Volume 13, Issue 4 (Autumn 2024)
Arch Hyg Sci 2024, 13(4): 139-144 |
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Asadi-Ghalhari, Kazemizad T, Ranjdost F. Simulation Software as a Solution for Improving Wastewater Treatment Plant Performance: A Systematic Review. Arch Hyg Sci 2024; 13 (4) :139-144
URL: http://jhygiene.muq.ac.ir/article-1-731-en.html
URL: http://jhygiene.muq.ac.ir/article-1-731-en.html
1- Research Center for Environmental Pollutants, Department of Environmental Health Engineering, Faculty of Health, Qom University of Medical Sciences, Qom, Iran.
2- Student Research Committee, Qom University of Medical Sciences, Qom, Iran.
2- Student Research Committee, Qom University of Medical Sciences, Qom, Iran.
Abstract: (333 Views)
Background & Aims: Software used in the design and control of wastewater treatment processes has solved many design problems for engineers and researchers. These models are effective in analyzing conditions and improving process performance. Moreover, the software is used to control and predict the design conditions, as well as make the desired changes. There is much software available, and suitable software can be selected based on the needs and goals of each study.
Materials and Methods: Articles related to the study were identified by reviewing the PubMed, ScienceDirect, and Google Scholar databases using the keywords "Wastewater Treatment", "Modeling", and "Simulation Software."
Results: This review study examined published articles on AQUASIM, BIOWIN, GPS-X, SIMBA, STOAT, WEST, and SUMO software, highlighting their features and functionalities. Moreover, key aspects, such as software cost, open-source availability, and functional capabilities, were analyzed to evaluate the effectiveness and applicability of the software.
Conclusion: The study showed that the goals of using simulation software include optimizing and controlling the processes of existing systems, designing new systems, and gaining a better understanding of the process performance.
Materials and Methods: Articles related to the study were identified by reviewing the PubMed, ScienceDirect, and Google Scholar databases using the keywords "Wastewater Treatment", "Modeling", and "Simulation Software."
Results: This review study examined published articles on AQUASIM, BIOWIN, GPS-X, SIMBA, STOAT, WEST, and SUMO software, highlighting their features and functionalities. Moreover, key aspects, such as software cost, open-source availability, and functional capabilities, were analyzed to evaluate the effectiveness and applicability of the software.
Conclusion: The study showed that the goals of using simulation software include optimizing and controlling the processes of existing systems, designing new systems, and gaining a better understanding of the process performance.
Type of Study: Review Article |
Subject:
Special
Received: 2024/06/3 | Accepted: 2024/07/2 | Published: 2024/08/20
Received: 2024/06/3 | Accepted: 2024/07/2 | Published: 2024/08/20
References
1. 1. Sadri Moghaddam S, Pirali MR. Modeling and calibration of a full-scale wastewater treatment plant using GPS-X model (A case study of Tehran). J Num Meth Civ Eng. 2021;5(4):67-76. doi: 10.52547/nmce.5.4.67 [DOI:10.52547/nmce.5.4.67]
2. Latif, E. Applying novel methods in conventional activated sludge plants to treat low-strength wastewater. Environ Monit Assess. 2022;194:323. doi: 10.1007/s10661-022-09968-9 [DOI:10.1007/s10661-022-09968-9]
3. Juneidi SJ, Sorour MT, Aly SA. Proposed systematic approach for assessing different wastewater treatment plants alternatives: Case study of Aqaba city (South Jordan). Alexandria Eng J. 2022;61(12):12567-80. doi:10.1016/j.aej.2022.06.044 [DOI:10.1016/j.aej.2022.06.044]
4. Sid S, Volant A, Lesage G, Heran M. Cost minimization in a full-scale conventional wastewater treatment plant: Associated costs of biological energy consumption versus sludge production. Water Sci Technol. 2017;76(9):2473-81. doi: 10.2166/wst.2017.423 [DOI:10.2166/wst.2017.423]
5. Hvala N, Vrečko D, Levstek M, Bordon C. The use of dynamic mathematical models for improving the designs of upgraded wastewater treatment plants. J Sustain Dev Energy Water Environ Syst. 2017;5(1):15-31. doi:10.13044/j.sdewes.d5.0130 [DOI:10.13044/j.sdewes.d5.0130]
6. Moragaspitiya C, Rajapakse J, Senadeera W, Ali I. Simulation of dynamic behaviour of a biological wastewater treatment plant in South East Queensland, Australia using bio-win software. Eng J. 2017;21(3):1-22. doi:10.4186/ej.2017.21.3.1 [DOI:10.4186/ej.2017.21.3.1]
7. Oleyiblo OJ, Cao J, Feng Q, Wang G, Xue Z, Fang F. Evaluation and improvement of wastewater treatment plant performance using BioWin. Chinese J Oceanol Limnol. 2015;33(2):468-76. doi:10.1007/s00343-015-4108-8 [DOI:10.1007/s00343-015-4108-8]
8. Vitanza R, Colussi I, Cortesi A, Gallo V. Implementing a respirometry-based model into BioWin software to simulate wastewater treatment plant operations. J Water Proc Eng. 2015;9:267-75. doi:10.1016/j.jwpe.2015.02.007 [DOI:10.1016/j.jwpe.2015.02.007]
9. Jafarinejad S. A framework for the design of the future energy-efficient, cost-effective, reliable, resilient, and sustainable full-scale wastewater treatment plants. Curr Opin Environ Sci Health. 202013:91-100. doi:10.1016/j.coesh.2020.01.001 [DOI:10.1016/j.coesh.2020.01.001]
10. Revilla M, Galán B, Viguri J. Analysis of simulation tools and optimization of the operational conditions for biofilm activated sludge industrial process. Int J Environ Sci Technol. 2018;15(12):2499-510. doi: 10.1007/s13762-017-1626-2 [DOI:10.1007/s13762-017-1626-2]
11. Hamawand I, Baillie C. Anaerobic digestion and biogas potential: simulation of lab and industrial-scale processes. Energies. 2015;8(1):454-74. doi: 10.3390/en8010454 [DOI:10.3390/en8010454]
12. Cao J, Yang E, Xu C, Zhang T, Xu R, Fu B, et al. Model-based strategy for nitrogen removal enhancement in full-scale wastewater treatment plants by GPS-X integrated with response surface methodology. Sci Total Environ. 2021;769:144851. [DOI:10.1016/j.scitotenv.2020.144851]
13. Comeau Y, Takàcs I. Schematic representation of activated sludge models. Water Environ Federation. 2008; 2008(13): 3266-82. doi:10.2175/193864708788733224 [DOI:10.2175/193864708788733224]
14. Gernaey KV, Van Loosdrecht MC, Henze M, Lind M, Jørgensen SB. Activated sludge wastewater treatment plant modelling and simulation: state of the art. Environ Model Softw. 2004;19(9):763-83. doi:10.1016/j.envsoft.2003.03.005 [DOI:10.1016/j.envsoft.2003.03.005]
15. Faris AM, Zwain HM, Hosseinzadeh M, Majdi HS, Siadatmousavi SM. Start-up and operation of novel EN-MBBR system for sidestreams treatment and sensitivity analysis modeling using GPS-X simulation. Alexandria Eng J. 2022;61(12):10805-18. doi:10.1016/j.aej.2022.04.026 [DOI:10.1016/j.aej.2022.04.026]
16. Sean WY, Chu YY, Mallu LL, Chen JG, Liu HY. Energy consumption analysis in wastewater treatment plants using simulation and SCADA system: Case study in northern Taiwan. J Clean Product. 2020;276:124248. doi:10.1016/j.jclepro.2020.124248 [DOI:10.1016/j.jclepro.2020.124248]
17. Al‐Sayed A. Hellal MS, Al‐Shemy MT, Hassan GK. Performance evaluation of submerged membrane bioreactor for municipal wastewater treatment: Experimental study and model validation with GPS‐X software simulator. Water Environ J. 2023;37(3):480-92. doi:10.1111/wej.12852 [DOI:10.1111/wej.12852]
18. Khalaf AH, Ibrahim, W, Fayed M, Eloffy M. Comparison between the performance of activated sludge and sequence batch reactor systems for dairy wastewater treatment under different operating conditions. Alexandria Eng J. 2021;60(1):1433-45. doi:10.1016/j.aej.2020.10.062 [DOI:10.1016/j.aej.2020.10.062]
19. Devesa F, Comas J, Turon C, Freixó A, Carrasco F, Poch M. Scenario analysis for the role of sanitation infrastructures in integrated urban wastewater management. Environ Model Softw. 2009;24(3):371-80. doi:10.1016/j.envsoft.2008.08.003 [DOI:10.1016/j.envsoft.2008.08.003]
20. Odeibat AS, Mohammad R, Abu-Zreig M. Integrated environmental management and GPS-X modelling for current and future sustainable wastewater treatment: A case study from the Middle East". Heliyon. 2024;10(14):e34164. doi: 10.1016/j.heliyon.2024.e34164 [DOI:10.1016/j.heliyon.2024.e34164]
21. Ontiveros GA, Campanella EA. Environmental performance of biological nutrient removal processes from a life cycle perspective. Bioresour Technol. 2013;150:506-12. doi: 10.1016/j.biortech.2013.08.059 [DOI:10.1016/j.biortech.2013.08.059]
22. Elawwad A, Matta M, Abo-Zaid M, Abdel-Halim H. Plant-wide modeling and optimization of a large-scale WWTP using BioWin's ASDM model. J Water Proc Eng. 2019;31:100819. doi: 10.1016/j.jwpe.2019.100819 [DOI:10.1016/j.jwpe.2019.100819]
23. Jabari P, Yuan Q, Oleszkiewicz JA. Potential of hydrolysis of particulate COD in extended anaerobic conditions to enhance biological phosphorous removal. Biotechnol Bioeng. 2016;113(11):2377-85. doi: 10.1002/bit.25999 [DOI:10.1002/bit.25999]
24. Robescu D, Calin A, Robescu D, Nasaramba B. Simulation of attached growth biological wastewater treatment process in the mobile bed biofilm reactor. Proceeding of the 10th WSEAS international conference on mathematic and computers in biology and chemistry; 2009. Link
25. TEAM TE. Together, over 100 years of wastewater process modeling experience. Link
26. Balakrishnan A, Kanchinadham SBK, Kalyanaraman C. Assessment on biodegradability prediction of tannery wastewater using EPI Suite BIOWIN model. Environ Monit Assess. 2020;192(11):732. doi: 10.1007/s10661-020-08661-z [DOI:10.1007/s10661-020-08661-z]
27. Liwarska-Bizukojc E, Olejnik D, Biernacki R, Ledakowicz S. Calibration of a complex activated sludge model for the full-scale wastewater treatment plant. Bioprocess Biosyst Eng. 2011;34(6):659-70. doi: 10.1007/s00449-011-0515-1 [DOI:10.1007/s00449-011-0515-1]
28. Vineyard, D, Karthikeyan K, Davidson C, Barak P. Improving BioWin modeling of phosphorus solubilization in acid-phase digesters. Environments. 2024;11(2):31. doi:10.3390/environments11020031 [DOI:10.3390/environments11020031]
29. Elawwad A, Abo-Zaid M, Edward M. Plant-wide simulation for a mega WWTP: A case study of Gabal El-Asfar WWTP, Egypt. The International Conference of Recent Trends in Environmental Science and Engineering (RTESE'17); 2017. doi:10.11159/rtese17.139 [DOI:10.11159/rtese17.139]
30. Magalhães TM, Tonetti AL, Bueno DAC, Tonon D. 2Nitrification process modeling in intermittent sand filter applied for wastewater treatment. Ecol Eng. 2016;93:18-23. doi: 10.1016/j.ecoleng.2016.05.003 [DOI:10.1016/j.ecoleng.2016.05.003]
31. Mburu N, Rousseau DP, Stein OR, Lens PN. Simulation of batch-operated experimental wetland mesocosms in AQUASIM biofilm reactor compartment. J Environ Manage. 2014 Feb 15;134:100-8. doi: 10.1016/j.jenvman.2014.01.005 [DOI:10.1016/j.jenvman.2014.01.005]
32. Lindamulla LMLKB, Jegatheesan V, Jinadasa KBSN, Nanayakkara KGN, Othman MZ. Integrated mathematical model to simulate the performance of a membrane bioreactor. Chemosphere. 2021;284:131319. doi: 10.1016/j.chemosphere.2021.131319 [DOI:10.1016/j.chemosphere.2021.131319]
33. Galanopoulos C, Sazakli E, Leotsinidis M, Lyberatos G. A pilot-scale study for modeling a free water surface constructed wetlands wastewater treatment system. J Environ Chem Eng. 2013;1(4):642-51. doi:10.1016/j.jece.2013.09.006 [DOI:10.1016/j.jece.2013.09.006]
34. Research Ea. Abteilung systemanalyse, integrated assessment und modellierung: Eawag aquatic research; [Software]. Link
35. Reichert P. Aquasim 2.0-user manual. Swiss Federal Institute for Environmental Science and Technology Dubendorf, Switzerland;1998. Link
36. Pijáková I, Derco J. Application of dynamic simulations for assessment of urban wastewater systems operation. Chem Biochem Eng Quart. 2015;29(1):55-62. doi: 10.15255/CABEQ.2014.2127 [DOI:10.15255/CABEQ.2014.2127]
37. Alex J, Tschepetzki R, Jumar U. Predictive control of nitrogen removal in WWTPs using parsimonious models. IFAC Proceedings Volumes. 2002;35(1):405-10. doi: 10.3182/20020721-6-ES-1901.01460 [DOI:10.3182/20020721-6-ES-1901.01460]
38. Lacour C, Schütze M. Real-time control of sewer systems using turbidity measurements. Water Sci Technol. 2011;63(11):2628-32. doi:10.2166/wst.2011.159 [DOI:10.2166/wst.2011.159]
39. Krieg T, Enzmann F, Sell D, Schrader J, Holtmann D. Simulation of the current generation of a microbial fuel cell in a laboratory wastewater treatment plant. Appl Energy. 2017;195:942-9. doi:10.1016/j.apenergy.2017.03.101 [DOI:10.1016/j.apenergy.2017.03.101]
40. Satpathy P, Steinigeweg S, Uhienhut F, Siefert E. Application of anaerobic digestion model 1 (ADM1) for prediction of biogas production. Int J Sci Eng Res. 2013;4(12):86-9. Link
41. Kommunikation IfAu. SIMBA - Simulation software for water and wastewater systems. Institut für Automation und Kommunikation. Link
42. Abdulla F, Farahat S. Impact of climate change on the performance of wastewater treatment plant: Case study Central Irbid WWTP (Jordan). Procedia Manufact. 2020;44:205-12. doi:10.1016/j.promfg.2020.02.223 [DOI:10.1016/j.promfg.2020.02.223]
43. Esposito G, Bastianutti C, Bortone G, Pirozzi F, Sgroi S. Influence of primary sedimentation on pre-denitrification system performances. Water Sci Technol. 2001;44(1):113-20. Link [DOI:10.2166/wst.2001.0027]
44. Issa HM. Optimization of wastewater treatment plant design using process dynamic simulation: a case study from Kurdistan, Iraq. ARO-The Sci J Koya Univ. 2019;7(1):59-66. doi: 10.14500/aro.10488 [DOI:10.14500/aro.10488]
45. Akuma DA, Hundie KB., Bentu AF. Textile wastewater treatment modelling and design using stoat graph and summary statistic model. Pak J Anal Environ Chem. 2022;23(1):32-40. doi:10.21743/pjaec/2022.06.03 [DOI:10.21743/pjaec/2022.01.03]
46. Abdissa Akuma D, Beyecha Hundie K, Aga Bullo T. Performance improvement of textile wastewater treatment plant design by STOAT model simulation. Environ Health Eng Manag J. 2022;9(3):213-21. doi: 10.34172/EHEM.2022.22 [DOI:10.34172/EHEM.2022.22]
47. Gao YN, Liu XZ, Zhang RX, Shan JJ, Sun MQ, Zong ZX, et al. Operation and management of Liaoning waste water treatment plants by STOAT Simulation. MATEC Web of Conferences; 2016: EDP Sciences. 2016;63. doi: 10.1051/matecconf/20166304019 [DOI:10.1051/matecconf/20166304019]
48. Sarkar U, Dasgupta D, Bhattacharya T, Pal S, Chakroborty T. Dynamic simulation of activated sludge based wastewater treatment processes: Case studies with Titagarh Sewage treatment plant, India. Desalination. 2010;252(1-3):120-6. doi: 10.1016/j.desal.2009.10.014 [DOI:10.1016/j.desal.2009.10.014]
49. Wang W, Shi C, Yang J, Zeng M, Dai Z, Zhang Z. Modelling performance of oxidation ditch in wastewater treatment plant by STOAT software. IOP Conf Ser: Earth Environ Sci. 2019;300(3). doi:10.1088/1755-1315/300/3/032065 [DOI:10.1088/1755-1315/300/3/032065]
50. Water Research Centre. STOAT: Wastewater plant modelling tool. WRC. Link
51. Gazsó Z, Házi F, Kenyeres I, Váci L. Full-scale wastewater treatment plant simulation for real-time optimization. Water Pract Technol. 207;12(4):848-56. doi:10.2166/wpt.2017.091 [DOI:10.2166/wpt.2017.091]
52. Bingoa, MN, Basiterea M, Ntwampea SKO. Poultry slaughterhouse wastewater treatment plant design advancements. 16th SOUTH AFRICA Int'l Conference on Agricultural, Chemical, Biological & Environmental Sciences (ACBES-19). 131(684). doi:10.17758/EARES8.EAP1119145 [DOI:10.17758/EARES8.EAP1119145]
53. Liu SY, Liang S, Chen ZQ, Ma YG, Gao WC, Tian XY, et al. Mathematical modeling and indirect carbon emission reduction analysis of urban wastewater treatment systems under different temperature conditions. Water. 2024;16(21):3039. doi: 10.3390/w16213039 [DOI:10.3390/w16213039]
54. Dynamita. Sumo software. dynamita. Link
55. Group D. Wastewater treatment process modelling WEST. DHI. Link
56. Vanhooren H, Meirlaen J, Amerlinck Y, Claeys F, Vangheluwe H, Vanrolleghem PA. WEST: modelling biological wastewater treatment. J Hydroinformatics. 2003;5(1):27-50. doi:10.2166/hydro.2003.0003 [DOI:10.2166/hydro.2003.0003]
57. Meirlaen J, Van Assel J, Vanrolleghem P. Real time control of the integrated urban wastewater system using simultaneously simulating surrogate models. Water Sci Technol. 2002;45(3):109-16. Link [DOI:10.2166/wst.2002.0066]
58. Meirlaen J, Schütze M, Van der Stede D, Butler D, Vanrolleghem P. Fast, parallel simulation of the integrated urban wastewater system. Proceedings WAPUG Spring meeting; 2000. Link
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