CONCEPTUAL MODEL OF FACTORS INFLUENCING THE EFFICIENCY OF GAS PREPARATION AND SEPARATION PROCESS
Abstract
The paper presents the concept of adaptive control in natural gas separation. The control systems used in practice are usually based on fixed control algorithms and do not consider dynamic changes in physical, technical and operational parameters, which leads to a decrease in separation quality and an increase in energy consumption. The article describes the approach of combining system analysis, modeling and the Pareto method. This paper presents a concept for adaptive control in natural gas separation. Control systems used in practice are typically based on fixed control algorithms and do not account for dynamic changes in physical, technical, and operational parameters, which leads to reduced separation quality and increased energy costs. This paper describes an approach combining systems analysis and the Pareto method. The aim of this paper is to develop a concept for adaptive control of the natural gas treatment process based on systems analysis. Recent studies demonstrate that data-driven methods enable more precise parameter adjustment, better responsiveness to raw-gas fluctuations and improved impurity removal efficiency. To achieve this goal, this paper addresses the challenges of identifying and classifying factors affecting the quality and efficiency of gas separation, as well as integrating their relationships within a unified conceptual control model. Particular attention is paid to the impact of precise control of key parameters, such as pressure, temperature, and flow rate, on the efficiency of these processes. An analysis of recent research demonstrates the growing use of neural networks and machine learning models in gas purification for predictive control, anomaly detection, and optimization of operating parameters. A comparative evaluation of classic PID controllers, fuzzy, adaptive, and neural control methods confirms the advantages of intelligent control in terms of stability, adaptability, and energy efficiency. The main result of this study is the substantiation of key factors determining separation efficiency, among which pressure, temperature, and gas flow rate have the greatest impact. The resulting model forms a methodological basis for the development of intelligent and adaptive control systems for gas purification processes.
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