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Modeling and evaluation of electro-oxidation of dye wastewater using artificial neural networks

机译：人工神经网络对染料废水电氧化的建模与评估

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Treatment of CBSOL LE red wool dye containing wastewater by an electro- oxidation (EO) method was investigated using Ti/RuO2 electrode. The performance of the EO system was evaluated in terms of % dye degradation (Y-1) and % colour removal (Y-2) along with important operating cost parameters such as energy consumed (Y-3) at three EO process parameters: pH, current (i) and time (t). ANNs were applied for the modeling of the EO process, and optimization was performed by using multi response optimization by desirability function approach of Central composite Design (CCD) with stimulated data obtained from ANNs. Modeling for the treatment of CBSOL LE red wool dye wastewater by the EO process was done successfully by ANNs, and optimization by CCD vividly underscores interactions between variables and their effects for the degradation of CBSOL LE red wool dye by the EO process. At the optimum conditions, the actual % dye degradation (Y-1), % color removal (Y-2) and energy consumed (Y-3) were 89.87%, 96.71%, 2.029 Wh respectively. The predictions agree well with the experimental results. It was found that both the mechanisms of EO treatment i.e. direct oxidation and indirect oxidation are responsible for the dye degradation/color removal. Color was found to be nearly completely removed, whereas 10.13% of dye is present in the treated wastewater.
机译：研究了使用Ti / RuO2电极通过电氧化（EO）方法处理含CBSOL LE红羊毛染料的废水。根据染料降解率（Y-1）和脱色率（Y-2）以及重要的运营成本参数（如在三个EO工艺参数下消耗的能量（Y-3））评估了EO系统的性能，电流（i）和时间（t）。将人工神经网络应用于EO过程的建模，并通过中央复合设计（CCD）的期望函数方法，利用从ANN获得的受激数据，通过多响应优化来进行优化。人工神经网络成功地完成了EO工艺处理CBSOL LE红羊毛染料废水的建模，而CCD的优化生动地强调了变量之间的相互作用及其对EO工艺降解CBSOL LE红羊毛染料的影响。在最佳条件下，实际染料降解百分比（Y-1），脱色百分比（Y-2）和能耗（Y-3）分别为89.87％，96.71％和2.029 Wh。这些预测与实验结果吻合得很好。已经发现，EO处理的两种机理，即直接氧化和间接氧化均负责染料的降解/脱色。发现颜色几乎被完全去除，而处理后的废水中存在10.13％的染料。

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