Department of Biology, Faculty of Art and Science, University of Gaziantep, 27310 Gaziantep, Turkey.
Bioresour Technol. 2013 Feb;129:396-401. doi: 10.1016/j.biortech.2012.11.085. Epub 2012 Nov 29.
Artificial neural network (ANN), pseudo second-order kinetic, and gene expression programming (GEP) models were constructed to predict removal efficiency of Lanaset Red G (LR G) using lentil straw (LS) based on 1152 experimental sets. The sorption process was dependent on adsorbent particle size, pH, initial dye concentration, and contact time. These variables were used as input to construct a neural network for prediction of dye uptake as output. ANN was an excellent model because of the lowest error and the highest coefficient values. ANN indicated that initial dye concentration had the strongest effect on dye uptake, followed by pH. The GEP model successfully described the sorption kinetic process as function of adsorbent particle size, pH, initial dye concentration, and contact time in a single equation. Low cost adsorbent, LS, had a great potential to remove LR G as an eco-friendly process, which was well described by GEP and ANN.
人工神经网络 (ANN)、伪二阶动力学和基因表达编程 (GEP) 模型被构建用于基于小扁豆秸秆 (LS) 预测 Lanaset Red G (LR G) 的去除效率，该模型基于 1152 组实验数据。吸附过程取决于吸附剂颗粒大小、pH 值、初始染料浓度和接触时间。这些变量被用作输入，以构建神经网络来预测染料的吸收作为输出。由于具有最低的误差和最高的系数值，ANN 是一个出色的模型。ANN 表明，初始染料浓度对染料吸收的影响最大，其次是 pH 值。GEP 模型成功地以单个方程的形式描述了吸附动力学过程，该方程与吸附剂颗粒大小、pH 值、初始染料浓度和接触时间有关。LS 作为一种环保的低成本吸附剂，具有去除 LR G 的巨大潜力，这一点被 GEP 和 ANN 很好地描述了。
