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Forecasting the Required Quantity of Cement Manufacturing Materials Using Time Series and Q-Network Techniques Cover

Forecasting the Required Quantity of Cement Manufacturing Materials Using Time Series and Q-Network Techniques

Open Access
|Oct 2025

References

  1. Md AQ, Jha K, Haneef S, Sivaraman AK, Tee KF. A review on data-driven quality prediction in the production process with machine learning for industry 4.0. Processes. 2022;10(10). DOI: 10.3390/pr10101966.
  2. Zermane H, Kasmi R. Intelligent industrial process control based on fuzzy logic and machine learning. Int J Fuzzy System Applications. 2020;9(1):92-111. DOI: 10.4018/IJFSA.2020010104.
  3. Zermane H, Mouss H. Development of an internet and fuzzy based control system of manufacturing process. Int J Automation Computing. 2017;14(6):706-18. DOI: 10.1007/s11633-016-1027-x.
  4. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9:1735-80. DOI: 10.17582/journal.pjz/2018.50.6.2199.2207.
  5. Schuster M, Paliwal KK. Bidirectional recurrent neural networks. IEEE Trans Signal Processing. 1997;45(11):2673-81. DOI: 10.1109/78.650093.
  6. Yufang L, Mingnuo C, Wanzhong Z. Investigating long‐term vehicle speed prediction based on BP‐LSTM algorithms. IET Intelligent Transport Systems. 2019;13(8):1281-90. DOI: 10.1049/iet-its.2018.5593.
  7. François-Lavet V, Henderson P, Islam R, Bellemare MG, Pineau J, Bellemare MG. An introduction to deep reinforcement learning. Foundations Trends Machine Learning. 2018;II(3-4):1-140. DOI: 10.1561/2200000071.
  8. Plaat A. Deep Reinforcement Learning. Netherlands: Springer Nature; 2022. DOI: 10.1007/978-981-19-0638-1.
  9. Zermane H, Madjour H, Ziar A, Zermane A. Forecasting material quantity using machine learning and time series techniques. J Electrical Eng. 2024;75(3):237-48. DOI: 10.2478/jee-2024-0029.
  10. Kashyap AA, Raviraj S, Devarakonda A, Nayak KSR, Santhosh KV, Bhat SJ. Traffic flow prediction models - A review of deep learning techniques. Cogent Eng. 2022;9(1):1-24. DOI: 10.1080/23311916.2021.2010510.
  11. Hossain GMS, Rashid MdHO, Islam MdR, Sarker A, Yasmin Must A. Towards mining public opinion: An attention-based long short term memory network using transfer learning. J Computer Communications. 2022;10(06):112-31. DOI: 10.4236/jcc.2022.106010.
  12. Liu B, Song C, Wang Q, Wang Y. Forecasting of China’s solar PV industry installed capacity and analyzing of employment effect: based on GRA-BiLSTM model. Environ Sci Pollut Res. 2022;29(3):4557-73. DOI: 10.1007/s11356-021-15957-1.
  13. Sivamohan S, Sridhar SS. An optimized model for network intrusion detection systems in industry 4.0 using XAI based Bi-LSTM framework. Neural Computing Applications. 2023;35(15):11459-75. DOI: 10.1007/s00521-023-08319-0.
  14. Usuga Cadavid JP, Lamouri S, Grabot B, Pellerin R, Fortin A. Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0. J Intelligent Manufacturing. 2020;31(6):1531-58. DOI: 10.1007/s10845-019-01531-7.
  15. Cai C, Tao Y, Zhu T, Deng Z. Short-term load forecasting based on deep learning bidirectional LSTM neural network. Appl Sci. 2021;11(17). DOI: 10.3390/app11178129.
  16. Hong YY, Martinez JJF, Fajardo AC. Day-ahead solar irradiation forecasting utilizing Gramian angular field and convolutional long short-term memory. IEEE Access. 2020;8:18741-53. DOI: 10.1109/ACCESS.2020.2967900.
  17. Greff K, Srivastava RK, Koutnik J, Steunebrink BR, Schmidhuber J. LSTM: A search space Odyssey. IEEE Trans Neural Networks Learning Systems. 2017;28(10):2222-32. DOI: 10.1109/TNNLS.2016.2582924.
  18. Zarzycki K, Ławryńczuk M. LSTM and GRU neural networks as models of dynamical processes used in predictive control: A comparison of models developed for two chemical reactors. Sensors. 2021;21(16). DOI: 10.3390/s21165625.
  19. Abumohsen M, Owda AY, Owda M. Electrical load forecasting using LSTM, GRU, and RNN algorithms. Energies. 2023;16(5):1-31. DOI: 10.3390/en16052283.
  20. Furizal, Maarif A, Rifaldi D. Application of Machine Learning in Healthcare and Medicine: A Review. J Robotics Control (JRC). 2023;4(5):621-31. DOI: 10.18196/jrc.v4i5.19640.
  21. Van Houdt G, Mosquera C, Nápoles G. A review on the long short-term memory model. Artif Intelligence Rev. 2020;53(8):5929-55. DOI: 10.1007/s10462-020-09838-1.
  22. Xu Q, Hao X, Shi X, Zhang Z, Sun Q, Di Y. Control of denitration system in cement calcination process: A novel method of deep neural network model predictive control. J Cleaner Prod. 2022;332(1):129970. DOI: 10.1016/j.jclepro.2021.129970.
  23. Yogeswaran M, Ponnambalam SG. Reinforcement learning: Exploration-exploitation dilemma in multi-agent foraging task. Opsearch. 2012;49(3):223-36. DOI: 10.1007/s12597-012-0077-2.
  24. Agostinelli F, Hocquet G, Singh S, Baldi P. From Reinforcement Learning to Deep Reinforcement Learning: An Overview Forest. In: Rozonoer L, Mirkin B, Muchnik I, editors. Braverman Readings in Machine Learning. Vol. 11. Boston, MA, USA: Springer Int Publishing; 2018. pp. 219-354. DOI: 10.1561/2200000071.
  25. Al-Hamadani MNA, Fadhel MA, Alzubaidi L, Balazs H. Reinforcement learning algorithms and applications in healthcare and robotics: A comprehensive and systematic review. Sensors. 2024;24(8):2461. DOI: 10.3390/s24082461.
  26. Aghdasinia H, Hosseini SS, Hamedi J. Improvement of a cement rotary kiln performance using artificial neural network. J Ambient Intelligence Humanized Computing. 2021;12(7):7765-76. DOI: 10.1007/s12652-020-02501-1.
  27. Zermane H, Drardja A. Development of an efficient cement production monitoring system based on the improved random forest algorithm. Int J Adv Manufacturing Technol. 2022;120(3-4):1853-66. DOI: 10.1007/s00170-022-08884-z.
  28. Zermane H, Mouss H. Internet and fuzzy based control system for rotary kiln in cement manufacturing plant. Int J Computat Intelligence Systems. 2017;10(1). DOI: 10.2991/ijcis.2017.10.1.56.
  29. Zhang Y, Cheng H. Research on the innovative path of college English teaching based on deep reinforcement learning. Appl Mathematics Nonlinear Sci. 2024;9(1):1-13. DOI: 10.2478/amns.2023.2.01532.
  30. He Z, Tran KP, Thomassey S, Zeng X, Xu J, Yi C. Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning. J Manufacturing Systems. 2022;62:939-49. DOI: 10.1016/j.jmsy.2021.03.017.
DOI: https://doi.org/10.2478/eces-2025-0021 | Journal eISSN: 2084-4549 | Journal ISSN: 1898-6196
Language: English
Page range: 415 - 426
Published on: Oct 10, 2025
Published by: Society of Ecological Chemistry and Engineering
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Hassina Madjour, Hanane Zermane, Sonia Benaicha, published by Society of Ecological Chemistry and Engineering
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.