Journal of Information Systems Engineering and Management

Impact of artificial intelligence in the reduction of electrical consumption in wastewater treatment plants: a review
Francisco António Esteves 1 * , José Cardoso 1, Sérgio Leitão 1, Eduardo Pires 1
More Detail
1 University of Trás-os-Montes e Alto Douro, Vila Real, Portugal
* Corresponding Author
Literature Review

Journal of Information Systems Engineering and Management, 2023 - Volume 8 Issue 3, Article No: 21855
https://doi.org/10.55267/iadt.07.13623

Published Online: 31 Aug 2023

Views: 330 | Downloads: 278

How to cite this article
APA 6th edition
In-text citation: (Esteves et al., 2023)
Reference: Esteves, F. A., Cardoso, J., Leitão, S., & Pires, E. (2023). Impact of artificial intelligence in the reduction of electrical consumption in wastewater treatment plants: a review. Journal of Information Systems Engineering and Management, 8(3), 21855. https://doi.org/10.55267/iadt.07.13623
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Esteves FA, Cardoso J, Leitão S, Pires E. Impact of artificial intelligence in the reduction of electrical consumption in wastewater treatment plants: a review. J INFORM SYSTEMS ENG. 2023;8(3):21855. https://doi.org/10.55267/iadt.07.13623
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Esteves FA, Cardoso J, Leitão S, Pires E. Impact of artificial intelligence in the reduction of electrical consumption in wastewater treatment plants: a review. J INFORM SYSTEMS ENG. 2023;8(3), 21855. https://doi.org/10.55267/iadt.07.13623
Chicago
In-text citation: (Esteves et al., 2023)
Reference: Esteves, Francisco António, José Cardoso, Sérgio Leitão, and Eduardo Pires. "Impact of artificial intelligence in the reduction of electrical consumption in wastewater treatment plants: a review". Journal of Information Systems Engineering and Management 2023 8 no. 3 (2023): 21855. https://doi.org/10.55267/iadt.07.13623
Harvard
In-text citation: (Esteves et al., 2023)
Reference: Esteves, F. A., Cardoso, J., Leitão, S., and Pires, E. (2023). Impact of artificial intelligence in the reduction of electrical consumption in wastewater treatment plants: a review. Journal of Information Systems Engineering and Management, 8(3), 21855. https://doi.org/10.55267/iadt.07.13623
MLA
In-text citation: (Esteves et al., 2023)
Reference: Esteves, Francisco António et al. "Impact of artificial intelligence in the reduction of electrical consumption in wastewater treatment plants: a review". Journal of Information Systems Engineering and Management, vol. 8, no. 3, 2023, 21855. https://doi.org/10.55267/iadt.07.13623
ABSTRACT
Wastewater Treatment Plants are energy-intensive consumers. Thus, understanding their energy consumption to achieve efficient management can provide considerable environmental and economic benefits. The complexity of the treatment systems, the non-linearity, and the uncertainty and data availability limitations require the use of energy audits, according to a truly holistic view, as well as the use of alternative analysis models and decision support, more efficient than traditional modeling techniques.   The purpose of this review paper is to identify practical examples of the main lines of thought using Artificial Intelligence algorithms used to reduce the consumption of electrical energy in the wastewater sector over the last years. From the several reviewed papers, from different research platforms, it is concluded that, despite the success of AI in reducing energy consumption, in particular Artificial Neural Networks, there is room to improve energy efficiency consumption, identifying or quantifying inefficiency phenomena associated with data collection.
KEYWORDS
REFERENCES
  • ADENE (2021). Eficiência energética. http://www.ersar.pt/pt/site- comunicacao/site-noticias/documents/gt24-eficiencia-energetica.pdfhttp://www.ersar.pt/pt/site- comunicacao/site-noticias/documents/gt24-eficiencia-energetica.pdf.
  • Ahmadi, M. M., Mahdavirad, H., and Bakhtiari, B. (2017). Multi-criteria analysis of site selection for groundwater recharge with treated municipal wastewater. Water Science and Technology, 76(4):909–919.
  • Asadi, A., Verma, A., Yang, K., and Mejabi, B. (2017). Wastewater treatment aeration process optimization: A data mining approach. Journal of environmental management, 203:630–639.
  • Benedetti, L., Dirckx, G., Bixio, D., Thoeye, C., and Vanrolleghem, P. A. (2008). Environmental and economic performance assessment of the integrated urban wastewater system. Journal of Environmental Management, 88(4):1262–1272.
  • Bezdek, J. C. (2016). (computational) intelligence: What’s in a name? IEEE Systems, Man, and Cybernetics Magazine, 2(2):4–14.
  • Bodik, I. and Kubaska, M. (2013). Energy and sustainability of oper- ation of a wastewater treatment plant. Environment Protection Engineering, 39(2):15–24.
  • Boulos, P. F., Wu, Z., Orr, C. H., Moore, M., Hsiung, P., and Thomas, D. (2001). Optimal pump operation of water distribution systems using genetic algorithms. In Distribution system symposium. Citeseer.
  • Breiman, L. (1996). Bagging predictors. Machine learning, 24:123–140.
  • Breiman, L. (1998). Rejoinder to the paper ‘arcing classifiers’ by leo breiman. Annals of Statistics, 26(2):841–849.
  • Campanelli, M., Foladori, P., and Vaccari, M. (2013). Consumi elettrici ed efficienza energetica del trattamento delle acque reflue. Maggioli editore.
  • Cao, W. and Yang, Q. (2020). Online sequential extreme learning machine based adaptive control for wastewater treatment plant. Neurocomputing, 408:169–175.
  • Chau, K. w. (2006). A review on integration of artificial intelligence into water quality modelling. Marine pollution bulletin, 52(7):726–733.
  • Chen, W., Chang, N.-B., and Shieh, W. K. (2001). Advanced hybrid fuzzy- neural controller for industrial wastewater treatment. Journal of environmental engineering, 127(11):1048–1059.
  • Cherkassky, V. and Mulier, F. M. (2007). Learning from data: concepts, theory, and methods. John Wiley & Sons.
  • Cire¸san, D. C., Meier, U., Masci, J., Gambardella, L. M., and Schmidhuber, J. (2011). High-performance neural networks for visual object classification. arXiv preprint arXiv:1102.0183.
  • Daw, J., Hallett, K., DeWolfe, J., and Venner, I. (2012). Energy efficiency strategies for municipal wastewater treatment facilities. Technical report, National Renewable Energy Lab.(NREL), Golden, CO (United States).
  • Dietterich, T. G. (1998). Approximate statistical tests for comparing supervised classification learning algorithms. Neural computation, 10(7):1895–1923.
  • Diretiva 2012/27/UE do Parlamento Europeu e do Conselho, de 25 de outubro de 2012, relativa à eficiência energética, que altera as Diretivas 2009/125/CE e 2010/30/UE e revoga as Diretivas 2004/8/CE e 2006/32/CE Texto relevante para efeitos do EEE. (2012). 56.
  • Doherty, E., McNamara, G., Fitzsimons, L., and Clifford, E. (2017). Design and implementation of a performance assessment methodology cognisant of data accuracy for irish wastewater treatment plants. Journal of Cleaner Production, 165:1529–1541.
  • Drucker, H., Wu, D., and Vapnik, V. N. (1999). Support vector machines for spam categorization. IEEE Transactions on Neural networks, 10(5):1048–1054.
  • Durrenmatt, D. J. and Gujer, W. (2012). Data-driven modeling approaches to support wastewater treatment plant operation. Environmental Modelling & Soft- ware, 30:47–56.
  • Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., and Mizutani, K. (2017). State-of-the-art deep learning: Evolving machine intelligence toward to- morrow’s intelligent network traffic control systems. IEEE Communications Surveys & Tutorials, 19(4):2432–2455.
  • Fan, M., Hu, J., Cao, R., Ruan, W., and Wei, X. (2018). A review on experi- mental design for pollutants removal in water treatment with the aid of artificial intelligence. Chemosphere, 200:330–343.
  • Filipe, J., Bessa, R. J., Reis, M., Alves, R., and Povoa, P. (2019). Data-driven predictive energy optimization in a wastewater pumping station. Applied Energy, 252:113423.
  • Fiter, M., Gu¨ell, D., Comas, J., Colprim, J., Poch, M., and Rodr´ıguez-Roda, I. (2005). Energy saving in a wastewater treatment process: an application of fuzzy logic control. Environmental technology, 26(11):1263–1270.
  • Freund, Y., Schapire, R., and Abe, N. (1999). A short introduction to boost- ing. Journal-Japanese Society For Artificial Intelligence, 14(771-780):1612.
  • Gao, F., Nan, J., and Zhang, X. (2017). Simulating a cyclic activated sludge sys- tem by employing a modified asm3 model for wastewater treatment. Bioprocess and Biosystems Engineering, 40:877–890.
  • Golberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addion wesley, 1989(102):36.
  • Goldstein, R. and Smith, W. (2002). Water & sustainability (volume 4): US electricity consumption for water supply & treatment-the next half century. Electric Power Research Institute.
  • Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., and Ku- mar, P. (2021). Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular diversity, 25:1315–1360.
  • Han, H.-G., Zhang, L., Liu, H.-X., and Qiao, J.-F. (2018). Multiobjective design of fuzzy neural network controller for wastewater treatment process. Applied Soft Computing, 67:467–478.
  • Herrera, F. and Magdalena, L. (1997). Genetic fuzzy systems: A tutorial. Tatra Mt. Math. Publ.(Slovakia), 13:93–121.
  • Ho, T. K. (1995). Random decision forests. vol. 1. In Proceedings of 3rd international conference on document analysis and recognition, pages 278–282.
  • Holenda, B., Domokos, E., R´edey, A., and Fazakas, J. (2007). Aeration op- timization of a wastewater treatment plant using genetic algorithm. Optimal control applications and methods, 28(3):191–208.
  • Huang, G.-B., Zhu, Q.-Y., and Siew, C.-K. (2004). Extreme learning ma- chine: a new learning scheme of feedforward neural networks. In 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541), volume 2, pages 985–990. Ieee.
  • International Energy Agency. (2023). Energy and water exploring the interdependence of two critical resources. https://www.iea.org/topics/energy-and-water
  • ISO 50001 (2011). Energy Management. https://www.iso.org/iso-50001-energy-management.html
  • Kuster, C., Rezgui, Y., and Mourshed, M. (2017). Electrical load forecasting models: A critical systematic review. Sustainable cities and society, 35:257–270.
  • Li, R., Hu, S., Wang, Y., and Yin, M. (2017). A local search algorithm with tabu strategy and perturbation mechanism for generalized vertex cover problem. Neural Computing and Applications, 28:1775–1785.
  • Liang, N.-Y., Huang, G.-B., Saratchandran, P., and Sundararajan, N. (2006). A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans- actions on neural networks, 17(6):1411–1423.
  • Long, S. and Cudney, E. (2012). Integration of energy and environmental systems in wastewater treatment plants. International Journal of Energy and Environment (Print), 3.
  • Longo, S., d’Antoni, B. M., Bongards, M., Chaparro, A., Cronrath, A., Fa- tone, F., Lema, J. M., Mauricio-Iglesias, M., Soares, A., and Hospido, A. (2016a). Monitoring and diagnosis of energy consumption in wastewater treatment plants. a state of the art and proposals for improvement. Applied energy, 179:1251–1268.
  • Longo, S., d’Antoni, B. M., Bongards, M., Chaparro, A., Cronrath, A., Fa- tone, F., Lema, J. M., Mauricio-Iglesias, M., Soares, A., and Hospido, A. (2016b). Monitoring and diagnosis of energy consumption in wastewater treatment plants. a state of the art and proposals for improvement. Applied energy, 179:1251–1268.
  • Marsland, S. (2011). Machine learning: an algorithmic perspective. Chapman and Hall/CRC.
  • Martin H, J. A., de Lope, J., and Maravall, D. (2009). Adaptation, antic- ipation and rationality in natural and artificial systems: computational paradigms mimicking nature. Natural Computing, 8:757–775.
  • McCarthy, J. (2007). What is artificial intelligence. Technical report, Stanford University.
  • McCelloch, W. and Pitts, W. (1943). A logical calculus of the idea immanent in neural nets. Bulletin ofMathematical Biophysics, 5:115–133.
  • Mingzhi, H., Jinquan, W., Yongwen, M., Yan, W., Weijiang, L., and Xiaofei, S. (2009). Control rules of aeration in a submerged biofilm wastewater treatment process using fuzzy neural networks. Expert Systems with Applications, 36(7):10428–10437.
  • Mizuta, K. and Shimada, M. (2010). Benchmarking energy con- sumption in municipal wastewater treatment plants in japan. Water Science and Technology, 62(10):2256–2262.
  • Molinos-Senante, M., Hanley, N., and Sala-Garrido, R. (2015). Mea- suring the co2 shadow price for wastewater treatment: A directional distance function approach. Applied Energy, 144:241–249.
  • Molinos-Senante, M., Hern´andez-Sancho, F., Mochol´ı-Arce, M., and Sala-Garrido, R. (2014). Economic and environmental performance of wastewater treatment plants: Potential reductions in greenhouse gases emissions. Resource and Energy Economics, 38:125–140.
  • Nieto, P. G., Fernandez, J. A., de Cos Juez, F., Lasheras, F. S., and Muniz, C. D. (2013). Hybrid modelling based on support vector regression with genetic algorithms in forecasting the cyanotoxins presence in the trasona reservoir (northern spain). Environmental research, 122:1–10.
  • Nourani, V., Elkiran, G., and Abba, S. (2018). Wastewater treatment plant performance analysis using artificial intelligence–an ensemble approach. Water Science and Technology, 78(10):2064–2076.
  • of China, P. R. (2005). Summary environmant impact assessment henan wastew- ater management and water supply project. https://www.adb.org/sites/default/files/project- document/69417/prc-henan-seia.pdfhttps://www.adb.org/sites/default/files/project- document/69417/prc-henan-seia.pdf.
  • Oliveira, P., Fernandes, B., Analide, C., and Novais, P. (2021). Forecast- ing energy consumption of wastewater treatment plants with a transfer learning approach for sustainable cities. Electronics, 10(10):1149.
  • Ostojin, S., Mounce, S., and Boxall, J. (2011). An artificial intelligence approach for optimizing pumping in sewer systems. Journal of hydroinformatics, 13(3):295–306.
  • Pedrycz, W. (1990). Fuzzy sets in pattern recognition: methodology and methods. Pattern recognition, 23(1-2):121–146.
  • Plappally, A. et al. (2012). Energy requirements for water production, treatment, end use, reclamation, and disposal. Renewable and Sustainable Energy Reviews, 16(7):4818–4848.
  • Raduly, B., Gernaey, K. V., Capodaglio, A. G., Mikkelsen, P. S., and Henze, M. (2007). Artificial neural networks for rapid wwtp performance evaluation: Methodology and case study. Environmental modelling & software, 22(8):1208–1216.
  • Rajaee, T., Ebrahimi, H., and Nourani, V. (2019). A review of the artificial intelligence methods in groundwater level modeling. Journal of hydrology, 572:336–351.
  • Ramli, N. A. and Hamid, M. F. A. (2018). Data based modeling of a wastewater treatment plant by using machine learning methods. Journal of Engineering Tech- nology, 6:14–21.
  • Rosenblatt, F. (1958). Two theorems of statistical separability in the perceptron. United States Department of Commerce Washington, DC, USA.
  • Rosso, D. and Stenstrom, M. K. (2006). Surfactant effects on α- factors in aeration systems. Water research, 40(7):1397–1404.
  • Safeer, S., Pandey, R. P., Rehman, B., Safdar, T., Ahmad, I., Hasan, S. W., and Ullah, A. (2022). A review of artificial intelligence in water purification and wastewater treatment: Recent advancements. Journal of Water Process Engineering, 49:102974.
  • Salehi, H. and Burguen˜o, R. (2018). Emerging artificial intelligence methods in structural engineering. Engineering structures, 171:170–189.
  • Savic, D. A., Walters, G. A., and Schwab, M. (1997). Multiobjective genetic algorithms for pump scheduling in water supply. In Evolutionary Computing: AISB International Workshop Manchester, UK, April 7–8, 1997 Selected Papers, pages 227–235. Springer.
  • Shapiro, A. F. (2002). The merging of neural networks, fuzzy logic, and genetic algorithms. Insurance: Mathematics and Economics, 31(1):115–131.
  • Siddique, N. and Adeli, H. (2013). Computational intelligence: syner- gies of fuzzy logic, neural networks and evolutionary computing. John Wiley & Sons.
  • Silva, C. and Rosa, M. J. (2015). Energy performance indicators of wastewa- ter treatment: a field study with 17 portuguese plants. Water Science and Technology, 72(4):510– 519.
  • Singh, P., Carliell-Marquet, C., and Kansal, A. (2012). Energy pattern analysis of a wastewater treatment plant. Applied Water Science, 2:221–226.
  • Torregrossa, D., Hern´andez-Sancho, F., Hansen, J., Cornelissen, A., Popov, T., and Schutz, G. (2017). Energy saving in wastewater treatment plants: A plant- generic cooperative decision support system. Journal of Cleaner Production, 167:601–609.
  • Torregrossa, D., Leopold, U., Hern´andez-Sancho, F., and Hansen, J. (2018). Machine learning for energy cost modelling in wastewater treatment plants. Journal of environmental management, 223:1061–1067.
  • Torregrossa, D., Schutz, G., Cornelissen, A., Hern´andez-Sancho, F., and Hansen, J. (2016). Energy saving in wwtp: daily benchmarking under uncertainty and data availability limitations. Environmental research, 148:330–337.
  • Turing, A. (1936). On computable numbers, with an application to the entschei- dungs problem, 1936. The essential Turing: seminal writings in computing, logic, philosophy, artificial intelligence, and artificial life, plus the secrets of Enigma, page 58.
  • Vijayaraghavan, G., Jayalakshmi, M., et al. (2015). A quick review on applications of fuzzy logic in waste water treatment. Int. J. Res. Appl. Sci. Eng. Technol, 3(5):421–425.
  • Wang, J. and Deng, Z. (2016). Modeling and prediction of oyster norovirus outbreaks along gulf of mexico coast. Environmental health perspectives, 124(5):627–633.
  • Wei, X. and Kusiak, A. (2015). Short-term prediction of influent flow in wastewater treatment plant. Stochastic environmental research and risk assessment, 29:241–249.
  • Widrow, B. and Hoff, M. E. (1960). Adaptive switching circuits. In 1960 IRE WESCON Convention Record, Part 4, pages 96–104, New York. IRE.
  • Yang, L., Zeng, S., Chen, J., He, M., and Yang, W. (2010). Operational energy performance assessment system of municipal wastewater treatment plants. Water Science and Technology, 62(6):1361–1370.
  • Zadeh, L. (1965). Zadeh, fuzzy sets. Inform Control, 8:338–353.
  • Zadeh, L. A. (1983). The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets and Systems, 11:197–198.
  • Zhang, Y. and Pan, B. (2014). Modeling batch and column phosphate removal by hydrated ferric oxide-based nanocomposite using response surface methodology and artificial neural network. Chemical Engineering Journal, 249:111–120.
  • Zhang, Z., Kusiak, A., Zeng, Y., and Wei, X. (2016). Modeling and optimiza- tion of a wastewater pumping system with data-mining methods. Applied energy, 164:303–311.
  • Zhang, Z., Zeng, Y., and Kusiak, A. (2012). Minimizing pump energy in a wastewater processing plant. Energy, 47(1):505–514.
  • Zhao, L., Dai, T., Qiao, Z., Sun, P., Hao, J., and Yang, Y. (2020). Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse. Process Safety and Environmental Protection, 133:169–182.
LICENSE
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.