ANFIS Modeling For a Water-Methanol System in a Spiral Plate Heat Exchanger

Abstract

In this paper, an experimental investigation of Water- Methanol system in a Spiral plate Heat Exchanger (SHE) is presented. Experiments have been conducted by varying the mass flow rate of cold fluid (Methanol), the mass flow rate of hot fluid (Water) and inlet temperature of the hot fluid, by keeping inlet temperature of the cold fluid constant. The effects of relevant parameters on the performance of spiral plate heat exchanger are studied. Also, in this paper, an attempt is made to propose Adaptive Network based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models for the analysis of SHE. The data required to train the models are obtained from the experimental data based on Response Surface Methodology (RSM). The ANN models are developed using Back Propagation Network (BPN) algorithm, incorporating Levenberg-Marquardt (L-M) training method. The ANFIS models are developed based on advanced neural- fuzzy technology. The ANFIS model possesses the robustness of fuzzy system, the learning ability of neural networks and can adapt to various situations. The accuracy of the trained networks are verified according to their ability to predict unseen data by minimizing root mean square error (RMSE), average percentage error (APE) and correlation coefficient (%R 2 ) value. The prediction of the parameters can be obtained without using charts and complicated equations. The data obtained from ANFIS and ANN models for overall heat transfer coefficient (U) and pumping power (Wp) are compared with those of experimental data. It is observed that the accuracy between the ANFIS model's predictions, NN model's predictions and experimental values are achieved with minimum % error, APE, RMSE and Correlation coefficient (%R 2 ). Also it is proved that ANFIS models give better performance