Modeling of Shell and Tube Heat Exchanger Using Adaptive Neuro-fuzzy Inference System and Nature-Inspired Whales Optimization Algorithm

This paper presents a novel approach by integrating Whales Optimization (WO) with Adaptive Neuro-fuzzy Inference Systems (ANFIS) to construct both forward and inverse models for shell and tube heat exchangers (STHE). Accurate modeling of STHE performance is pivotal for optimizing design parameters and operational efficiency in diverse industrial applications. Inspired by the cooperative hunting behavior of whales, WO is employed to update weights in the neural network layer of ANFIS. The study validates the efficacy of the WO-based ANFIS methodology, demonstrating its superiority over conventional modeling techniques. This innovative fusion of Whales Optimization and ANFIS holds great potential for achieving precise forward and inverse modeling of STHE, offering a promising avenue for advancements in this critical aspect of heat exchanger optimization.