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

IF 5.1 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress Pub Date : 2024-10-01 DOI:10.1016/j.tsep.2024.102953

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引用次数: 0

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.

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基于神经网络的智能绿色制造过程中热能与信息管理的自适应优化

热能自适应优化研究具有重要的理论和实践意义。利用深度学习神经网络模型，对生产过程中的热能数据进行实时监测和分析。通过构建自适应优化算法，对热能输入和输出进行系统评估和调整，并根据环境变化和生产需求动态优化热能分配方案。同时，引入信息管理系统，实现数据汇总、反馈和决策支持。实验结果表明，与传统管理方法相比，所提出的方法能有效减少热能损耗，优化热能利用率，大幅降低综合能耗。通过实时数据更新，该系统提高了生产过程的灵活性和响应速度，显著提高了生产效率。基于神经网络的热能自适应优化方法为智能绿色制造提供了有效的解决方案，不仅能优化热能管理，还能为其他资源节约和环境保护提供参考。

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来源期刊

Thermal Science and Engineering Progress Chemical Engineering-Fluid Flow and Transfer Processes

CiteScore

7.20

自引率

10.40%

发文量

327

审稿时长

41 days

期刊介绍： Thermal Science and Engineering Progress (TSEP) publishes original, high-quality research articles that span activities ranging from fundamental scientific research and discussion of the more controversial thermodynamic theories, to developments in thermal engineering that are in many instances examples of the way scientists and engineers are addressing the challenges facing a growing population – smart cities and global warming – maximising thermodynamic efficiencies and minimising all heat losses. It is intended that these will be of current relevance and interest to industry, academia and other practitioners. It is evident that many specialised journals in thermal and, to some extent, in fluid disciplines tend to focus on topics that can be classified as fundamental in nature, or are ‘applied’ and near-market. Thermal Science and Engineering Progress will bridge the gap between these two areas, allowing authors to make an easy choice, should they or a journal editor feel that their papers are ‘out of scope’ when considering other journals. The range of topics covered by Thermal Science and Engineering Progress addresses the rapid rate of development being made in thermal transfer processes as they affect traditional fields, and important growth in the topical research areas of aerospace, thermal biological and medical systems, electronics and nano-technologies, renewable energy systems, food production (including agriculture), and the need to minimise man-made thermal impacts on climate change. Review articles on appropriate topics for TSEP are encouraged, although until TSEP is fully established, these will be limited in number. Before submitting such articles, please contact one of the Editors, or a member of the Editorial Advisory Board with an outline of your proposal and your expertise in the area of your review.