ALGORITHM DEVELOPMENT FOR FUME GAS CONTROL SYSTEMS WITH ARTIFICIAL INTELLIGENCE BASED ON FUZZY LOGIC
DOI:
https://doi.org/10.58225/sw.2024.2-159-167Keywords:
Carbon monoxide-carbon monoxide, Logical Control Algorithms, Fuzzification, Fuzzy neural network, training algorithmsAbstract
Many studies have been conducted to apply fuzzy logic-based artificial intelligence (AI) systems to carbon monoxide measurement and control. These systems work with logic algorithms and learning algorithms based on modeling experiences to control the dependence of flue gases on temperature, pressure and other parameters. In these algorithms, logical rules are applied to determine the optimal control strategies, taking into account the behaviors of the flue gases. Logic-based AI systems monitor emissions, analyze sensor data, and determine control actions. These algorithms are used to determine control strategies that maintain flue gas values at optimal levels, increase energy efficiency, and meet safety requirements. Basically, these algorithms are used to apply logic control principles and optimization techniques to improve the efficiency of exhaust gas control systems. This enables higher performance and safer operation.
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Copyright (c) 2024 Khurshudov Dursun, Aliyeva Samira, . Sultanova Konül (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
