Optimal parameters input parameters by nftool-GA.

In this study, shell and heat exchangers are optimized using an integrated optimization framework. In this research, A structured Design of Experiments (DOE) comprising 16 trials was first conducted to systematically determine the essential parameters, including mass flow rates (mh, mc), temperatures (T1, t1, T2, t2), and heat transfer coefficients (€, TR, U). By identifying the first four principal components, PCA was able to determine 87.7% of the variance, thereby reducing the dimensionality of the problem. Performance-related aspects of the system are the focus of this approach. Key outcomes (€, TR, U) were predicted by 99% R-squared using the RSM models. Multiple factors, such as the mass flow rate and inlet temperature, were considered during the design process. The maximizing efficiency, thermal resistance, and utility were achieved by considering these factors. By using genetic algorithms, Pareto front solutions that meet the requirements of decision-makers can be found. The combination of the shell and tube heat exchangers produced better results than expected. Engineering and designers can gain practical insight into the mass flow rate, temperature, and key responses (€, TR, U) if they quantify improvements in these factors. Despite the importance of this study, it has several potential limitations, including specific experimental conditions and the need to validate it in other situations as well. Future research could investigate other factors that influence system performance. A holistic optimization framework can improve the design and engineering of heat exchangers in the future. As a result of the study, a foundation for innovative advancements in the field has been laid with tangible improvements. The study exceeded expectations by optimizing shell and heat exchanger systems using an integrated approach, thereby contributing significantly to the advancement of the field.

本研究采用集成优化框架对管壳式换热器开展优化设计。本研究首先开展了包含16组试验的结构化实验设计（Design of Experiments, DOE），以系统确定核心参数，包括质量流量（mh、mc）、温度（T1、t1、T2、t2）以及换热系数（€、TR、U）。通过提取前四个主成分，主成分分析（Principal Component Analysis, PCA）可解释87.7%的总方差，从而实现问题的降维处理。该研究方法聚焦于系统的性能相关维度。借助响应面法（Response Surface Methodology, RSM）模型，关键输出指标（€、TR、U）的预测决定系数（R²）可达99%。设计过程中纳入了质量流量、入口温度等多项影响因素，通过考量上述因素可实现换热效率、热阻与热负荷的最大化。借助遗传算法，可求解出符合决策者需求的帕累托前沿（Pareto front）解集。管壳式换热器的集成优化方案取得了优于预期的优化效果。工程人员与设计师可通过量化上述因素的改进幅度，获取关于质量流量、温度以及关键响应指标（€、TR、U）的实用设计启示。尽管本研究具有重要学术与工程价值，但仍存在若干潜在局限，例如仅针对特定实验条件开展，且需在其他工况下进行验证。未来研究可拓展探究更多影响系统性能的潜在因素。后续可依托全流程集成优化框架，进一步优化换热器的设计与工程应用流程。本研究可为该领域的创新发展奠定坚实基础，并带来切实的技术改进。本研究通过集成优化方法实现了换热器系统的高效优化，达成了超出预期的研究成果，为该领域的发展作出了重要贡献。