Prediction of Peracetic Acid Disinfection Performance for Secondary Municipal Wastewater Treatment Using Artificial Neural Networks
Prediction of Peracetic Acid Disinfection Performance for Secondary Municipal Wastewater Treatment Using Artificial Neural Networks
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DOI：
10.1021/acsestwater.0c00095
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发表时间：
2021-02-12
期刊：
影响因子：
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通讯作者：
Cath, Tzahi Y.
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文献类型：
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作者：
Newhart, Kathryn B.;Goldman-Torres, Joshua E.;Cath, Tzahi Y.
消毒是城市污水处理的关键工序之一。然而，传统的化学给药方法没有考虑水质和工艺操作的变化如何改变消毒性能。这项工作的目的是开发新的消毒模型，以精确预测过氧乙酸（PAA）的性能，考虑水质的实时变化。人工神经网络和递归神经网络（ANN和RNN，分别）进行训练，预测PAA在不同的位置在整个消毒池，CT（活性浓度和接触时间的函数），和前和消毒后大肠杆菌使用在线和实验室数据。人工神经网络被发现预测PAA浓度的误差率可比的在线分析仪。此外，人工神经网络可以更准确地预测CT比传统的第一原理方法，无论有没有在线分析仪。一个带有滞后响应变量的人工神经网络可以预测E。大肠杆菌中的一小部分时间的RNN，但略有增加的错误。在这项工作中开发的模型集成到现有的监测和控制系统可以提供更强大的和动态的消毒控制，而不需要昂贵的分析仪的处理设施。
Disinfection is one of the most critical processes for municipal wastewater treatment. However, traditional chemical dosing approaches do not consider how changes in water quality and process operation can alter disinfection performance. This work aims to develop novel disinfection models for precise prediction of peracetic acid (PAA) performance that considers real-time changes in water quality. Artificial and recurrent neural networks (ANN and RNN, respectively) are trained to predict PAA at various locations throughout the disinfection basin, CT (a function of the active concentration and contact time), and pre-and postdisinfection Escherichia coli using online and laboratory data. An ANN is found to predict PAA concentrations at an error rate comparable to that of an online analyzer. Additionally, an ANN can predict CT more accurately than a conventional first-principles method both with and without an online analyzer. An ANN with a lagged response variable can predict E. coli in a fraction of the time of an RNN, but with a slightly increased error. The integration of the models developed in this work into existing monitoring and control systems could provide treatment facilities with more robust and dynamic disinfection control without the need for costly analyzers.