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All Journal IAES International Journal of Robotics and Automation (IJRA) Bulletin of Electrical Engineering and Informatics
Suieuova, Nabat
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Automotive Engineering Electrical & Electronics Engineering

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Construction of automated optimal control systems with elements of artificial intelligence Tsen, Khu Ven; Umarova, Zhanat; Kozhabekova, Pernekul; Suieuova, Nabat
IAES International Journal of Robotics and Automation (IJRA) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v12i4.pp365-372

Abstract

This article examines the widespread introduction of artificial intelligence technologies, means of their implementation and support as a determining factor in the development of scientific and technological progress. On this basis, various advanced objects of various functional purposes are created, which are characterized as “smart”. Their distinguishing feature is the ability to implement a “reasonable” way of functioning, taking into account the prevailing circumstances. This ability is expressed in the fact that in the object automatically, i.e., without human participation, or with minimal human participation, the most rational or optimal modes of functioning are supported, the definition of which involves the performance of operations containing signs of rational activity. This “smart” behavior of technical objects is mainly determined by the “intelligent” functioning of the control systems built into them. In particular, intelligent automated systems for optimal control. In accordance with this, the development of new approaches and methods that expand the possibilities of building such control systems should be considered as an urgent and priority task.
Integration of genetic algorithm and mesoscopic modeling for the optimization of membrane separation processes Umarova, Zhanat; Makhanova, Zlikha; Zhumatayev, Nurlybek; Kopzhassarova, Asylzat; Suieuova, Nabat; Imanbayeva, Aigul; Yegenova, Aliya
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8268

Abstract

This article is dedicated to the development of an innovative approach to optimizing membrane separation processes. The paper introduces the integration of a genetic algorithm (GA) and mesoscopic modeling to enhance the efficiency and accuracy of process parameter optimization. The GA is employed for evolutionary search of optimal parameters, such as pressure, temperature, and membrane material characteristics. The use of evolutionary principles allows for efficient exploration of parameter space, identifying optimal solutions. Mesoscopic modeling serves as a tool for detailed analysis and visualization of membrane separation processes. It involves modeling the interaction of molecules with the membrane surface, enabling a more accurate consideration of the physicochemical aspects of the process. The integration of the GA and mesoscopic modeling creates a unique tool for membrane separation process optimization. The developed approach contributes not only to improving component separation efficiency but also to minimizing energy consumption. The method presented in the article has been successfully tested on model membrane process systems and demonstrated significant improvements compared to traditional optimization methods. The research results confirm the potential of the proposed approach for application in membrane technology industries, opening new perspectives in the field of separation process optimization.
Co-Authors Imanbayeva, Aigul Kopzhassarova, Asylzat Kozhabekova, Pernekul Makhanova, Zlikha Tsen, Khu Ven Umarova, Zhanat Yegenova, Aliya Zhumatayev, Nurlybek