Multi-objective optimization and performance assessment of response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy interfence system (ANFIS) for estimation of fouling in phosphoric acid/steam heat exchanger

Fouling is a common occurrence in industrial heat exchangers, leading to a decrease of the thermal efficiency. This research addresses the challenge of applying various machine learning algorithms including Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to effectively model the fouling resistance in a heat exchanger in phosphoric acid concentration loop. Subsequently, a multi -objective optimization approach was employed to minimize the fouling resistance. Confirmatory experiments are then conducted in the phosphoric acid concentration plant using optimized variables. The findings of this study indicate that the three models accurately align with one year of operational data, achieving a remarkably high coefficient of correlation (R). Among the models utilized, RSM demonstrates the highest level of prediction accuracy, with an R of 0.9998, accompanied by the lowest mean square error (MSE) of 5.9388 10 -13 and root mean squared error (RMSE) of 7.7064 10 -7 . The RSM optimization process identifies the optimal conditions for variables such as time, acid inlet and outlet temperature, steam temperature, acid density, and volume flow rate, which are determined to be 114.805 h; 72.214 degrees C; 80.407 degrees C; 116.784 degrees C; 1642.47 kg/m 3 ; and 2308.1 m 3 /h, respectively. The predicted fouling resistance demonstrates a strong correlation with the actual data, with a negligible percentage difference of 2.19 %, further validating the accuracy and reliability of the RSM model.