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Artificial neural network (ANN) approach for modeling of selected biogenic compounds in a mixture of treated municipal and dairy wastewater

机译:人工神经网络(ANN)方法用于模拟处理后的市政和乳业废水混合物中的特定生物化合物

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This paper presents artificial neural network (ANN) model of wastewater treatment plant, which was used for average monthly concentrations of N-NH4~(+), N-NO3~(-), N-NO2~(-), total Kiejdahl nitrogen (TKN), PO4~(3-)and SO4~(2-)approximation. ANN model was developed for wastewater treatment plant located in Bystre, Poland which treats municipal wastewater with a share of dairy wastewater. The object was chosen because of the unique location, in the Great Mazury Lakes area and the need for its special environmental protection. Input layer of developed ANN model consisted of BOD, COD, concentrations of total nitrogen and total phosphorus, total organic carbon, sulphates, wastewater temperature and pH., The developed model reflected extreme values observed during study period. Average error percentage with which output variables were approximated equalled to 35.35%; 8.99%; 21.23%; 5.08%; 10.99%; 3.02% respectively for N-NH4~(+), N-NO3~(-), N-NO2~(-), TKN, PO4~(3-)and SO4~(2-).
机译:本文提出了污水处理厂的人工神经网络模型,用于计算N-NH4〜(+),N-NO3〜(-),N-NO2〜(-),Kiejdahl总氮的月平均浓度。 (TKN),PO4〜(3-)和SO4〜(2-)近似值。 ANN模型是为位于波兰比斯特(Bystre)的废水处理厂开发的,用于处理市政废水和部分乳制品废水。选择该对象是因为其在大马祖里湖地区的独特地理位置以及对其特殊环境保护的需求。所建立的人工神经网络模型的输入层包括BOD,COD,总氮和总磷浓度,总有机碳,硫酸盐,废水温度和pH值。所建立的模型反映了研究期间观察到的极值。输出变量近似的平均误差百分比等于35.35%; 8.99%; 21.23%; 5.08%; 10.99%; N-NH4〜(+),N-NO3〜(-),N-NO2〜(-),TKN,PO4〜(3-)和SO4〜(2-)分别为3.02%。

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