AP08856867 – Development and testing of intelligent algorithms for optimal control of the technological process of purification of yellow phosphorus in the conditions of the NDPP

Objective of the project: The purpose of this project is to develop and test intelligent algorithms for optimal control of technological processes for the purification of phosphorus from arsenic and organic matter in the industrial conditions of the NDPP.

Relevance: Development of an optimal control model for the technological process of purification of yellow phosphorus from arsenic and organic impurities based on the knowledge, experience and intuition of process operators, instead of creating a mathematical description of the technological processes themselves. This will significantly reduce the development time for optimal control algorithms for the phosphorus purification process, improve the adequacy of the mathematical control model (control algorithm) and thereby increase the efficiency and safety of the control system being created..

Scientific adviser: Doctor of technical sciences, Professor, Batyrbek Suleimenov

Results obtained: Within the research work, a methodology for expert selection was developed and technological processes of yellow phosphorus settling and purification were analyzed. Intelligent control models and algorithms based on fuzzy, neuro-fuzzy approaches and neural networks were designed to describe settling, sludge dewatering, and arsenic removal processes. Experimental modeling using full factorial design was carried out to identify key process variables and parameters. The developed models were validated in terms of adequacy, stability, sensitivity, and uniqueness, confirming their consistency with physico-chemical principles. The proposed algorithms were implemented in software and deployed in the NS 900 PLC controller using the OPC DA standard for industrial data exchange. Industrial trials demonstrated an increase in product yield and improvement in product quality. The results confirm the effectiveness of the proposed intelligent control approach.

List of publications with links to them

1. Kulakova Ye.A., Suleimenov B.A. Development and research of intelligent algorithms for controlling the process of ore jigging // International Journal of Emerging Trends in Engineering Research. – 2020. – Vol. 8, No. 9. – P. 6240–6246. – DOI: https://doi.org/10.30534/iieter/2020/214892020
2. Makhanbet M., Lv T., Orynbet M., Suleimenov B. A fully distributed and clustered learning of power control in user-centric ultra-dense HetNets // IEEE Transactions on Vehicular Technology. – 2020. – Early Access. – P. 1–1. – DOI: https://doi.org/10.1109/TVT.2020.3013329
3. Toktassynova N., Fourati H., Suleimenov B. Application of grey system theory to phosphorite sinter process: from modeling to control // Asian Journal of Control. – 2021. – Vol. 23, No. 1. – P. 13–22. – DOI: https://doi.org/10.1002/asjc.2348
4. Kulakova Ye.A., Wójcik W., Suleimenov B., Smolarz A. Comparison of intelligent control methods for the ore jigging process // International Journal of Electronics and Telecommunications. – 2021. – Vol. 67, No. 3.
5. Toktassynova N., Suleimenov B.A., Kulakova Ye.A. Modeling of phosphorus production processes and developing a management structure based on grey systems // Energy- and resource-saving technologies of developing the raw-material base of mining regions. – Multi-authored monograph. – Petroșani: UNIVERSITAS Publishing, 2021. – P. 239–275. – ISBN 978-973-741-733-6. – DOI: https://doi.org/10.31713/m1001
6. Batayev N., Suleimenov B., Batayeva S. Centrifugal compressor anti-surge control system modelling // International Journal of Electrical and Computer Engineering. – 2022. – Vol. 12, No. 2. – P. 1419–1428. – DOI: https://doi.org/10.11591/ijece.v12i2.pp1419-1428
7. Suleimenov B.A., Doshtaev B.Zh., Beisembayuly S. Intelligent algorithms for controlling yellow phosphorus purification processes: monograph. – Almaty: Shikula, 2022. – 109 p. – ISBN 978-601-323-316-1.