A Genetic Algorithm and Fuzzy Neural Network-Based Intelligent Temperature Control Decision Model for Coagulation Cooling Systems

Abstract

The coagulation cooling system is a common key component in many industrial processes, and reasonable temperature control is crucial. However, due to the complexity of the coagulation cooling system, traditional temperature control methods often cannot achieve optimal performance. To solve this problem, we design an intelligent temperature control decision model by a combination of genetic algorithm and fuzzy neural network. The study firstly utilizes genetic algorithm to optimize the objective function and constraint conditions of the coagulation cooling PID system. At the same time, fuzzy neural network is fused with genetic algorithm to establish a dedicated T-S/2 neural network structure, completing the complete model design of this study. Finally, cooling efficiency, task completion rate and model stability analysis are evaluated on real-world datasets. To validate the proposed model, an example of a coagulation cooling system was constructed in the laboratory and compared with traditional temperature control methods. The experimental results show that the proposal can significantly improve the performance of temperature control and reduce energy consumption under different conditions. In addition, the proposal has the characteristics of adaptability and optimization performance, and can effectively achieve optimal temperature control in uncertain and complex environments.