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Prediction of wastewater quality indicators at the inflow to the wastewater treatment plant using data mining methods

机译：利用数据采矿方法预测废水处理厂流入的废水质量指标

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In the study, models developed using data mining methods are proposed for predicting wastewater quality indicators: biochemical and chemical oxygen demand, total suspended solids, total nitrogen and total phosphorus at the inflow to wastewater treatment plant (WWTP). The models are based on values measured in previous time steps and daily wastewater inflows. Also, independent prediction systems that can be used in case of monitoring devices malfunction are provided. Models of wastewater quality indicators were developed using MARS (multivariate adaptive regression spline) method, artificial neural networks (ANN) of the multilayer perceptron type combined with the classification model (SOM) and cascade neural networks (CNN). The lowest values of absolute and relative errors were obtained using ANN+SOM, whereas the MARS method produced the highest error values. It was shown that for the analysed WWTP it is possible to obtain continuous prediction of selected wastewater quality indicators using the two developed independent prediction systems. Such models can ensure reliable WWTP work when wastewater quality monitoring systems become inoperable, or are under maintenance.
机译：在该研究中，提出了使用数据挖掘方法开发的模型，用于预测废水质量指标：生物化学和化学需氧量，全部悬浮固体，总氮和总磷在废水处理厂（WWTP）。该模型基于以前的时间步长测量的值以及每日废水流入。此外，提供了在监视设备故障的情况下可以使用的独立预测系统。使用火星（多变量自适应回归曲序）方法开发了废水质量指标的模型，多层Perceptron型与分类模型（SOM）和级联神经网络（CNN）组合的人工神经网络（ANN）。使用Ann + SOM获得绝对和相对误差的最低值，而MARS方法产生了最高的误差值。结果表明，对于分析的WWTP，可以使用两种开发的独立预测系统来获得对所选废水质量指标的连续预测。当废水质量监测系统变得无法操作或正在维护时，这种模型可以确保可靠的WWTP工作。

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