Modeling and estimation of fouling factor on the hot wire probe by smart paradigms

Abstract

The fouling factor is a complex non-linear function of several feature variables. Accurate estimation of this measurable factor is applicable for controlling the heat transfer efficiency of heat exchangers. Artificial neural network (ANN) and adaptive neural-based fuzzy inference system (ANFIS) techniques are accurate approaches for the understanding treatment of the most complicated systems. The cascade-feedforward (CFF), multi-layer perceptron (MLP), radial basis function (RBF), and generalized regression (GR) neural networks and ANFIS were applied for predicting the fouling factor of the hot wire probe from some measured variables using a huge databank, including 1870 empirical dataset. Pearson's and Spearman's techniques confirmed the highest relation between considered features and the first order of fouling factor. The results demonstrate that the cascade feed-forward neural network containing eight hidden neurons was the best artificial intelligence (AI) model with excellent overall AARE = 3.44 %, MSD = 0.0000315, RMSD = 0.0056, and R2 = 0.9982. This work's significance lies in presenting an applicable and accurate tool for estimating fouling factors on hot wire probes to the research community and industry. This model can be considered a reliable replacement for empirical analyses that are often expensive and time-consuming.