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

This paper introduces an innovative methodology that integrates whales optimization (WO) with adaptive neuro-fuzzy inference systems (ANFIS) to develop the forward and inverse model of shell and tube heat exchanger (STHE). The accurate modeling of STHE performance is critical for optimizing design parameters and operational efficiency across various industrial applications. Drawing inspiration from the coordinated hunting behavior of whales, WO is applied to update the weights from layer to layer in neural network part of ANFIS. This paper validates the effectiveness of the WO-based ANFIS approach, showcasing its superiority over conventional modeling techniques. This innovative fusion of WO and ANFIS holds immense promise for accurate forward and inverse model.