Support vector regression: A novel soft computing technique for predicting the removal of cadmium from wastewater
The presence of toxic heavy metals in the wastewater coming from industries is of great concern across the world. In the present work, a novel soft computing technique support vector regression (SVR)technique has been used to predict the removal of cadmium ions from wastewater with agricultural waste ‘rice polish’ as a low-cost adsorbent, with contact time, initial adsorbate concentration, pH of the medium, and temperature as the independent parameters. The developed SVR-based model has been compared with the widely used multiple regression (MR) model based on the statistical parameters such as coefficient of determination (R2), average relative error (AARE) etc. The prediction performance of SVR-based model has been found to be more accurate and generalized in comparison to MR model with low AARE values of 0.67% and high R2 values of 0.9997 while MR model gives an AARE value of 29.27% and 0.2161 as coefficient of determination (R2). Furthermore, it has also been observed that the SVR model effectively predicts the behavior of the complex interaction process of cadmium ions removal from waste water under various experimental conditions.
Heavy metals; Low cost adsorbent; Support vector regression (SVR); Coefficient of determination (R2); Average relative error (AARE)
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