Artificial neural network (ANN) model was applied for predicting the biosorption capacity of excess municipal wastewater sludge for hexavalent chromium (Cr(VI)) ions from aqueous solution. The effects of initial concentration (5 to 90 mg/L), adsorbent dosage (2 to 10 g/L), initial pH (2 to 8), agitation speed (50 to 200 rpm) and agitation time (5 to 480 min) were investigated. The maximum amount of chromium removal was about 96% in optimum conditions. The experimental results were simulated using ANN model. Levenberg-Marquardt algorithm was used for the training of this network with tangent sigmoid as transfer function at hidden and output layer with 13 and 1 neurons, respectively. The applied model successfully predicted Cr(VI) biosorption capacity. The average mean square error is 0.00401 and correlation coefficient between predicted removal rate and experimental results is 0.9833.

中文翻译：

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水溶液中Cr（VI）生物吸附的人工神经网络建模

应用人工神经网络（ANN）模型来预测过量的城市污水污泥对水溶液中六价铬（Cr（VI））离子的生物吸附能力。初始浓度（5至90 mg / L），吸附剂剂量（2至10 g / L），初始pH（2至8），搅拌速度（50至200 rpm）和搅拌时间（5至480分钟）的影响被调查了。在最佳条件下，铬的最大去除量约为96％。实验结果采用人工神经网络模型进行仿真。使用Levenberg-Marquardt算法以正切S型曲线作为隐函数和输出层分别具有13和1个神经元的传递函数来训练该网络。应用模型成功地预测了六价铬的生物吸附能力。平均均方误差为0。