Modelling dye removal by adsorption onto water treatment residuals using combined response surface methodology-artificial neural network approach.

In this study, response surface methodology (RSM)-artificial neural network (ANN) approach was used to optimise/model disperse dye removal by adsorption using water treatment residuals (WTR). RSM was first applied to evaluate the process using three controllable operating parameters, namely WTR dose, initial pH (pHinitial) and dye concentration, and optimal conditions for colour removal were determined. In the second step, the experimental results of the design data of RSM were used to train the neural network along with a non-controllable parameter, the final pH (pHfinal). The trained neural networks were used for predicting the colour removal. A colour removal of 52.6 ±â€¯2.0% obtained experimentally at optimised conditions (pHinitial 3.0, adsorbent dose 30 g/L and dye concentration 75 mg/L) was comparable to 52.0% and 52.2% predicted by RSM and RSM-ANN, respectively. This study thus shows that optimising/predicting the colour removal process using the RSM-ANN approach is possible, and it also indicates that adsorption onto WTR could be used as a primary treatment for removal of colour from dye wastewater.

Chemical coagulation of greywater: modelling using artificial neural networks.

In the present study, chemical coagulation with alum and polyaluminium chloride (PACl) was utilized for greywater treatment. More than 140 jar tests on greywater with varying characteristics were conducted in order to determine the optimum coagulant dosage and treated greywater characteristics. The average removal efficiencies of turbidity, chemical oxygen demand (COD) and total suspended solids were obtained as 91, 73 and 83% using alum and 93, 74 and 89% using PACl, respectively. For similar initial turbidity levels, optimum PACl dosages required were significantly less compared to optimum alum dosages. Further, PACl produced treated greywater with lower levels of turbidity compared to alum. Results of the coagulation tests were used to design artificial neural network (ANN) models for the prediction of the optimum coagulant dosage and treated greywater quality parameters. ANN models with initial turbidity, pH, conductivity and alkalinity as the input parameters could predict the optimum coagulant dose and treated greywater quality. The performance of the models was found to be good, with correlation coefficient values greater than 0.80. Empirical formulas for the prediction of alum and PACl dosages were also derived using the algorithm weights and bias values from the networks eliminating the need for running the ANN software.