Design optimization of plate heat exchanger by utilizing artificial intelligence

The plate heat exchangers are components that provide heat transfer between two or more mediums; therefore, they have crucial importance in the industry. While the plate heat exchangers are produced, sheet metal forming process is employed. Growing interest in manufacturing lightweight, cost effective, and robust products in HVACR industry motivates the search for compact plate heat exchangers used in combi-boilers. Lightweight compact plate heat exchangers distinguish them from other plate heat exchangers in case of raw materials shortage periods. However, manufacturing lightweight plate heat exchangers requires time consuming simulation works and significant investment for production trials. On the other hand, implementation of machine learning assisted optimization process has been proved promising and reasonable alternative for traditional production methods which inspired current work: "design optimization of plate heat exchangers by utilizing artificial intelligence" that satisfies requirements stated in material order specifications of Bosch Home Comfort. In this work, an extensive dataset was constructed from finite element analysis simulations on sheet metal forming process by considering the design limits of compact plate heat exchanger produced in Bosch Home Comfort Manisa Plant to train multilayer feed-forward neural network algorithm. The maximum thinning amount of metal plates after forming process was predicted by using optimized neural network algorithm. Six cases with desired outputs were selected to validate neural network algorithm predictions. The demonstrated thinning amounts verify the success of machine learning algorithm in design optimization of plate heat exchangers.