Department of Biology, Faculty of Art and Science, University of Gaziantep, 27310 Gaziantep, Turkey.
Bioresour Technol. 2012 Jan;103(1):64-70. doi: 10.1016/j.biortech.2011.09.106. Epub 2011 Oct 1.
An artificial neural network (ANN) model was used to predict removal efficiency of Lanaset Red (LR) G on walnut husk (WH). This adsorbent was characterized by FTIR-ATR. Effects of particle size, adsorbent dose, initial pH value, dye concentration, and contact time were investigated to optimize sorption process. Operating variables were used as the inputs to the constructed neural network to predict the dye uptake at any time as an output. Commonly used pseudo second-order model was fitted to the experimental data to compare with ANN model. According to error analyses and determination of coefficients, ANN was the more appropriate model to describe this sorption process. Results of ANN indicated that pH was the most efficient parameter (43%), followed by initial dye concentration (40%) for sorption of LR G on WH.
采用人工神经网络 (ANN) 模型预测 Lanaset Red (LR) G 在核桃壳 (WH) 上的去除效率。该吸附剂的特点是 FTIR-ATR。考察了粒径、吸附剂用量、初始 pH 值、染料浓度和接触时间对吸附过程的影响，以优化吸附过程。将操作变量作为输入到所构建的神经网络中，以预测任何时间的染料吸收作为输出。常用的拟二级模型拟合实验数据，与 ANN 模型进行比较。根据误差分析和系数的确定，ANN 是更适合描述该吸附过程的模型。ANN 的结果表明，pH 值是最有效的参数 (43%)，其次是初始染料浓度 (40%)，用于 WH 上 LR G 的吸附。
