Journal of Structural and Construction Engineering

Journal of Structural and Construction Engineering

Analysis of Sewage Treatment Plant’s Data to evaluate Quality of Effluent using Experimental Results and Prediction based on Artificial Intelligence Models (Case Study: Tehran Wastewater Treatment Plant)

Document Type : Original Article

Authors
Haniyeh Malek 1
Majid Ehteshami 2
1 Master of Environment Engineering, Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran.
2 Associate Professor, Department of Environment, Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran.
10.22065/jsce.2022.333652.2754
Abstract
The use of wastewater outflowing from the municipal wastewater treatment plants for irrigation of agricultural lands is one of the important and fundamental issues for reuse of the plants’ effluent. In recent years, the artificial neural networks (ANN) have received considerable attention for modeling the sewage treatment process. Accordingly, the ANN models, fuzzy logic (FL) and adaptive neuro fuzzy inference system (ANFIS) have been utilized in this paper to predict quality of the effluent running out of the treatment plant. For this purpose, first, three scenarios were chosen and their inputs were reduced using the principal component analysis (PCA) method. Finally, the process of modeling was conducted with and without this method and then, the results were compared. Evaluating the results obtained from the predictions using the statistical indicators revealed that the ANFIS model with mean error reduction value of 13.92% compared to that of the FL model and a reduction value of 8.22% in contrast to the ANN model, benefits from a higher accuracy and this trend has been held true with and without PCA. Moreover, calculating the percentage of pollutant removal efficiency indicated that the maximum removal efficiency is obtained when total suspended solids (TSS) are removed which is equal to 96.68%. It bears to mention that the rest of the pollutants had values approximately equal to that of TSS. Based on the results, as a simple, accurate, efficient and reliable approach, the ANN models can be applied to predict quality of the effluents.
Keywords
Sewage treatment Plant
Effluent
Artificial Neural Network (ANN)
Multivariate Statistical Analysis Method
Statistical Indicators

Subjects

  • Row, D. Abdelmagid, I. (1995). Handbook of Waste Water Reclamation and Reuse. Lewis Publisher, by CRC press INC.
  • Hamed, M. Khalafallah, M.G. Hassanein, E.A. (2004). Prediction of wastewater treatment plant performance using artificial neural network. Environmental Modeling and Software. Vol 19, pp: 919–928.
  • Hong, Y.T. Rosen, M.R. Bhamidimarri, R. (2003). Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis.Water Research, Vol 37, pp: 1608–1618.
  • Ouyang, Y. Nkedi-Kizza, P. Wu, Q.T. Shined, D. Huang, C.H. (2006). Assessment of seasonal variations in surface water quality. Water Res, 40, pp: 3800-3810.
  • Camdevyren, H. Demyr, N. Kanik, A. Keskyn, S. (2005). Use of principal component scores in multiple linear regression models for prediction of Chlorophyll- in reservoirs. Modell. 181, pp: 581-589.
  • Gontarski, C.A. Rodrigues, P.R. Mori, M. Prenem, L.F. (2000). Simulation of an industrial wastewater treatment plant using artificial neural network. Computer & Chemical Engineering, 24, pp: 1719-1723.
  • Mahmoud, S. Nasr, Medhat, A.E. Moustafa, Hamdy, A.E. Seif, Galal El Kobrosy. (2012). Application of Artificial Neural Network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT. Alexandria Engineering Journal, 51, PP: 37–43.
  • Abba, S.I. Elkirana, G. (2017). Effluent prediction of chemical oxygen demand from the astewater treatment plant using artificial neural network application. Procedia computer science, 120, pp: 156-163.
  • Abbaa, S.I. Hadia, S.J. Abdullahia, J. (2017). River water modelling prediction using multi-linear regression, artificial neural network, and adaptive neuro-fuzzy inference system techniques. Procedia Computer Science, 120, pp: 75–82.
  • Elkiran, G. Nourani, V. Abba, S.I. Abdullahi, J. (2018). Artificial intelligence-based approaches for multi-station modelling of dissolve oxygen in river. Global J. Environ. Sci. Manage., 4(4), pp: 439-450.
  • Djeddou, M. Aouatef, H. Loukam, I. (2018). Wastewater Treatment Plant Performances Modelling Using Artificial Neural Networks. The international water association ecotechnologies for wastewater treatment. Western University, London, Ontario, Canada, pp: 25-27.
  • Abba, S.I. Abdulkadir, R.A. Gaya, M.S. Saleh, M.A. Esmaili, P. Jibril, M.B. (2019). Neuro-fuzzy ensemble techniques for the prediction of turbidity in water treatment plant. 2nd International Conference of the IEEE Nigeria Computer Chapter, pp: 1-6.
  • Alsulaili, A. Refaie, A. (2021). Artificial neural network modeling approach for the prediction of five-day biological oxygen demand and wastewater treatment plant performance. Water Supply, 21(5), pp: 1861–1877.
  • Tarun Gupta, (2019). Data Preprocessing in Data Mining & Machine Learning. Towards Data Science.
  • Abba, S.I. Elkiranb, G. Nouranic, V. (2021). Improving novel extreme learning machine using PCA algorithms for multi-parametric modeling of the municipal wastewater treatment plant. Desalination and Water Treatment, 215, pp: 414-426.
  • Chandak, A. Lee, W. Stamp, M. (2021). A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classification. Publisher: Springer International Publishing.
  • Real-Olvera, J. Morales-Rivera, J. Patricia, González-López, A. Sulbarán-Rangel, B. Zúñiga-Grajeda, V. (2020). Adsorption of Organic Pollutants from Cold Meat Industry Wastewater by Electrochemical Coagulation: Application of Artificial Neural Networks. Water, Vol. 12(11).
  • Qasaimeh, A. Al-Ghazawi, Z. (2020). Regression modeling for rapid prediction of wastewater BOD5. Desalination and Water Treatment, 201, pp: 165-172.
  • Gaya, M.S. Abba, S.I. Abdu, A.M. Tukur, A.I. Saleh, M.A. Esmaili, P. Wahab, N.A. (2020). Estimation of water quality index using artificial intelligence approaches and multi-linear regression. IAES International Journal of Artificial Intelligence (IJ-AI), Vol. 9, No. 1, pp: 126-134.
  • El-Nasr, M.S. Nguyen Dinh, T. H. Canossa, A. Drachen, A. (2021). Game Data Science. Published to Oxford Scholarship Online.
  • Mariani, M.C. Tweneboah, O.K. Beccar‐Varela, M.P. (Edition November 2021). Data Science in Theory and Practice, p: 400.
  • Hanteh, M. Malek, H. Akbari, M. (2022). Development of the integrated MARS-PSO and ELM-PSO models for estimating the compressive strength of concrete in circular concrete columns confined with FRP polymer fibers. Scientific and Research Journal of Civil Engineering Sharif.
  • Hanteh, R. Hanteh, M. Kheyroddin, A. & Rezaifar, O. (2020). Determination of Strength Parameters in Roller Compacted Concrete (RCC) Dams using Laboratory Results and Forecasting based on Artificial Neural Networks. Modares Civil Engineering journal, 20(2), pp: 55-70.
  • Akbari, M. & Henteh, M. (2019). Comparison of Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO) for Discrete and Continuous Size Optimization of 2D Truss Structures. Journal of Soft Computing in Civil Engineering, 3(2), pp: 76-97.
  • Naderpour, H. Rafiean, A.H. Fakharian, P. (2018). Compressive Strength Prediction of Environmentally Friendly Concrete Using Artificial Neural Networks. Journal of Building Engineering, Vol 16, pp: 213-219.
  • Naderpour, H. Sharei, M. Fakharian, P. Heravi, M.A. (2022). Shear Strength Prediction of Reinforced Concrete Shear Wall Using ANN, GMDH-NN and GEP. Journal of Soft Computing in Civil Engineering 6 (1), pp: 66-87.
  • Ghanizadeh, A.R. Ziaee, A. Khatami, S.M.H. Fakharian, P. (2022). Predicting Resilient Modulus of Clayey Subgrade Soils by Means of Cone Penetration Test Results and Back-Propagation Artificial Neural Network. Journal of Rehabilitation in Civil Engineering 10 (4), pp: 146-162.
  • Naderpour, H. Fakharian, P. (2018). Predicting the Torsional Strength of Reinforced Concrete Beams Strengthened with FRP Sheets in terms of Artificial Neural Networks. Journal of Structural and Construction Engineering 5 (1), pp: 20-35.
  • Naderpour, H. Rezazadeh Eidgahee, D. Fakharian. P. Rafiean A. H. Kalantari. S.M. (2020). A New Proposed Approach for Moment Capacity Estimation of Ferrocement Members Using Group Method of Data Handling. Engineering Science and Technology, an International Journal, Volume 23, Issue 2, pp: 382-391.
  • Naderpour, H. Nagai, K. Fakharian, P. Haji, M. (2019) .Innovative Models for Prediction of Compressive Strength of FRP-Confined Circular Reinforced Concrete Columns Using Soft Computing Methods. Composite Structures, Vol 215, pp: 69-84.
  • El Alaoui, M. (2021). Fuzzy TOPSIS. Boca Raton, 1st Edition, p: 216.
  • Vilela, M. Oluyemi, G. (2022). Value of Information and Flexibility. Springer International Publishing.
  • Mansouri, I. Kisi, O. (2015). Prediction of debonding strength for masonry elements retrofitted with frp composites using neuro fuzzy and neural network approaches. ELSAVIER, Composites Part B: Engineering, 70, pp: 247-255.
  • Manu, S. Thalla, A.K. (2017). Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater. Appl Water Sci 7, pp: 3783–3791.
  • Zhu, S.L. Heddam, S. Nyarko, E.K. Hadzima-Nyarko, M. Piccolroaz, S. Wu, S.Q. (2019). Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models. Sci. Pollut. Res, 26, pp: 402–420.

  • Receive Date 13 March 2022
  • Revise Date 13 August 2022
  • Accept Date 23 August 2022