application of evolutionary polynomial regression in ultrafiltration systems considering the effect of different parameters on oily wastewater treatment

in the present work, the effects of operating conditions including ph, transmembrane pressure, oil concentration, and temperature on fouling resistance and the rejection of turbidity for a polymeric membrane in an ultrafiltration system of wastewater treatment were studied. a new modeling technique called evolutionary polynomial regression (epr) was investigated. epr is a method based on regression algorithm, which combines the best properties of the conventional numerical regression technique. this paper employs the capability of epr as a powerful tool to develop a formula with a variable number of polynomial coefficients. herein, the evolutionary polynomial regression approach is adopted on two parametric studies, i.e. total fouling resistance and rejection rate. these parameters are all evaluated as a function of some mentioned independent variables. maximum average error and minimum average error are obtained to be equal to 4.25% and 0.05%, respectively. therefore, epr is a practical and useful method to describe a membrane performance.