A comparative study and combined application of RSM and ANN in adsorptive removal of diuron using biomass ashes

Biomass ashes like rice husk ash (RHA), bagasse fly ash (BFA), were used for aqueous phase removal of a pesticide, diuron. Response surface methodology (RSM) and artificial neural network (ANN) were successfully applied to estimate and optimize the conditions for the maximum diuron adsorption using biomass ashes. The effect of operational parameters such as initial concentration (10-30 mg/L); contact time (0.93-16.07 h) and adsorbent dosage (20-308 mg) on adsorption were studied using central composite design (CCD) matrix. Same design was also employed to gain a training set for ANN. The maximum diuron removal of 88.95 and 99.78% was obtained at initial concentration of 15 mg/L, time of 12 h, RHA dosage of 250 mg and at initial concentration of 14 mg/L, time of 13 h, BFA dosage of 60 mg respectively. Estimation of coefficient of determination (R-2) and mean errors obtained for ANN and RSM (R-RHA(2) = 0.976, R-BFA(2) = 0.943) proved ANN (R-RHA(2) = 0.997, R-BFA(2) = 0.982) fits better. By employing RSM coupled with ANN model, the qualitative and quantitative activity relationship of experimental data was visualized in three dimensional spaces. The current approach will be instrumental in providing quick preliminary estimations in process and product development.

Automated summary

In this study, biomass ashes such as RHA and BFA were utilized for the aqueous phase removal of diuron, a pesticide. Response surface methodology (RSM) and artificial neural network (ANN) were employed to estimate and optimize the conditions for maximum diuron adsorption using biomass ashes. The operational parameters' effects on adsorption were studied through central composite design (CCD) matrix, and the ANN model was found to fit better than RSM for this process.