Application of factor analysis in a large‐scale industrial wastewater treatment plant simulation using principal component analysis–artificial neural network hybrid approach: (Case Study: Fajr Industrial Wastewater Treatment Plant, Mahshahr, Iran)

The effective operation of industrial wastewater treatment plants is quite complicated due to having diverse qualitative and quantitative variations in their effluent characteristics during a day. In this article, we take full advantages of well‐known prediction models to acquire an applicable and constructive operation over industrial treatment plants. We combine multilayer perceptron feed forward neural networks with Levenberg–Marquardt training function (Trainlm) and principal component analysis method to estimate pH, chemical oxygen demand, total dissolved solid, Cl − , turbidity, and achieve appropriate operation of Fajr petrochemical industrial treatment plant for the first time in Iran. Moreover, factor analysis approach was applied to determine the paramount input parameters of the models to reduce the parameters' dimension. Mean square error, root mean square error, and correlation coefficient ( R ) were used for evaluating the performance of the models. Results indicate that correlation coefficients ( R ) in the range of 0.8–0.94 showed excellent accuracy of the models in estimating qualitative profile of wastewater. Simulation of a whole treatment plant, better prediction of parameters, and proposing a new hybrid model could be some advantages of this study.