Hybrid data driven approach based on ANNs-PCA for wastewater treatment plant performance assessment
Redouane Elharbili,
Tawfik El Moussaoui,
Khalid El Ass,
Mohamed Oussama Belloulid,
Abdelhafid El Alaoui El Fels,
Mohamed Yassine Samiri
Affiliations
Redouane Elharbili
Rabat-Mines School (ENSMR), Ave Hadj Ahmed Cherkaoui, Agdal, Rabat BP 753, Morocco
Tawfik El Moussaoui
Rabat-Mines School (ENSMR), Ave Hadj Ahmed Cherkaoui, Agdal, Rabat BP 753, Morocco; Laboratory of Water, Biodiversity and Climate Change, Faculty of Sciences Semlalia, Cadi Ayyad University, BP-2390, Marrakesh 40,000, Morocco; Corresponding author at: Rabat-Mines School (ENSMR), Ave Hadj Ahmed Cherkaoui, Agdal, Rabat BP 753, Morocco.
Khalid El Ass
Rabat-Mines School (ENSMR), Ave Hadj Ahmed Cherkaoui, Agdal, Rabat BP 753, Morocco
Mohamed Oussama Belloulid
Laboratory of Water, Biodiversity and Climate Change, Faculty of Sciences Semlalia, Cadi Ayyad University, BP-2390, Marrakesh 40,000, Morocco
Abdelhafid El Alaoui El Fels
Geology and Sustainable Mining Institute (GSMI), Mohammad VI Polytechnic University (UM6P), Lot 660 Hay Moulay Rachid, Ben Guerir 43150, Morocco
Mohamed Yassine Samiri
Laboratory of Computer and Systems Engineering, Cadi Ayyad University, Marrakesh, Morocco
In this paper, a data driven method to assess and predict performance of full scale urban activated sludge wastewater treatment plant (WWTP) is presented. The proposed hybrid approach consists of a combination of artificial neural networks (ANNs) and principal component analysis (PCA). Measurement results of a municipal activated sludge WWTP operation of 1.3 million inhabitant equivalents are presented and discussed. In ANNs PCA design, the ANNs used to calculate a nonlinear and dynamic model of the processes under normal operating conditions. Besides, PCA is used to generate monitoring charts based on all measured parameters. Results highlight that ANNs-PCA monitoring is crucial tool that can be used to optimize and predict process spatiotemporal evaluation. This research results provide a practical strategy for improving operation, management and performance prediction of studied WWTP. This supports Sustainable Development Goal (SDG) 6: Clean Water and Sanitation and worldwide sustainability actions and efforts.
