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.

中文翻译：

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支持向量回归：一种用于预测废水中镉去除量的新型软计算技术

来自世界各地的工业废水中有毒重金属的存在备受关注。在当前的工作中，一种新颖的软计算技术支持向量回归（SVR）技术已被用于预测以农业废料“稻米抛光”为低成本吸附剂的废水中镉离子的去除，其接触时间，初始吸附物浓度，介质的p H和温度作为独立参数。基于统计参数（例如确定系数（R ²）），将已开发的基于SVR的模型与广泛使用的多元回归（MR）模型进行了比较），平均相对误差（AARE）等。与MR模型相比，基于SVR的模型具有较低的AARE值（0.67％）和较高的R ²值（0.9997 ），预测性能更加准确和通用。AARE值为29.27％，其确定系数（R ²）为0.2161 。此外，还已经观察到，SVR模型可以有效预测在各种实验条件下从废水中去除镉离子的复杂相互作用过程的行为。