Adaptive optimization of thermal energy and information management in intelligent green manufacturing process based on neural network

Abstract

The study of thermal energy adaptive optimization has important theoretical and practical significance. The deep learning neural network model is used to monitor and analyze the thermal energy data in the manufacturing process in real time. Through the construction of adaptive optimization algorithm, the heat input and output are systematically evaluated and adjusted, and the heat distribution scheme is dynamically optimized according to environmental changes and production needs. At the same time, information management system is introduced to realize data summary, feedback and decision support. The experimental results show that the proposed method can effectively reduce the heat energy loss, optimize the heat energy utilization, and significantly reduce the overall energy consumption compared with traditional management methods. With real-time data updates, the system improves the flexibility and responsiveness of the production process, significantly improving manufacturing efficiency. The thermal energy adaptive optimization method based on neural network provides an effective solution for intelligent green manufacturing, which can not only optimize thermal energy management, but also provide a reference for other resource conservation and environmental protection.