In this study, the photocatalytic method was used for treating the spent caustic in the wastewater of Olefin units used in petrochemical industries which contain large amounts of total dissolved solids (TDS). By using the synthetic photocatalyst of suspended titanium dioxide and measuring the chemical oxygen demand (COD) which was reduced in the photocatalyst (lbc) process, the values of COD were modeled and evaluated by means of the Box-Behnken (BBD) and the artificial neural network (ANN) using experimental tests in a double-cylindrical-shell photo reactor. According to the applied calculations, it was found that the artificial neural network was a more suitable method than the experimental design in modeling and forecasting the amount of COD removal. The modeling employed in this research showed that increasing the concentration of the photocatalyst in a state of neutral pH enhanced the COD removal up to the optimal amount of 1.31 g/L without restrictions and 2 g/L with restrictions at the rate of 81% and 79%, respectively. In addition, the study of the parameter effects including oxidizer amount, aeration rate, pH, and the amount of loaded catalyst indicated that all factors except pH had a positive effect on the model; furthermore, if the interactions were neglected, the COD removal efficiency would increase by increasing each of these factors (except pH). In addition, there was no interaction between the aeration and the concentration of the photocatalyst, and the acidic pH was more suitable at low concentrations of the photocatalyst. Besides that, by increasing the pH, the efficiency of removal was reduced when the oxidant was at its low level. The results showed that photolysis and adsorption adoptions had a very small effect on the efficiency of the removal of COD compared to the photocatalyst adoptions, and it was insignificant. In addition, the photocatalytic method had an acceptable capacity for removing the phenol in the wastewater sample, whereas it was inefficient in reducing the sulfide solution in the wastewater.
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机译：在这项研究中，光催化方法用于处理石油化学工业中含有大量总固形物（TDS）的烯烃装置废水中的废碱液。通过使用悬浮二氧化钛的合成光催化剂并测量在光催化剂（lbc）过程中减少的化学需氧量（COD），通过Box-Behnken（BBD）和人工方法对COD值进行建模和评估神经网络（ANN）在双圆柱壳光反应器中使用实验测试。根据应用的计算，发现在建模和预测COD去除量方面，人工神经网络比实验设计更合适。这项研究中使用的模型表明，在中性pH值下增加光催化剂的浓度可将COD的去除量提高到最佳量，无限制的达到1.31 g / L，有限制的2 g / L的最佳比例为81％和分别为79％。此外，对包括氧化剂用量，曝气速率，pH值和催化剂负载量等参数影响的研究表明，除pH值外，所有其他因素均对模型产生积极影响。此外，如果忽略了相互作用，则通过增加这些因素（pH值除外）中的每一个因素，都会提高COD去除效率。另外，曝气与光催化剂的浓度之间没有相互作用，并且在低浓度的光催化剂下酸性pH更合适。除此之外，通过提高pH值，当氧化剂处于低水平时，去除效率降低。结果表明，与采用光催化剂相比，采用光解吸附法对去除COD的效率影响很小，并且意义不大。另外，光催化方法具有去除废水样品中的苯酚的可接受的能力，而其在还原废水中的硫化物溶液方面效率低下。
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