Advanced Supervisory Control System Implemented at Full-Scale WWTP—A Case Study of Optimization and Energy Balance Improvement - Publication - Bridge of Knowledge

Your account

login

Search

Advanced Supervisory Control System Implemented at Full-Scale WWTP—A Case Study of Optimization and Energy Balance Improvement

Abstract

In modern and cost-eective Wastewater Treatment Plants (WWTPs), processes such as aeration, chemical feeds and sludge pumping are usually controlled by an operating system integrated with online sensors. The proper verification of these data-driven measurements and the control of different unit operations at the same time has a strong influence on better understanding and accurately optimizing the biochemical processes at WWTP—especially energy-intensive biological parts (e.g., the nitrification zone/aeration system and denitrification zone/internal recirculation). In this study, by integrating a new powerful PreviSys with data driven from the Supervisory Control and Data Acquisition (SCADA) software and advanced algorithms such as Model Predictive Control (MPC) by using the WEST computer platform, it was possible to conduct different operation strategies for optimizing and improving the energy balance at a full-scale “Klimzowiec” WWTP located in Chorzow (Southern Poland). Moreover, the novel concept of double-checking online data-driven measurements (from installed DO, NO3, NH4 sensors, etc.) by mathematical modelling and computer simulation predictions was applied in order to check the data uncertainty and develop a support operator system (SOS)—an additional tool for the widely-used in-operation and control of modern andcost-effective WWTPs. The results showed that by using sophisticated PreviSys technology, a better understanding and accurate optimization of biochemical processes, as well as more sustainable WWTP operation, can be achieved.

Citations

  • 1 1

    CrossRef

  • 0

    Web of Science

  • 1 4

    Scopus

Author (1)

Cite as

Full text

download paper
downloaded 36 times
Publication version
Accepted or Published Version
License
Creative Commons: CC-BY open in new tab

Keywords

Details

Category:
Articles
Type:
artykuły w czasopismach
Published in:
Water no. 11, pages 1218 - 1240,
ISSN: 2073-4441
Language:
English
Publication year:
2019
Bibliographic description:
Drewnowski J.: Advanced Supervisory Control System Implemented at Full-Scale WWTP—A Case Study of Optimization and Energy Balance Improvement// Water -Vol. 11,iss. 6 (2019), s.1218-1240
DOI:
Digital Object Identifier (open in new tab) 10.3390/w11061218
Bibliography: test
  1. Swinarski, M.; Mąkinia, J.; Czerwionka, K.; Chrzanowska, M.; Drewnowski, J. Modeling external carbon addition in combined N-P activated sludge systems with an extension of the IWA activated sludge models. Water Environ. Res. 2012, 84, 646-655. [CrossRef] [PubMed] open in new tab
  2. Drewnowski, J.; Remiszewska-Skwarek, A.; Fernandez-Morales, F.J. Model based evaluation of plant improvement at a large wastewater treatment plant (WWTP). J. Environ. Sci. Health Part A. 2018, 53, 669-675. [CrossRef] open in new tab
  3. Qiu, Y.; Li, J.; Huang, X.; Shi, H. A Feasible Data-Driven Mining System to Optimize Wastewater Treatment Process Design and Operation. Water 2018, 10, 1342. [CrossRef] open in new tab
  4. Gujer, W. Activated sludge modelling: Past, present and future. Water Sci. Technol. 2006, 53, 111-119. [CrossRef] [PubMed] open in new tab
  5. Zarrad, W.; Harmand, J.; Devisscher, M.; Steyer, J.P. Comparison of advanced control strategies for improving the monitoring of activated sludge processes. Control Eng. Pract. 2004, 12, 323-333. [CrossRef] open in new tab
  6. Olsson, G.; Nielses, M.K.; Yuan, Z.; Lynggaard-Jensen, A.; Steyer, J.P. Adding realism to simulated sensors and actuators. Water Sci. Technol. 2008, 57, 337-344.
  7. Yong, M.; Yong-Zhen, P.; Jeppsson, U. Dynamic evaluation of integrated control strategies for enhanced nitrogen removal in activated sludge process. Control Eng. Pract. 2006, 14, 1269-1278. [CrossRef] open in new tab
  8. Yong, M.; Yong-Zhen, P.; Xiao-Lian, W.; Shu-Ying, W. Intelligent control aeration and external carbon addition for improving nitrogen removal. Environ. Model. Softw. 2006, 21, 821-828. [CrossRef] open in new tab
  9. Holenda, B.; Domokos, E.; Rédey, Á.; Fazakas, J. Dissolved oxygen control of the activated sludge wastewater treatment process using model predictive control. Comput. Chem. Eng. 2008, 32, 1270-1278. [CrossRef] open in new tab
  10. Stare, A.; Vrecko, D.; Hvala, N.; Strmcnik, S. Comparison of control strategies for nitrogen removal in an activated sludge process in terms of operating costs: A simulation study. Water Res. 2007, 41, 2004-2014. [CrossRef] open in new tab
  11. Ayesa, E.; De la Sota, A.; Grau, P.; Sagarna, J.M.; Salterain, A.; Suescun, J. Supervisory control strategies for the new WWTP of Galindo-Bilbao: The long run from the conceptual design to the full-scale experimental validation. Water Sci. Technol. 2006, 53, 193-201. [CrossRef] [PubMed] open in new tab
  12. Grau, P.; de Gracia, M.; Vanrolleghem, P.A.; Ayesa, E. A new plant-wide modelling methodology for WWTPs. Water Res. 2007, 41, 4357-4372. [CrossRef] [PubMed] open in new tab
  13. Szaja, A.; Aguilar, J.A.; Łagód, G. Estimation of chemical oxygen demand fractions of municipal wastewater by respirometric method-Case study. Annual Set the Environment Protection/Rocznik OchronaŚrodowiska 2015, 17, 289-299.
  14. Desjardins, M.-A.; Belanger, G.; Elmonayiri, D.S.; Stephenson, J. Wastewater Treatment Plant Optimization Using a Dynamic Model Approach. In Proceedings of the Sixth International Water Technology Conference, IWTC 2001, Alexandria, Egypt, 23-25 March 2001; pp. 370-377.
  15. Drewnowski, J.; Zmarzły, M. The use of mathematical models for diagnosis of activated sludge systems in WWTP. E3S Web Conf. 2017, 22, 00037. [CrossRef] Water 2019, 11, 1218 20 of 22 open in new tab
  16. Longo, S.; Mirko d'Antoni, B.; Bongards, M.; Chaparro, A.; Cronrath, A.; Fatone, F.; Lema, J.M.; Mauricio-Iglesias, M.; Soares, A.; Hospido, A. Monitoring and diagnosis of energy consumption in wastewater treatment plants. A state of the art and proposals for improvement. Appl. Energy 2016, 179, 1251-1268. [CrossRef] open in new tab
  17. Krampe, J. Energy benchmarking of South Australian WWTPs. Water Sci. Technol. 2013, 67, 2059-2066. [CrossRef] open in new tab
  18. Bodik, I.; Kubaská, M. Energy and sustainability of operation of a wastewater treatment plant. Environ. Prot. Eng. 2013, 39, 15-24. open in new tab
  19. Pan, T.; Zhu, X.; Ye, Y. Estimate of life-cycle greenhouse gas emissions from a vertical subsurface flow constructed wetland and conventional wastewater treatment plants: A case study in China. Ecol. Eng. 2011, 37, 248-254. [CrossRef] open in new tab
  20. Lackner, S.; Gilbert, E.M.; Vlaeminck, S.E.; Joss, A.; Horn, H.; van Loosdrecht, M.C. Full-scale partial nitritation/anammox experiences-an application survey. Water Res. 2014, 55, 292-303. [CrossRef] open in new tab
  21. Rodriguez-Garcia, G.; Molinos-Senante, M.; Hospido, A.; Hernández-Sancho, F.; Moreira, M.T.; Feijoo, G. Environmental and economic profile of six typologies of wastewater treatment plants. Water Res. 2011, 45, 5997-6010. [CrossRef] open in new tab
  22. Stamm, C.; Eggen, R.I.; Hering, J.G.; Hollender, J.; Joss, A.; Schärer, M. Micropollutant Removal from Wastewater: Facts and Decision-Making Despite Uncertainty. Environ. Sci. Technol. 2015, 49, 6374-6375. [CrossRef] open in new tab
  23. Gori, R.; Jiang, L.-M.; Sobhani, R.; Rosso, D. Effects of soluble and particulate substrate on the carbon and energy footprint of wastewater treatment processes. Water Res. 2011, 45, 5858-5872. [CrossRef] [PubMed] open in new tab
  24. Gao, H.; Scherson, Y.D.; Wells, G.F. Towards energy neutral wastewater treatment: methodology and state of the art. Environ. Sci. Process. Impacts 2014, 16, 1223-1246. [CrossRef] [PubMed] open in new tab
  25. Zhou, X.; Wu, Y.; Shi, H.; Song, Y. Evaluation of oxygen transfer parameters of fine-bubble aeration system in plug flow aeration tank of wastewater treatment plant. J. Environ. Sci. 2014, 25, 295-301. [CrossRef] open in new tab
  26. Rieger, L.; Gillot, S.; Langergraber, G.; Ohtsuki, T.; Shaw, A.; Takacs, I.; Winkler, S. Guidelines for Using Activated Sludge Models; IWA Publishing: London, UK, 2012. open in new tab
  27. Nelder, J.A.; Mead, R.A. A simplex method for function minimization. Comput. J. 1965, 7, 308-313. [CrossRef] open in new tab
  28. Banaszek, P. Klimzowiec według algorytmu. Kierunek WOD-KAN 2014, 3, 26-29. open in new tab
  29. APHA; AWWA; WEF. Standard Methods for the Examination of Water and Wastewater, 22nd ed.; American Public Health Association: Washington, DC, USA, 2012; ISBN 978-087553-013-0.
  30. WEST-Modelling Wastewater Treatment Plants: Short Description; Mike by DHI: Hørsholm, Denmark, 2012.
  31. Vangheluwe, H.L.; Claeys, F.; Vansteenkiste, G.C. The WEST++ wastewater treatment plant modelling and simulation environment. In 10th European Simulation Symposium, Nottingham, UK, 26-28 October 1998;
  32. Bergiela, A., Kerckhoffs, E., Eds.; Society for Computer Simulation (SCS): Nottingham, UK, 1998. open in new tab
  33. Vanhooren, H.; Meirlaen, J.; Amerlinck, Y.; Claeys, F.; Vangheluwe, H.; Vanrolleghem, P.A. WEST: Modelling biological wastewater treatment. J. Hydroinform. 2003, 5, 27-50. [CrossRef] open in new tab
  34. Henze, M.; Gujer, W.; Mino, T.; Matsuo, T.; Wentzel, M.C.; Marais, G.v.R.; Van Loosdrecht, M.C.M. Activated sludge model No.2d, ASM2D. Water Sci. Technol. 1999, 39, 165-182. [CrossRef] open in new tab
  35. Henze, M.; Grady, C.P.L., Jr.; Gujer, W.; Marais, G.V.R.; Matsuo, T. Activated Sludge Model No 1; IAWPRC Scientific and Technical Reports, No 1; IAWPRC: London, UK, 1987. open in new tab
  36. Henze, M.; Gujer, W.; Mino, T.; Matsuo, T.; Wentzel, M.C.; Marais, G.V.R. Activated Sludge Model No. 2; IAWPRC Scientific and Technical Reports, No 3; IAWPRC: London, UK, 1995.
  37. Henze, M.; Gujer, W.; Mino, T.; Matsuo, T.; Wentzel, M.C.; Marais, G.V.R. Wastewater and biomass characterization for the activated sludge model No. 2: Biological phosphorus removal. Water Sci. Technol. 1995, 31, 13-23. [CrossRef] open in new tab
  38. Henze, M.; Gujer, W.; Mino, T.; van Loosdrecht, M.C.M. Activated Sludge Models ASM1, ASM2, ASM2d and ASM3; IWA Scientific and Technical Report No. 9; IWA Publishing: London, UK, 2000. open in new tab
  39. Ruano, M.V.; Ribes, J.; De Pauw, D.J.W.; Sin, G. Parameter subset selection for the dynamic calibration of activated sludge models (ASMs): Experience versus systems analysis. Water Sci. Technol. 2007, 56, 107-115. [CrossRef] open in new tab
  40. Manga, J.; Ferrer, J.; Seco, A.; Garcia-Usach, F. Design of nutrient removal activated sludge systems. Water Sci. Technol. 2003, 47, 115-122. [CrossRef] [PubMed] open in new tab
  41. Boontian, N. A calibration approach towards reducing ASM2d parameter subsets in phosphorus removal processes. World Acad. Sci. Eng. Technol. 2012, 64, 984-990.
  42. Machado, V.C.; Tapia, G.; Gabriel, D.; Lafuente, J.; Baeza, J.A. Systematic identifiability study based on the Fisher Information Matrix for reducing the number of parameters calibration of an activated sludge model. Environ. Model. Softw 2009, 24, 1274-1284. [CrossRef] open in new tab
  43. Brun, R.; Kühni, M.; Siegrist, H.; Gujer, W.; Reichert, P. Practical identifiability of ASM2d parameters-Systematic selection and tuning of parameter subsets. Water Res. 2002, 36, 4113-4127. [CrossRef] open in new tab
  44. Yagci, N.; Insel, G.; Tasli, R.; Artan, N.; Randall, C.W.; Orhon, D. A new interpretation of ASM2d for modeling of SBR performance for enhanced biological phosphorus removal under different P/HAc ratios. Biotechnol. Bioeng. 2003, 93, 258-270. [CrossRef] [PubMed] open in new tab
  45. Meijer, S.C.F.; van Loosdrecht, M.C.M.; Heijnen, J.J. Metabolic Modeling of Full-Scale Biological Nitrogen and Phosphorus Removing WWTP's. Water Res. 2001, 35, 2711-2733. [CrossRef] open in new tab
  46. Makinia, J.; Drewnowski, J.; Swinarski, M.; Czerwionka, K. Internal vs. External (Alternative) Carbon Sources for Denitrification and EBPR Accomplished by a Full-Scale Biomass. In Proceedings of the Water Environment Federation/International Water Association 2nd Nutrient Removal, Specialty Conference, Washington, DC, USA, 28 June-1 July 2009; Water Environment Federation: Alexandria, VA, USA, 2009; pp. 16-30. open in new tab
  47. Makinia, J. Performance Prediction of Full-Scale Biological Nutrient Removal Systems Using Complex Activated Sludge Models. In Veröffentlichungen des Institutes für Siedlungswasser-wirtschaft und Abfalltechnik der Universität Hannover; Unidruck of Leibniz, Universität Hannover: Hannover, Germany, 2006.
  48. Brdjanovic, D.; van Loosdrecht, M.C.M.; Versteeg, P.; Hooijmans, C.M.; Alaerts, G.J.; Heijnen, J.J. Modeling COD, N and P Removal in a Full Scale WWTP Haarlem Waarderpolder. Water Res. 2000, 34, 846-858. [CrossRef] open in new tab
  49. Drewnowski, J.; Makinia, J. The role of biodegradable particulate and colloidal organic compounds in biological nutrient removal activated sludge systems. Int. J. Environ. Sci. Technol. 2014, 11, 1973-1988. [CrossRef] open in new tab
  50. Weijers, S.R.; Vanrolleghem, P.A. A procedure for selecting best identifiable parameters in calibrating Activated Sludge Model No. 1 to full-scale plant data. Water Sci. Technol. 1997, 36, 69-79. [CrossRef] open in new tab
  51. Brun, R.; Reichert, P.; Künsch, H.R. Practical identifiability analysis of large environmental simulation models. Water Resour. Res. 2001, 37, 1015-1030. [CrossRef] open in new tab
  52. Ferrer, J.; Morenilla, J.J.; Bouzas, A.; García-Usach, F. Calibration and simulation of two large wastewater treatment plants operated for nutrient removal. Water Sci. Technol. 2004, 50, 87-94. [CrossRef] [PubMed] open in new tab
  53. Penya-Roja, J.M.; Seco, A.; Ferrer, J.; Serralta, J. Calibration and validation of activated sludge model No. 2d for Spanish municipal wastewater. Environ. Technol. 2002, 23, 849-862. [CrossRef] [PubMed] open in new tab
  54. Vrečko, D.; Hvala, N.; Carlsson, B. Feedforward-feedback control of an activated sludge process: A simulation study. Water Sci. Technol. 2003, 47, 19-26. [CrossRef] [PubMed] open in new tab
  55. Ingildson, P. Realising Full-Scale Control in Wastewater Treatment Systems Using In-Situ Nutrient Sensors. Ph.D. Thesis, Lund University, Lund, Sweden, 2002.
  56. Qiu, Y.; Shi, H.; He, M. Nitrogen and Phosphorous Removal in Municipal Wastewater Treatment Plants in China: A Review. Int. J. Chem. Eng. 2010, 2010, 914159. [CrossRef] open in new tab
  57. Zhang, Z.; Kusiak, A.; Zeng, Y.; Wei, X. Modeling and optimization of a wastewater pumping system with data-mining methods. Appl. Energy 2016, 164, 303-311. [CrossRef] open in new tab
  58. Kusiak, A.; Zeng, Y.; Zhang, Z. Modeling and analysis of pumps in a wastewater treatment plant: A data-mining approach. Eng. Appl. Artif. Intell. 2013, 26, 1643-1651. [CrossRef] open in new tab
  59. Kusiak, A.; Wei, X. A data-driven model for maximization of methane production in a wastewater treatment plant. Water Sci. Technol. 2012, 65, 1116-1122. [CrossRef] [PubMed] open in new tab
  60. Zhu, J.; Kang, L.; Anderson, P.R. Predicting influent biochemical oxygen demand: Balancing energy demand and risk management. Water Res. 2018, 128, 304-313. [CrossRef] [PubMed] open in new tab
  61. Haimi, H.; Mulas, M.; Corona, F.; Vahala, R. Data-derived soft-sensors for biological wastewater treatment plants: An overview. Environ. Model. Softw. 2013, 47, 88-107. [CrossRef] open in new tab
  62. Duerrenmatt, D.J.; Gujer, W. Data-driven modeling approaches to support wastewater treatment plant operation. Environ. Model. Softw. 2012, 30, 47-56. [CrossRef] Water 2019, 11, 1218 22 of 22 open in new tab
  63. Jeppsson, U.; Rosen, C.; Alex, J.; Copp, J.; Gernaey, K.V.; Pons, M.-N.; Vanrolleghem, P.A. Towards a benchmark simulation model for plant-wide control strategy performance evaluation of WWTPs. In Proceedings of the 6th International Symposium on Systems Analysis and Integration Assessment, Beijing, China, 3-5 November 2004. open in new tab
  64. Kroiss, H.; Cao, Y. Energy considerations. In Activated Sludge-100 Years and Counting;
  65. Jenkins, D., Wanner, J., Eds.; IWA Publishing: London, UK, 2014; pp. 221-244.
  66. Hernandez-del-Olmo, F.; Llanes, F.H.; Gaudioso, E. An emergent approach for the control of wastewater treatment plants by means of reinforcement learning techniques. Exp. Syst. Appl. 2012, 39, 2355-2360. [CrossRef] open in new tab
  67. Zhao, Y.; Guo, L.; Liang, J.; Zhang, M. Seasonal artificial neural network model for water quality prediction via a clustering analysis method in a wastewater treatment plant of China. Desalin. Water Treat. 2016, 57, 3452-3465. [CrossRef] open in new tab
  68. Guerrini, A.; Romano, G.; Indipendenza, A. Energy Efficiency Drivers in Wastewater Treatment Plants: A Double Bootstrap DEA Analysis. Sustainability 2017, 9, 1126. [CrossRef] open in new tab
  69. Turunen, V.; Sorvari, J.; Mikola, A. A decision support tool for selecting the optimal sewage sludge treatment. Chemosphere 2018, 193, 521-529. [CrossRef] open in new tab
  70. © 2019 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). open in new tab
Verified by:
Gdańsk University of Technology

seen 123 times

Recommended for you

The experimental evaluation and modeling of SBR removing nutrients under varied aeration conditions

2015

Computer Simulation in Predicting Biochemical Processes and Energy Balance at WWTPs

2018

The Simulation of Activated Sludge System for Optimization of Predictive Aeration at Large WWTP

2015

Innovations in Wastewater Treatment: Harnessing Mathematical Modeling and Computer Simulations with Cutting-Edge Technologies and Advanced Control Systems

2023
Meta Tags