摘要
由一名新闻记者-机器人和机器学习每日新闻的工作人员新闻编辑-一项关于机器学习的新研究现在可以获得。根据NewsRx Jour Nalists在希腊塞萨洛尼基的新闻报道，研究表明：“为了应对工业部门余热回收的挑战，本研究提出了一种新的填充床潜热存储系统(PBLHS)的设计和优化框架。该框架具有深度Le Arning(DL)模型，并结合了元启发式算法。”这项研究的财政支持来自欧盟委员会联合研究中心。新闻记者引用了塞萨洛尼基亚里斯多德大学的一篇研究文章:“DL模型是利用经验证的计算流体力学(CFD)模型生成的数据来预测PBLHS性能的。该模型具有良好的性能，r(2)值为0.975，平均绝对百分比误差(<9.14%)较低。为了提高ML模型的效率和优化性能，该模型具有良好的性能。”探索了各种元理论算法。和声搜索算法通过早期筛选成为最有效的算法，并进行了进一步的改进。优化算法通过快速产生设计来证明其能力，与有效的优化实验PBLHS设计相比，总效率提高了85%。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-A new study on Machine Learning is now available. According to news reporting originating in Thessaloniki, Greece, by NewsRx jour nalists, research stated, "To tackle the challenge of waste heat recovery in the industrial sector, this research presents a novel design and optimization frame work for Packed Bed Latent Heat Storage Systems (PBLHS). This features a Deep Le arning (DL) model, integrated with metaheuristic algorithms." Financial support for this research came from European Commission Joint Research Centre. The news reporters obtained a quote from the research from the Aristotle Univers ity of Thessaloniki, "The DL model was developed to predict PBLHS performance, t rained using data generated from a validated Computational Fluid Dynamics (CFD) model. The model exhibited a high performance with an R(2 )value of 0.975 and a low Mean Absolute Percentage Error (<9.14%). T o enhance the ML model's efficiency and optimized performance, various metaheuri stic algorithms were explored. The Harmony Search algorithm emerged as the most effective through an early screening and underwent further refinement. The optim ized algorithm demonstrated its capability by rapidly producing designs that sho wcased an improvement in total efficiency of up to 85% over availa ble optimized experimental PBLHS designs."
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