational indices of beneficiation process under dynamic environment, PhD Dissertation, Shenyang, China: Northeastern Univ., 2012.

  17. Hu, H.Z., Research on optimal and control of the process in new dry cement production, PhD Dissertation, Hangzhou, China: Zhenjiang Univ., 2012.

  18. Wu, M.S., Yuan, Z.G., and Zhang, Q., Modeling of the combined grinding system based on the cement particle size mode template, Control Eng. China, 2016, vol. 23, no. 9, pp. 1343–1348.

    Google Scholar 

  19. Wang, G.S., The specific surface area of cement was rapidly predicted by linear regression with a fineness of 45 um, Sichuan Cement, 2008, no. 2, pp. 50–51.

  20. Krundyshev, V.M., Identification of cyber threats in networks of industrial internet of things based on neural network methods using memory, Autom. Control Comput. Sci., 2020, vol. 54, no. 8, pp. 900–906. doi https://doi.org/10.3103/S0146411620080180

    Article  Google Scholar 

  21. Pchelkin, A.A. and Borisov, A.N., On the sensitivity of the neural network implementing the principal component analysis method, Autom. Control Comput. Sci., 2009, vol. 43, no. 4, pp. 195–202. https://doi.org/10.3103/S0146411609040051

    Article  Google Scholar 

  22. Lim, S. and Chi, S., XGBoost application on bridge management systems for proactive damage estimation, Adv. Eng. Inf., 2019, vol. 41, p. 100922.  https://doi.org/10.1016/j.aei.2019.100922

    Article  Google Scholar 

  23. Shi, X., Sun. Q., Ji, Ya., Xu, Q., Yang, X., and Hao, X., Predictive control research for cement burning system using two-cycle coupling optimization, Expert Syst. Appl., 2021, vol. 191, p. 116259. https://doi.org/10.1016/j.eswa.2021.116259

    Article  Google Scholar 

Download references

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).

Author information

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

Authors
  1. Chong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xunian Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lizhao Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaochen Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaochen Hao.

Ethics declarations

The authors declare that they have no conflicts of interest.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords:

Search

Navigation