Downstream Processing Laboratory, Department of Biotechnology, Kumaraguru College of Technology, Coimbatore 641 049, India.
Bioresour Technol. 2013 Nov;148:550-9. doi: 10.1016/j.biortech.2013.08.149. Epub 2013 Sep 3.
In the present work, the evaluation capacities of two optimization methodologies such as RSM and ANN were employed and compared for predication of Cr(VI) uptake rate using defatted pongamia oil cake (DPOC) in both batch and column mode. The influence of operating parameters was investigated through a central composite design (CCD) of RSM using Design Expert 8.0.7.1 software. The same data was fed as input in ANN to obtain a trained the multilayer feed-forward networks back-propagation algorithm using MATLAB. The performance of the developed ANN models were compared with RSM mathematical models for Cr(VI) uptake rate in terms of the coefficient of determination (R(2)), root mean square error (RMSE) and absolute average deviation (AAD). The estimated values confirm that ANN predominates RSM representing the superiority of a trained ANN models over RSM models in order to capture the non-linear behavior of the given system.
在本工作中，采用了两种优化方法，即响应面法（RSM）和人工神经网络（ANN），并对其进行了评价和比较，以预测使用脱脂麻疯树饼（DPOC）在批量和柱式两种模式下对六价铬（Cr(VI)）的吸附速率。通过 Design Expert 8.0.7.1 软件中的中心复合设计（CCD）对 RSM 的操作参数进行了研究。将相同的数据作为输入输入到 ANN 中，使用 MATLAB 获得训练有素的多层前馈网络反向传播算法。根据决定系数（R(2)）、均方根误差（RMSE）和绝对平均偏差（AAD），将开发的 ANN 模型的性能与 RSM 数学模型进行了比较。估计值证实，ANN 优于 RSM，代表训练有素的 ANN 模型优于 RSM 模型，以捕捉给定系统的非线性行为。
