Kocaeli University, Chemistry Department, Kocaeli, Turkey.
Bioresour Technol. 2012 May;112:111-5. doi: 10.1016/j.biortech.2012.02.084. Epub 2012 Feb 24.
In this study, Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were employed to develop an approach for the evaluation of heavy metal biosorption process. A batch sorption process was performed using Nigella sativa seeds (black cumin), a novel and natural biosorbent, to remove lead ions from aqueous solutions. The effects of process variables which are pH, biosorbent mass, and temperature, on the sorbed amount of lead were investigated through two-levels, three-factors central composite design (CCD). Same design was also utilized to obtain a training set for ANN. The results of two methodologies were compared for their predictive capabilities in terms of the coefficient of determination-R(2) and root mean square error-RMSE based on the validation data set. The results showed that the ANN model is much more accurate in prediction as compared to CCD.
在这项研究中，响应面法（RSM）和人工神经网络（ANN）被用来开发一种评价重金属生物吸附过程的方法。采用黑种草种子（黑孜然）作为一种新型天然生物吸附剂，通过间歇吸附过程从水溶液中去除铅离子。通过两水平、三因素中心复合设计（CCD）研究了过程变量（pH、生物吸附剂质量和温度）对铅吸附量的影响。同样的设计也被用于 ANN 的训练集。根据验证数据集，通过决定系数-R（2）和均方根误差-RMSE 比较了两种方法在预测能力方面的结果。结果表明，与 CCD 相比，ANN 模型在预测方面更加准确。
