Data-driven water quality prediction for wastewater treatment plants
Haitham Abdulmohsin Afan,
Wan Hanna Melini Wan Mohtar,
Faidhalrahman Khaleel,
Ammar Hatem Kamel,
Saif Saad Mansoor,
Riyadh Alsultani,
Ali Najah Ahmed,
Mohsen Sherif,
Ahmed El-Shafie
Affiliations
Haitham Abdulmohsin Afan
Upper Euphrates Basin Developing Center, University of Anbar, Iraq; Corresponding author. Upper Euphrates Basin Developing Center, University of Anbar, Iraq.
Wan Hanna Melini Wan Mohtar
Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia; Environmental Management Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia
Faidhalrahman Khaleel
Ministry of Electricity, The State Company of Electricity Production GCEP Middle Region, Baghdad, Iraq; Department of Civil Engineering, Atatürk University, 25240, Erzurum, Turkey
Ammar Hatem Kamel
Upper Euphrates Basin Developing Center, University of Anbar, Iraq; Dams and Water Resources Department, College of Engineering, University of Anbar, Iraq
Saif Saad Mansoor
Upper Euphrates Basin Developing Center, University of Anbar, Iraq
Riyadh Alsultani
Building and Construction Techniques Engineering Department, College of Engineering and Engineering Techniques, Al-Mustaqbal University, 51001, Babylon, Iraq
Ali Najah Ahmed
Research Centre For Human-Machine Collaboration (HUMAC), School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500, Selangor Darul Ehsan, Malaysia; Department of Engineering, School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500, Selangor Darul Ehsan, Malaysia
Mohsen Sherif
National Water and Energy Center, United Arab Emirate University, P.O. Box 15551, Al Ain, United Arab Emirates; Civil and Environmental Eng. Dept., College of Engineering, United Arab Emirates University, Al Ain, 15551, United Arab Emirates
Ahmed El-Shafie
National Water and Energy Center, United Arab Emirate University, P.O. Box 15551, Al Ain, United Arab Emirates
Monitoring and managing wastewater treatment plants (WWTPs) is crucial for environmental protection. The presection of the quality of treated water is essential for energy efficient operation. The current research presents a comprehensive comparison of machine learning models for water quality parameter prediction in WWTPs. Four machine learning models presented in MLP, GFFR, MLP-PCA, and RBF were employed in this study. The primary notion of this study is to apply the proposed models using two distinct modeling scenarios. The first scenario represents a straightforward approach by utilizing the inputs and outputs of WWTPs; meanwhile, the second scenario involves using multi-step modeling techniques, which incorporate intermediate outputs induced by primary and secondary settlers. The study also investigates the potential of the adopted models to handle high dimensional data as a result of the multi-step modeling since more data points and outputs are progressively integrated at each step. The results show that the GFFR model outperforms the other models across both scenarios, specifically in the second scenario in predicting conductivity (COND) by providing higher correlation accuracy (R = 0.893) and lower prediction deviations (NRMSE = 0.091 and NMAE = 0.071). However, all models across both scenarios struggle to predict the other water quality parameters, generating significantly lower prediction correlations and higher prediction deviations. Nonetheless, the innovative multi-step technique in scenario two has significantly boosted the prediction capacity of all models, with improvement ranging from 0.2 % to 157 % and an average of 60 %. The implementation of AI models has proven its ability to accomplish high accuracy for WQ parameter prediction, highlighting the impact of leveraging intermediate process data.
