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Evaluating the performance of a sequencing batch reactor for sanitary wastewater treatment using artificial neural network
Environmental Progress & Sustainable Energy ( IF 2.8 ) Pub Date : 2020-05-02 , DOI: 10.1002/ep.13438
Hamid Yazdani 1 , Abbas Khoshhal 1 , Nayereh S. Mousavi 2
Affiliation  

In this work, the performance of a sequencing batch reactor (SBR) was studied for treating sanitary wastewater of Yazd power plant, Iran. For this purpose, at the first, a pilot system was designed, installed, and started up. Then the effects of retention time, pH, temperature, influent chemical oxygen demand (COD) concentration, and air flow rate were investigated on the effluent concentration of COD. In SBR reactor used in the Yazd power plant, the microalga was not used for the wastewater treatment. In this case, the COD effluent output was, at the best conditions, approximately 92 mg/L. In the studied SBR system, we used the Chlorella vulgaris microalgae and microorganisms, simultaneously. In this case, the COD level reached 34 mg/L. An artificial neural network (ANN) was developed by applying Levenberg‐Marquardt training algorithm to predict the effluent concentration of COD. The optimum conditions were obtained at pH = 8, temperature of 30°C, influent COD concentration of 600 mg/L, and air flow rate of 50 L/min. ANN predicted results were in good agreement with the experimental data with a validation coefficient of determination (R2) and validation mean square error of 0.962 and 0.0015, respectively.

中文翻译:

使用人工神经网络评估顺序批处理反应器处理生活污水的性能

在这项工作中,研究了顺序批处理反应器(SBR)的性能,用于处理伊朗亚兹德电厂的生活污水。为此,首先设计,安装并启动了一个试验系统。然后研究了保留时间,pH,温度,进水化学需氧量(COD)浓度和空气流速对废水中COD浓度的影响。在亚兹德电厂使用的SBR反应器中,微藻类未用于废水处理。在这种情况下,在最佳条件下,COD的出水量约为92 mg / L。在研究的S​​BR系统中,我们使用了小球藻微藻和微生物同时存在。在这种情况下,COD水平达到了34 mg / L。利用Levenberg-Marquardt训练算法开发了一个人工神经网络(ANN),以预测COD的排放浓度。在pH = 8,温度为30°C,进水COD浓度为600 mg / L,空气流速为50 L / min的条件下获得了最佳条件。人工神经网络的预测结果与实验数据吻合较好,确定系数为R 2,均方误差分别为0.962和0.0015。
更新日期:2020-05-02
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