Wastewater treatment plant performance analysis using artificial intelligence – an ensemble approach

• Published in: Water science and technology Vol. 78; no. 10; pp. 2064 - 2076
• Main Authors: Nourani, Vahid, Elkiran, Gozen, Abba, S. I.
• Format: Journal Article
• Language: English
• Published: England IWA Publishing 01.12.2018
• Subjects: Adaptive systems; Artificial Intelligence; Artificial neural networks; Biochemical oxygen demand; Chemical oxygen demand; Cultural organizations; Effluents; Engineering; Environmental science; Fuzzy Logic; Fuzzy systems; Hydrology; Linear Models; Literature reviews; Mathematical models; Methods; Modelling; Neural networks; Neural Networks, Computer; Nitrogen; Organic chemistry; Performance enhancement; Performance prediction; Regression analysis; Sanitation; Support vector machines; Total oxygen demand; Waste Disposal, Fluid - methods; Waste Water; Wastewater; Wastewater treatment; Wastewater treatment plants; Water quality; Water shortages; Water treatment
• ISSN: 0273-1223; 1996-9732
• DOI: 10.2166/wst.2018.477

In the present study, three different artificial intelligence based non-linear models, i.e. feed forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), support vector machine (SVM) approaches and a classical multi-linear regression (MLR) method were applied for predicting the performance of Nicosia wastewater treatment plant (NWWTP), in terms of effluent biological oxygen demand (BODeff), chemical oxygen demand (CODeff) and total nitrogen (TNeff). The daily data were used to develop single and ensemble models to improve the prediction ability of the methods. The obtained results of single models proved that, ANFIS model provides effective outcomes in comparison with single models. In the ensemble modeling, simple averaging ensemble, weighted averaging ensemble and neural network ensemble techniques were proposed subsequently to improve the performance of the single models. The results showed that in prediction of BODeff, the ensemble models of simple averaging ensemble (SAE), weighted averaging ensemble (WAE) and neural network ensemble (NNE), increased the performance efficiency of artificial intelligence (AI) modeling up to 14%, 20% and 24% at verification phase, respectively, and less than or equal to 5% for both CODeff and TNeff in calibration phase. This shows that NNE model is more robust and reliable ensemble method for predicting the NWWTP performance due to its non-linear averaging kernel.