Abstract
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.
Highlights
The operation of the wastewater treatment plant is a complex process which is meant to ensure effective and relatively stable purification of wastewater
Because the indicators of wastewater quality constitute input data for the models describing the kinetics of changes in carbon, nitrogen and phosphorus compounds in the bioreactors, and their values vary to a large extent, it is necessary to predict those indicators
Out of the methods investigated in this study, only the parameter estimation algorithm developed for the Multivariate Adaptive Regression Splines (MARS) model offers the possibility of discarding those predictors the effect of which on the dependent variable is negligible
Summary
The operation of the wastewater treatment plant is a complex process which is meant to ensure effective and relatively stable purification of wastewater. That can prove problematic due to a high variation in the quantity and quality of raw wastewater conveyed by the sewer system to the treatment facility. In modern wastewater treatment plants (WWTPs), the values of indicators that describe wastewater quality are measured with on-line systems according to a pre-set time step, or determined in laboratory tests. In both cases, problems may arise with obtaining time series of measurement data at a constant resolution. In order to ensure effective performance of WWTPs, it is advisable to develop autonomous systems for the prediction of influent wastewater quantity and quality
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