Performance prediction and control for wastewater treatment plants using artificial neural network modeling of mechanical and biological treatment

Alnajjar, Hussein Y.H.; Üçüncü, Osman

doi:10.24425/aep.2023.145893

Artykuł - szczegóły

Tytuł artykułu

Performance prediction and control for wastewater treatment plants using artificial neural network modeling of mechanical and biological treatment

Autorzy

Alnajjar Hussein Y.H. , Üçüncü Osman

Treść / Zawartość

Pełne teksty:

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Identyfikatory

DOI

10.24425/aep.2023.145893

Warianty tytułu

Języki publikacji

Abstrakty

Biological treatment in wastewater treatment plants appears to be one of the most crucial factors in water quality management and planning. Though, measuring this important factor is challenging, and obtaining reliable results requires signifi cant eff ort. However, the use of artifi cial neural network (ANN) modeling can help to more reliably and cost-effectively monitor the pollutant characteristics of wastewater treatment plants and regulate the processing of these pollutants. To create an artifi cial neural network model, a study of the Samsun Eastern Advanced Biological WWTP was carried out. It provides a laboratory simulation and prediction option for flexible treatment process simulations. The models were created to forecast influent features that would affect effluent quality metrics. For ANN models, the correlation coefficients R-TRAINING and R-ALL are more than 0.8080. The MSE, RMSE, and MAPE were less than 0.8704. The model’s results showed compliance with the permitted wastewater quality standards set forth in the Turkish water pollution control law for the environment where the treated wastewater is discharged. This is a useful tool for plant management to enhance the quality of the treatment while enhancing the facility’s dependability and efficiency.

Słowa kluczowe

artificial neural network wastewater treatment total phosphorus total nitrogen biological oxygen demand

sztuczna sieć neuronowa oczyszczanie ścieków fosfor całkowity azot całkowity zapotrzebowanie na tlen

Wydawca

Institute of Environmental Engineering, Polish Academy of Sciences

Czasopismo

Archives of Environmental Protection

Rocznik

2023

Tom

Vol. 49, no 2

Strony

16--29

Opis fizyczny

Bibliogr. 33 poz., rys., tab., wykr.

Twórcy

autor

Alnajjar Hussein Y.H.

Karadeniz Technical University Civil Engineering Faculty Hydraulic Department, Trabzon

autor

Üçüncü Osman

392242@org.ktu.edu.tr

Karadeniz Technical University Civil Engineering Faculty Hydraulic Department, Trabzon

Bibliografia

1. Adeogun, A. I., Bhagawati, P. B. & Shivayogimath, C. B. (2021). Pollutants removals and energy consumption in electrochemical cell for pulping processes wastewater treatment: Artificial neural network, response surface methodology and kinetic studies. Journal of Environmental Management, 281(December 2020), 111897. DOI:10.1016/j.jenvman.2020.111897.
2. Agatonovic-Kustrin, S. & Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis, 22,5,pp. 717–727. DOI:10.1016/S0731-7085(99)00272-1.
3. Alnajjar, H. Y. H. & Üçüncü, O. (2023). Removal efficiency prediction model based on the artificial neural network for pollution prevention in wastewater treatment plants. Arab Gulf Journal of Scientific Research, ahead-of-p(ahead-of-print), DOI:10.1108/AGJSR-07-2022-0129.
4. Bagheri, M., Mirbagheri, S. A., Bagheri, Z. & Kamarkhani, A. M. (2015). Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach. Process Safety and Environmental Protection, 95, pp.12–25. DOI:10.1016/j.psep.2015.02.008.
5. Bekkari, N. & Zeddouri, A. (2019). Using artificial neural network for predicting and controlling the effluent chemical oxygen demand in wastewater treatment plant. Management of Environmental Quality: An International Journal, 30,3, pp. 593–608, DOI:10.1108/MEQ-04-2018-0084.
6. Borgulat, A., Zgórska, A. & Głodniok, M. (2022). Comparison of different municipal sewage sludge products for potential ecotoxicity. Archives of Environmental Protection, 48, 1, pp. 92–99. DOI:10.24425/aep.2022.140548.
7. Chang, N. Bin, Chen, W. C. & Shieh, W. K. (2001). Optimal control of wastewater treatment plants via integrated neural network and genetic algorithms. Civil Engineering and Environmental Systems, 18, 1, pp. 1–17. DOI:10.1080/02630250108970290.
8. Gangi Setti, S. & Rao, R. N. (2014). Artificial neural network approach for prediction of stress-strain curve of near β titanium alloy. Rare Metals, 33, 3, pp. 249–257. DOI:10.1007/s12598-013-0182-2..
9. Golzar, F., Nilsson, D. & Martin, V. (2020). Forecasting wastewater temperature based on artificial neural network (ANN) technique and Monte Carlo sensitivity analysis. Sustainability (Switzerland), 12, 16. DOI:10.3390/SU12166386.
10. Golzar, K., Modarress, H. & Amjad-Iranagh, S. (2016). Evaluation of density, viscosity, surface tension and CO2 solubility for single, binary and ternary aqueous solutions of MDEA, PZ and 12 common ILs by using artificial neural network (ANN) technique. International Journal of Greenhouse Gas Control, 53, pp. 187–197. DOI:10.1016/j.ijggc.2016.08.008.
11. Guo, H., Jeong, K., Lim, J., Jo, J., Kim, Y. M., Park, J. pyo, Kim, J. H. & Cho, K. H. (2015). Prediction of effluent concentration in a wastewater treatment plant using machine learning models. Journal of Environmental Sciences (China), 32, pp. 90–101. DOI:10.1016/j.jes.2015.01.007.
12. Hamada, M., Zaqoot, H. A. & Jreiban, A. A. (2018). Application of artificial neural networks for the prediction of Gaza wastewater treatment plant performance-Gaza strip. Journal of Applied Research in Water and Wastewater, 9, 1, pp. 399–406.
13. Hanbay, D., Turkoglu, I. & Demir, Y. (2008). Prediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networks. Expert Systems with Applications, 34, 2, pp. 1038–1043. DOI:10.1016/j.eswa.2006.10.030.
14. Haykin, S. U. (2009). Neural Networks and Learning Machines. In 3 (Ed.), Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics (Vols. 1–3). Library of Congress Cataloging. DOI:10.1016/B978-0-12-809633-8.20339-7.
15. Hong, Y.-S. T., Rosen, M. R. & Bhamidimarri, R. (2003). Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis. Water Research, 37, 7, pp. 1608–1618. DOI:10.1016/S0043-1354(02)00494-3.
16. Iratni, A. & Chang, N.-B. (2019). Advances in control technologies for wastewater treatment processes: status, challenges, and perspectives. IEEE/CAA Journal of Automatica Sinica, 6, 2, pp. 337–363, DOI:10.1109/JAS.2019.1911372.
17. Jana, D. K., Bhunia, P., Das Adhikary, S. & Bej, B. (2022). Optimization of Effluents Using Artificial Neural Network and Support Vector Regression in Detergent Industrial Wastewater Treatment. Cleaner Chemical Engineering, 3(June), pp. 100039. DOI:10.1016/j.clce.2022.100039
18. Jawad, J., Hawari, A. H. & Javaid Zaidi, S. (2021). Artificial neural network modeling of wastewater treatment and desalination using membrane processes: A review. Chemical Engineering Journal, 419(March), pp. 129540. DOI:10.1016/j.cej.2021.129540
19. Khatri, N., Khatri, K. K. & Sharma, A. (2020). Artificial neural network modelling of faecal coliform removal in an intermittent cycle extended aeration system-sequential batch reactor based wastewater treatment plant. Journal of Water Process Engineering, 37, pp. 101477. DOI:10.1016/j.jwpe.2020.101477.
20. Matheri, A. N., Ntuli, F., Ngila, J. C., Seodigeng, T. & Zvinowanda, C. (2021). Performance prediction of trace metals and cod in wastewater treatment using artificial neural network. Computers and Chemical Engineering, 149, pp. 107308. DOI:10.1016/j.compchemeng.2021.107308
21. MATLAB. (2022). The MathWorks Inc version R2022b (version R2021b). The MathWorks Inc. https://matlab.mathworks.com.
22. Negnevitsky, M. (2005). Artificial Intelligence A Guide to Intelligent Systems. In British Library Cataloguing (2nd ed., Vol. 123). DOI:10.1016/j.poly.2016.11.012.
23. Oliveira-Esquerre, K. P., Mori, M. & Bruns, R. E. (2002). Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis. Brazilian Journal of Chemical Engineering, 19, 4, pp. 365–370. DOI:10.1590/S0104-66322002000400002.
24. Pai, T.-Y. (2008). Gray and Neural Network Prediction of Effluent from the Wastewater Treatment Plant of Industrial Park Using Influent Quality. Environmental Engineering Science, 25, 5, pp. 757–766. DOI:10.1089/ees.2007.0136.
25. Paquin, F., Rivnay, J., Salleo, A., Stingelin, N. & Silva, C. (2015). Multi-phase semicrystalline microstructures drive exciton dissociation in neat plastic semiconductors. J. Mater. Chem. C, 3, 4 , pp. 10715–10722.DOI:10.1039/b000000x.
26. Sakiewicz, P., Piotrowski, K., Ober, J. & Karwot, J. (2020). Innovative artificial neural network approach for integrated biogas – wastewater treatment system modelling: Effect of plant operating parameters on process intensification. Renewable and Sustainable Energy Reviews, 124. DOI:10.1016/j.rser.2020.109784
27. Sharghi, E., Nourani, V., Aliashrafi, A. & Gökçekuş, H. (2019). Monitoring effluent quality of wastewater treatment plant by clustering baseartificial neural network method. Desalination and Water Treatment, 164, pp. 86–97. DOI:10.5004/dwt.2019.24385
28. Tumer, A. E. & Edebali, S. (2015). Prediction of wastewater treatment plant performance using multilinear regression and artificial neural networks. INISTA 2015 - 2015 International Symposium on Innovations in Intelligent SysTems and Applications, Proceedings, DOI:10.1109/INISTA.2015.7276742
29. Wang, G., Qiao, J., Bi, J., Li, W. & Zhou, M. (2019). TL-GDBN: Growing Deep Belief Network with Transfer Learning. IEEE Transactions on Automation Science and Engineering, 16, 2, pp. 874–885DOI:10.1109/TASE.2018.2865663
30. Yang, Y., Kim, K. R., Kou, R., Li, Y., Fu, J., Zhao, L. & Liu, H. (2022). Prediction of effluent quality in a wastewater treatment plant by dynamic neural network modeling. Process Safety and Environmental Protection, 158, pp. 515–524. DOI:10.1016/j.psep.2021.12.034
31. Zeinolabedini, M. & Najafzadeh, M. (2019). Comparative study of different wavelet-based neural network models to predict sewage sludge quantity in wastewater treatment plant. Environmental Monitoring and Assessment, 191, 3. DOI:10.1007/s10661-019-7196-7
32. Zhao, Ying, Guo, L., Liang, J. & Zhang, M. (2016). Seasonal artificial neural network model for water quality prediction via a clustering analysis method in a wastewater treatment plant of China. Desalination and Water Treatment, 57, 8, pp. 3452–3465, DOI:10.1080/19443994.2014.986202.
33. Zhao, Yuchao, Xie, Z. & Lou, I. (2015). Using Extreme Learning Machine for Filamentous Bulking Prediction in Wastewater Treatment Plants. [In] J. Cao, K. Mao, E. Cambria, Z. Man, & K.-A. Toh (Eds.), Proceedings of ELM-2014 Volume 2 , pp. 1–9, Springer International Publishing.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-9719734c-f058-4bd9-a1d3-16ddda81d224