Department of Mechanical Engineering, Annamacharya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India.
Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India.
PLoS One. 2024 Jun 10;19(6):e0304097. doi: 10.1371/journal.pone.0304097. eCollection 2024.
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.
在这项研究中，使用集成优化框架对壳式和换热器进行了优化。在本研究中，首先进行了一个包含 16 次试验的结构化试验设计（DOE），以系统地确定基本参数，包括质量流量（mh、mc）、温度（T1、t1、T2、t2）和传热系数（€、TR、U）。通过确定前四个主成分，PCA 能够确定 87.7%的方差，从而降低了问题的维度。该方法关注与系统性能相关的方面。使用 RSM 模型，通过 99%的 R-squared 预测了关键输出（€、TR、U）。在设计过程中考虑了多个因素，如质量流量和入口温度。通过考虑这些因素，可以实现效率、热阻和效用的最大化。通过使用遗传算法，可以找到满足决策者要求的 Pareto 前沿解决方案。壳管式换热器的组合产生了比预期更好的结果。如果工程和设计人员能够量化这些因素的改进，他们可以获得有关质量流量、温度和关键响应（€、TR、U）的实际见解。尽管这项研究很重要，但它也有几个潜在的限制，包括特定的实验条件和在其他情况下验证它的必要性。未来的研究可以调查影响系统性能的其他因素。一个整体的优化框架可以在未来提高换热器的设计和工程水平。通过采用集成方法对壳式和换热器系统进行优化，该研究取得了超出预期的成果，为该领域的创新进步奠定了基础。
