Application of Machine Learning in Thermodynamic System Modeling and Optimization

With the growing demand for energy and increasing environmental concerns, accurate modeling and efficient optimization of thermodynamic systems have become key research areas in engineering.In recent years, the application of machine learning techniques in thermodynamic systems has demonstrated significant potential, greatly improving modeling precision and optimization efficiency.However, existing methods face challenges in handling temperature delay phenomena, limiting the accuracy and effectiveness of system modeling and control.This paper introduces a temperature delay identification algorithm to enhance the accuracy of delay representation.Additionally, it develops a predictive control model for thermodynamic systems that incorporates temperature delay.These two components aim to improve the precision and efficiency of thermodynamic system modeling, providing new insights and methods for the intelligent management of complex thermodynamic systems.