Prediction of wastewater quality indicators
This special issue belongs to
|International Journal of Prevention and Control of Industrial Pollution|
Deadline for Manuscript Submission
|March 31st, 2023|
Deadline for Publication
|April 15, 2023|
Special Issue Description
Models created utilizing data mining techniques are suggested for forecasting the following wastewater quality indicators: biochemical and chemical oxygen demand, total suspended particles, total nitrogen, and total phosphorus at the intake to the wastewater treatment plant (WWTP). The models are built using data from prior time steps and daily wastewater intakes.
Additionally, separate prediction systems are offered, which can be employed if the monitoring equipment stops working.
Artificial neural networks (ANN) of the multilayer perceptron type coupled with the categorization model (SOM), as well as cascading neural networks, were used to construct models of wastewater quality indicators (CNN).ANN+SOM produced the lowest absolute and relative error values, whereas the MARS technique yielded the greatest error values. It was demonstrated that using the two created independent prediction methods, it is possible to get continuous predictions of particular wastewater quality indicators for the examined WWTP.
When wastewater quality monitoring systems are down for repair or become inoperative, such models can guarantee dependable WWTP operation.
*Quality indicators *Phosphorus *Artificial neural networks *Wastewater *Biochemical *Chemical oxygen demand *Nitrogen *Multilayer perceptron
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