Abstract—Optimization and control of waste water
treatment plants (WWTP) is an ongoing effort to make the
process more efficient and cost-effective. As found in literature,
data mining models such as neural networks have been applied
to simulate and model various aspects of the plant such as
performance, quality parameters and process parameters. In
this paper, we introduce bagging model, an ensemble data
mining model, to predict the performance of the WWTP.
Ensemble models have been shown to stabilize the base
classifier used and avoid overfitting the data. Bagging was used
to predict the performance of individual units (primary settler
and secondary settler) and the global plant performance. The
predicted performance of individual units was also used as
inputs to predict the global performance thereby enabling good
process control via predictive data models. Upon application to
the WWTP dataset, it was found that bagging models perform
at par or even better than ANN or SVM for the prediction and
hence are suitable models that can be implemented for process
control of the water treatment plants.
Index Terms—Waste water treatment plant (WWTP), ensemble models, bagging, process control.
Bharat B. Gulyani is with the Department of Chemical Engineering at BITS Pilani, Dubai Campus, Academic City, Dubai 345055, UAE (e-mail: email@example.com).
Arshia Fathima is with Nanolabs, Alfaisal University, Saudi Arabia (e-mail: firstname.lastname@example.org).
Cite: Bharat B. Gulyani and Arshia Fathima, "Introducing Ensemble Methods to Predict the Performance of Waste Water Treatment Plants (WWTP)," International Journal of Environmental Science and Development vol. 8, no. 7, pp. 501-506, 2017.