Empirical investigation of the heat exchanger.

A shell and tube heat exchanger (STHE) for heat recovery applications was studied to discover the intricacies of its optimization. To optimize performance, a hybrid optimization methodology was developed by combining the Neural Fitting Tool (NFTool), Particle Swarm Optimization (PSO), and Grey Relational Analysis (GRE). STHE heat exchangers were analyzed systematically using the Taguchi method to analyze the critical elements related to a particular response. To clarify the complex relationship between the heat exchanger efficiency and operational parameters, grey relational grades (GRGs) are first computed. A forecast of the grey relation coefficients was then conducted using NFTool to provide more insight into the complex dynamics. An optimized parameter with a grey coefficient was created after applying PSO analysis, resulting in a higher grey coefficient and improved performance of the heat exchanger. A major and far-reaching application of this study was based on heat recovery. A detailed comparison was conducted between the estimated values and the experimental results as a result of the hybrid optimization algorithm. In the current study, the results demonstrate that the proposed counter-flow shell and tube strategy is effective for optimizing performance.本研究针对应用于热回收场景的壳管式换热器（shell and tube heat exchanger, STHE）展开，旨在探究其优化过程中的复杂细节。为优化其换热性能，本研究结合神经拟合工具（Neural Fitting Tool, NFTool）、粒子群优化（Particle Swarm Optimization, PSO）与灰色关联分析（Grey Relational Analysis, GRE），构建了一套混合优化方法。本研究采用田口方法（Taguchi method）对壳管式换热器进行系统分析，以识别与特定响应指标相关的关键影响要素。为厘清换热器效率与运行参数间的复杂关联，研究首先计算了灰色关联度（grey relational grades, GRGs）。随后借助神经拟合工具对灰色关联系数进行预测，以进一步揭示系统的复杂运行特性。通过粒子群优化分析，最终得到具备最优灰色关联系数的参数组合，进而提升了灰色关联度数值，并优化了换热器的整体性能。本研究的核心应用场景聚焦于热回收领域，具备显著的工程推广价值。依托所提出的混合优化算法，本研究对模型预测值与实验实测结果展开了详细的对比验证。本研究结果证实，所提出的逆流壳管式换热方案可有效实现换热器性能优化。