METHODS FOR INTELLIGENT MODELING OF INDUSTRIAL TECHNOLOGICAL SYSTEMS AND OPTIMIZATION OF REAL-TIME CONTROL STRATEGIES
DOI:
https://doi.org/10.47390/ts-v3i12y2025N04Keywords:
artificial intelligence, IoT, Industry 4.0, intelligent modeling, real-time control, Model Predictive Control, neural networks, digital twin, chemical industry, oil and gas industry, forecasting, optimization, data-driven model.Abstract
This article discusses methods for intelligent modeling of industrial technological systems and the optimization of real-time control strategies. Within the framework of the Industry 4.0 concept, the theoretical and practical foundations for integrating advanced technologies such as artificial intelligence, the Internet of Things (IoT), digital twins, neural networks, and Model Predictive Control (MPC) into industrial processes are presented. Special attention is paid to modeling complex chemical and petrochemical systems using both physics-based and data-driven approaches. The paper provides examples of implementing smoothing, forecasting, and optimization algorithms using Python code, graphs, and mathematical models. In addition, real-time optimization, the role of digital twins, and reinforcement learning technologies are analyzed as key stages in the transition of industry towards autonomous control systems.
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