This review paper deals with the previous and current wastewater treatment plant modelling. The future of semantic modelling in a wastewater treatment plant through a new approach, Artificial Immune Systems (AIS), is introduced. AIS is still in the infant stage of soft computing. However, it has gained its popularity in the recent years, especially in prediction modelling. The first dynamic model of the activated sludge system was developed in the 1970s, and has been further developed since then. The process of a wastewater treatment is very complex, non-linear and characterised by many uncertainties within the influent parameters. The operation of a wastewater treatment process is limited because it is affected by variety of physical, chemical, and biological factors. A review of the wastewater modelling development was presented. The models' limitations were identified and a new technique in wastewater treatment plant is finally discussed.
| Published in | American Journal of Environmental and Resource Economics (Volume 2, Issue 1) |
| DOI | 10.11648/j.ajere.20170201.13 |
| Page(s) | 22-26 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Activated Sludge, Artificial Immune System, Modelling, Revolution, Wastewater Treatment Plant
| [1] | Henze, M, van Loosdrecht, MCM, Ekama, GA& Brdjanovic, D 2008, ‘Wastewater Treatment Development, in Biological Wastewater Treatment. Principles, Modelling and Design’, eds Henze, M, van Loosdrecht, MCM, Ekama, G. A. & Brdjanovic, D, IWA Publishing, London. |
| [2] | Azizi, S, Valipour, A & Sithebe, T, 2013, ‘Evaluation of Different Wastewater Treatment Processes and Development of a Modified Attached Growth Bioreactor as a Decentralized Approach for Small Communities’, The Scientific World Journal. |
| [3] | Butler, R. & MacCormick, T 1996, ‘Opportunities for Decentralized Treatment, Sewer Mining, and Effluent Reuse’, Desalination, vol. 106, pp. 273–283. |
| [4] | Otterpohl, R., Grottker, M, & Lange, J1997, ‘Sustainable Water and Waste Management in Urban Areas’, Water Science and Technology, vol. 35, no. 9, pp. 121–133. |
| [5] | Paraskevas, PA, Giokas, DL & Lekkas, TD 2002, ‘Wastewater Management in Coastal Urban Areas: The Case of Greece’, Water Science and Technology, vol. 46, no. 8, pp. 177–186. |
| [6] | USEPA (United States Environmental Protection Agency) 2005, ‘Handbook for Managing Onsite and Clustered (Decentralized) Wastewater Treatment Systems’, Office of Water, Washington, DC. |
| [7] | Goodman, BL & Englande, AJ 1974, ‘A Unified Model of the Activated Sludge Process’, Journal Water Pollution Control Fed., vol. 46, pp. 312−332. |
| [8] | Robertson, D, Bundy, A, Muetzelfeldt, R, Haggith, M& Uschold, M1991, ‘Eco-logic: Logic-Based Approaches to Ecological Modelling’, MIT Press, Boston, MA. |
| [9] | Wenzel, V 1992, ‘Semantics and Syntax Elements of a Unique Calculus for Modelling of Complex Ecological-Systems’, Ecological Modelling, vol. 63, pp. 113–131. |
| [10] | Keller, RM& Dungan, JL 1999, ‘Meta-Modelling: A Knowledge-based Approach to Facilitating Process Model Construction and Reuse’, Ecological Modelling, vol. 119, pp. 89–116. |
| [11] | Muetzelfeldt, R 2004, ‘Declarative Modelling in Ecological and Environmental Research’, in European Commission Directorate-General for Research, European Commission, Belgium. |
| [12] | Muetzelfeldt, R & Massheder, J 2003,‘The Simile visual modelling environment’, European Journal of Agronomy, vol. 18, pp. 345–358. |
| [13] | Richmond, B 2001, ‘An Introduction to Systems Thinking: STELLA Software’, High Performance Systems Inc., Hanover, NH. |
| [14] | Ayesa, E, Oyarbide, G, Larrea, L, & Garcı´a-Heras, J. L 1995, ‘Observability of Reduced Order Models. Application to a Model for Control of Alpha Process’, Water Science and Technology, vol. 31, no. 2, pp. 161–70. |
| [15] | Dochain, D, 1991, ‘Design of Adaptive Controllers for Non-Linear Stirred Tank Bioreactors: Extension to the MIMO Situation’, Journal of Process Control, vol. 1, pp. 41–58. |
| [16] | Moreno, R., de Prada, C., Lafuente, J., Poch, M. & Montague, G, 1992. ‘Non-Linear Predictive Control of Dissolved Oxygen in the Activated Sludge Process’, ICCAFT 5/IFAC-BIO 2 Conference, Keystone, pp. 289–293. |
| [17] | Nejjari, F, Benhammou, A, Dahhou, B & Roux, G 1997, ‘Nonlinear Multivariable Control of a Biological Wastewater Treatment Process’, IEEE, ECC 97, Brussels, Belgium. |
| [18] | Henze, M, Grady, CPL, Jr, Gujer, W, Marais GR& Matsuo, T 1987, ‘Activated Sludge Model No. 1’, IAWPRC Scientific and Technical Reports 1, IAWPRC, London. |
| [19] | Henze, M, Gujer, W, Mino, T, Matsuo, T, Wentzel, MC& Marais, GVR 1995, ‘Activated Sludge Model No. 2’, IAWPRC Scientific and Technical Reports 3, IAWPRC, London. |
| [20] | Okubo, T, Kubo, K, Hosomi, M& Murakami, A 1994, ‘A Knowledge-Based Decision Support System for Selecting Small-Scale Wastewater Treatment Processes’, Water Science and Technology, vol. 30, no. 2, pp. 175–184. |
| [21] | Serra, P, Sa`nchez, M, Lafuente, J, Corte´s, U & Poch, M 1997, ‘ISCWAP: A Knowledge-Based System for Supervising Activated Sludge Processes’, Computers in Chemical Engineering, vol. 21, no. 2, pp. 211–221. |
| [22] | Zhu, XX & Simpson, AR 1996, ‘Expert System for Water Treatment Plant Operation’, Journal of Environmental Engineering, pp. 822–829. |
| [23] | Sa`nchez, M, Corte´s, UR, Roda, I, Poch, M& Lafuente, J 1997, ‘Learning and Operation in WWTP through Case-Based Reasoning’, Microcomputers in Civil Engineering, vol. 12, no. 4, pp. 251–66. |
| [24] | Capodaglio, AG, Jones, HV, Novotny, V. & Feng, X 1991, ‘Sludge Bulking Analysis and Forecasting: Application of System Identification and Artificial Neural Computing Technologies’, Water Research, vol. 25, no. 10, pp. 1217–1224. |
| [25] | Coˆte´, M., Grandjean, BPA, Lessard, P. & Thibault, J 1995, ‘Dynamic Modelling of the Activated Sludge Process: Improving Prediction using Neural Networks’, Water Research, vol. 29, no. 4, pp. 995–1004. |
| [26] | Sa`nchez, M, Corte´s, UR, Lafuente, J, Roda, IR& Poch, M 1996, ‘DAI-DEPUR: ADistributed Architecture for Wastewater Treatment Plants Supervision’, Artificial Intelligence in Engineering, vol. 10, no. 3, pp. 275–285. |
| [27] | Zhao, H, Hao, OJ& McAvoy, TJ 1997, Modelling Nutrient Dynamics in a Sequencing Batch Reactor, Journal of Environmental Engineering, vol. 123, pp. 3110–3119. |
| [28] | Novak, O, Franz, A, Svardal, K, Muller, V, & Kuhn, V 1999, ‘Parameter Estimation for Activated Sludge Models with Help of Mass Balances’, Water Science Technology, vol. 39, no. 4, pp. 113−120. |
| [29] | Du, H. B., Shao, H. H., & Yao, P. J., 2006. Adaptive Neural Network Control for a Class of Low-Triangular-Structured Nonlinear Systems. IEEE Transactions on Neural Networks 17 (2), pp. 509−514. |
| [30] | Wang, LJ& Chen, CB 2008, ‘Support Vector Machine Applying in the Prediction of Effluent Quality of Sewage Treatment Plant with Cyclic Activated Sludge System Process’, in Proceedings IEEE International Symposium on Knowledge Acquisition and Modelling Workshop, KAM Workshop 2008, pp. 647–650. |
| [31] | Elmolla, SE, Chaudhuri, M& Mohamed, ME, 2010, ‘The Use of Artificial Neural Network (ANN) for Modelling of COD Removal from Antibiotic Aqueous Solution by the Fenton Process’, Journal of Hazardous Materials, vol. 179, no. 1−3, pp. 127−134. |
| [32] | Onat, M& Dogruel, M 2004, ‘Fuzzy Plus Integral Control of The Effluent Turbidity in Direct Filtration’, Control Systems Technology, vol. 12, pp. 65−74. |
| [33] | Guergachi, AA & Patry, GG 2006, ‘Constructing a Model Hierarchy with Background Knowledge for Structural Risk Minimization: Application to Biological Treatment of Wastewater. Systems, Man and Cybernetics, Part A: Systems and Humans’, IEEE, vol. 36, pp. 373−383. |
| [34] | Esra, Y & Sukran, Y 2011,‘Prediction of Primary Treatment Effluent Parameters by Fuzzy Inference System (FIS) Approach’, Procedia Computer Science, vol. 3, pp. 659−665. |
| [35] | Qiao, JF, Yang, WW & Yuan, MZ 2011, ‘Recurrent High Order Neural Network Modelling for Wastewater Treatment Process’, Journal of Computers, vol. 6, no. 8, pp. 1570−1577. |
| [36] | Ting, SC, Ismail, AR& Malek, MA 2012, ‘Bio-Engineered Solution in Enhancement of Septic Sludge with Artificial Immune System’, in Proceedings of the 2nd International Conference on Water Resources (ICWR 2012), 5-6 November 2012, Langkawi, Kedah. |
| [37] | Ting, SC, Malek, MA& Ismail, AR 2014, ‘Prediction Analysis of Effluent Removal in Septic Sludge Treatment Plant: A Biomimetics Engineering Approach’, Journal of Environmental Science Processes & Impacts, vol. 16, pp. 2208−2214. |
| [38] | Henze M, Gujer W, Mino, T, Matsuo, T, Wentzel MC, Marais GvR & van Loosdrecht, MCM 1999, ‘Activated sludge model no. 2d: ASM 2d’, Water Science Technology, vol. 39, no., 1, pp. 165−182. |
| [39] | Gujer, W, Henze, M, Mino, T & van Loosdrecht, MCM 1999, ‘Activated Sludge Model No. 3’, Water Science Technology, vol. 39, no. 1, pp. 183−193. |
| [40] | Henze, M, Gujer, W, Mino, T., & van Loosdrecht, M. C. M 2000. ‘Activated Sludge Models ASM1, ASM2, ASM2d and ASM3’, IWA Task Group on Mathematical Modelling for Design and Operation of Biological Wastewater Treatment, IWA Publishing, London, UK. |
| [41] | Sergiu, C, Mihaelaand, S & Maruan, B 2007, ‘Predictive Control of a Wastewater Treatment Process’, International Journal of Computers, Communications & Control, vol. 2, no. 2, pp. 132−142. |
| [42] | Belanche, L, Valdes, JJ, Comas, J, Roda, IR, & Poch, M, 2000. ‘Prediction of the Bulking Phenomenon in Wastewater Treatment Plants’, Artificial Intelligence in Engineering, vol. 14, pp. 307−317. |
| [43] | Environment Protection Agency (EPA) 1999, ‘Wastewater Technology Fact Sheet Sequencing Batch Reactors’, Office of Water, Washington, D. C. |
| [44] | Daniel, R, Sanfins, A & Belo, O 2013, ‘Wastewater Treatment Plant Performance Prediction with Support Vector Machine’, ed Perner, P, pp. 99–111. |
| [45] | Huang, MZ, Ma, YW, Wan, JQ& Wang, Y 2010,‘A Fast Predicting Neural Fuzzy Model for On-line Estimation of Nutrient Dynamics in an Anoxic/anoxic Process’, Bioresource Technology, vol. 101, pp. 1642−1651. |
| [46] | Petersen, B, Gernaey, K, Henze, M & Vanrolleghem, PA 2002, ‘Evaluation of an ASM 1 Model Calibration Procedure on Municipal-Industrial Wastewater Treatment Plant’, IWA Publishing, pp. 15−38. |
| [47] | Ye, H, Luo, F & Xu, Y 2009, ‘Application of RBF Network Based on Immune Algorithm to Predicting of Wastewater Treatment’, Springer, Verlag Berlin Heidelberg, pp. 1197−1202. |
| [48] | Harremoes, P, Capodaglio, AG, Hellstrom, BGM, Henze, KN Jensen, A, Lynggaaard-Jensen, R, Otterpohl, & Soeborg, H 1993, ‘Wastewater Treatment Plants under Transient Loading Performance, Modeling and Control’, Water Science and Technology, vol. 27, no. 12, pp. 71–115. |
| [49] | Ting, SC, Ismail, AR, & Malek, MA 2013, ‘Development of Effluent Removal Prediction Model Efficiency in Septic Sludge Treatment Plant through Clonal Selection Algorithm’, Journal of Environmental Management, vol. 129, pp. 260−265. |
| [50] | Hamoda, MF, Ghusain, IA & Hassan, AH 1999, ‘Integrated Wastewater Treatment Plant Performance Evaluation Using Artificial Neural Networks’, Water Science Technology, vol. 40, pp. 55–65. |
| [51] | Hamed, M. Khalafallah, MG, & Hassanein, EA 2004, ‘Prediction of Wastewater Treatment Plant Performance using Artificial Neural Network’, Environmental Modelling and Software, vol. 19, pp. 919–928. |
| [52] | Mjalli, FS, Al-Asheh, S, &Alfadala, HE 2007, ‘Use of Artificial Neural Network Black-Box Modelling for the Prediction of Wastewater Treatment Plants Performance’, Journal of Environmental Management, vol. 83, pp. 329–338. |
| [53] | Timmis, J, Martyn, A, Wolfgang, B& Andy, T 2006, ‘Going Back to Our Roots: Second Generation Biocomputing’, International Journal on Unconventional Computing, vol. 2, no. 4, pp. 349–382. |
| [54] | Dasgupta, D 1999, ‘Artificial Immune Systems and their Applications’, Springer. |
| [55] | Timmis, J, Hart, E, Hone, A, Neal, M, Robins, A, Stepney, S& Tyrrell, A 2008, ‘Immuno-engineering’, in Proceedings 2nd International Conference on Biologically Inspired Collaborative Computing (IFIP), vol. 268, pp. 3−17. |
| [56] | Enezi, AJR, Abbod, MF& Alsharhan, S 2010, Artificial Immune Systems - Models, Algorithms and Applications, International Journal of Research and Reviews in Applied Sciences (IJRRAS), vol. 3, no. 2, pp. 118−131. |
| [57] | Cohen, IR 2006, ‘Immune System Computation and the Immunological Homunculus’, in Proceedings of the 9th International Conference on Model Driven Engineering Languages and Systems (MoDELS), 4199 of LNCS, Springer, pp. 499–512. |
| [58] | De Castro, LN & Timmis, JI 2003, ‘Artificial Immune Systems as a Novel Soft Computing Paradigm’, Soft Computing Fusion Foundation Methodology Application, vol. 7, pp. 526−544. |
| [59] | Aickelin, U & Dasgupta, D 2005, ‘Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques’, in Artificial Immune System Burke, eds KB& Kendall, G, Chapter 13. |
| [60] | Singh, CT& Nair, SB 2005, ‘An Artificial Immune System for a Multi-Agent Robotic System’, World Academy of Science, Engineering and Technology, vol. 6, June 2005, pp. 208−311. |
| [61] | Dai, HW, Yang, Y & Li, CH 2010. ‘Distance Maintaining Compact Quantum Crossover Based Clonal Selection Algorithm’, Journal of Convergence Information Technology, vol. 5, no. 10, pp. 56−65. |
| [62] | Dragan, S. & Svetlana, S 2011, ‘Artificial Immune System in Risk of Falling Classification’, Third World Congress on Nature and Biologically Inspired Computing, IEEE, pp. 627−632, 2011. |
| [63] | Timmis, J 2007, ‘Artificial Immune Systems - Today and Tomorrow’, Natural Computing, vol. 6, no. 1, pp. 1-18. |
| [64] | De Castro, LN & Zuben, VFJ 2000, ‘The Clonal Selection Algorithm with Engineering Applications’, in Workshop on Artificial Immune Systems and Their Applications, pp. 36−37. |
APA Style
Ting Sie Chun, M. A. Malek, Amelia Ritahani Ismail. (2017). A Review of Wastewater Treatment Plant Modelling: Revolution on Modelling Technology. American Journal of Environmental and Resource Economics, 2(1), 22-26. https://doi.org/10.11648/j.ajere.20170201.13
ACS Style
Ting Sie Chun; M. A. Malek; Amelia Ritahani Ismail. A Review of Wastewater Treatment Plant Modelling: Revolution on Modelling Technology. Am. J. Environ. Resour. Econ. 2017, 2(1), 22-26. doi: 10.11648/j.ajere.20170201.13
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ajere.20170201.13,
author = {Ting Sie Chun and M. A. Malek and Amelia Ritahani Ismail},
title = {A Review of Wastewater Treatment Plant Modelling: Revolution on Modelling Technology},
journal = {American Journal of Environmental and Resource Economics},
volume = {2},
number = {1},
pages = {22-26},
doi = {10.11648/j.ajere.20170201.13},
url = {https://doi.org/10.11648/j.ajere.20170201.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajere.20170201.13},
abstract = {This review paper deals with the previous and current wastewater treatment plant modelling. The future of semantic modelling in a wastewater treatment plant through a new approach, Artificial Immune Systems (AIS), is introduced. AIS is still in the infant stage of soft computing. However, it has gained its popularity in the recent years, especially in prediction modelling. The first dynamic model of the activated sludge system was developed in the 1970s, and has been further developed since then. The process of a wastewater treatment is very complex, non-linear and characterised by many uncertainties within the influent parameters. The operation of a wastewater treatment process is limited because it is affected by variety of physical, chemical, and biological factors. A review of the wastewater modelling development was presented. The models' limitations were identified and a new technique in wastewater treatment plant is finally discussed.},
year = {2017}
}
TY - JOUR T1 - A Review of Wastewater Treatment Plant Modelling: Revolution on Modelling Technology AU - Ting Sie Chun AU - M. A. Malek AU - Amelia Ritahani Ismail Y1 - 2017/01/20 PY - 2017 N1 - https://doi.org/10.11648/j.ajere.20170201.13 DO - 10.11648/j.ajere.20170201.13 T2 - American Journal of Environmental and Resource Economics JF - American Journal of Environmental and Resource Economics JO - American Journal of Environmental and Resource Economics SP - 22 EP - 26 PB - Science Publishing Group SN - 2578-787X UR - https://doi.org/10.11648/j.ajere.20170201.13 AB - This review paper deals with the previous and current wastewater treatment plant modelling. The future of semantic modelling in a wastewater treatment plant through a new approach, Artificial Immune Systems (AIS), is introduced. AIS is still in the infant stage of soft computing. However, it has gained its popularity in the recent years, especially in prediction modelling. The first dynamic model of the activated sludge system was developed in the 1970s, and has been further developed since then. The process of a wastewater treatment is very complex, non-linear and characterised by many uncertainties within the influent parameters. The operation of a wastewater treatment process is limited because it is affected by variety of physical, chemical, and biological factors. A review of the wastewater modelling development was presented. The models' limitations were identified and a new technique in wastewater treatment plant is finally discussed. VL - 2 IS - 1 ER -
Information
Email: