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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Utepbergenov, Irbulat
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Computer Science & IT Electrical & Electronics Engineering

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Modernizing quality management with formal languages and neural networks Utepbergenov, Irbulat; Toibayeva, Shara
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4031-4042

Abstract

This paper explores the integration of formal languages and neural networks into quality management systems to enhance efficiency and sustainability. Formal languages standardize regulatory documents, reducing misinterpretation and simplifying modification, contributing to innovative infrastructure (SDG 9). Recurrent neural networks (RNNs) automate document analysis, non-conformance detection, and decision-making, improving production efficiency and promoting responsible consumption (SDG 12). Automation in quality management reduces costs, enhances competitiveness, and aligns with decent work and economic growth (SDG 8). Standardizing documentation and automating quality control enhance workforce competencies and support quality education (SDG 4). These technologies strengthen regulatory transparency, reduce legal risks, and improve governance, supporting strong institutions (SDG 16). The proposed approach fosters sustainable development through digitalization and automation, ensuring efficiency, innovation, and compliance with environmental and social standards.
Thermal mode modeling using neural network technologies and the finite element method Mussabekov, Nazarbek; Utepbergenov, Irbulat; Kaliyev, Zhanybek; Issayeva, Zhazira; Ybytayeva, Galiya; Ansabekova, Gulbakyt; Karnakova, Gayni; Butabaeva, Karlygash
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11268

Abstract

This study presents the analysis and modeling of the thermal regime of a furnace lining at an industrial copper smelting facility using a combined approach based on neural network (NN) technologies and the finite element method (FEM). Experimental temperature data were collected from a laboratory setup equipped with three thermocouples (TP-2488/1 and TCRosemount 0065), with a sampling frequency of 1 Hz over a total duration of 5 hours, resulting in 18,000 measurement points. The measurement uncertainty of the thermocouples did not exceed ±1.5 °C. These data were used both for model development and for validating the numerical FEM simulations. A feedforward neural network was trained using 70% of the dataset, while 15% and 15% were used for validation and testing, respectively. The prediction error of the neural network remained within 3% with a 95% confidence interval of [2.6%, 3.4%]. The results show that the proposed hybrid approach improves temperature prediction accuracy and reduces static control error by 15% when combined with a proportional-integral controller. The methodology demonstrates significant potential for improving thermal process stability and reducing energy consumption in high-temperature metallurgical systems.
Co-Authors Ansabekova, Gulbakyt Butabaeva, Karlygash Issayeva, Zhazira Kaliyev, Zhanybek Karnakova, Gayni Mussabekov, Nazarbek Toibayeva, Shara Ybytayeva, Galiya