Skip to main content
SpringerLink
Log in

The Application of Statistical Process Control Techniques for Quality Improvement of the Municipal Wastewater-Treated Process

  • Research Article-Chemical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Statistical process control techniques are useful tools for monitoring the production process and detecting abnormal process behavior due to special causes. Once the special causes for abnormal process behavior are identified and consequently eliminated, the process can be further improved. The aim of this study is to apply univariate and multivariate statistical process control techniques to enhance the monitoring of a wastewater treatment process and achieve a higher effluent quality. Phase I, Shewhart univariant control charts and Hotelling’s T2 multivariate control chart were developed for non-correlated and correlated variables of a wastewater treatment plant, respectively. Five representative water quality parameters: turbidity, total suspended solids (TSS), chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN) and fecal coliform were investigated. The interpretation of the Phase I for both types of control charts initially revealed that the process was out of statistical control conditions for all the investigated variables. T2 decomposition technique revealed that the main contributors for the out-of-control points were turbidity with 67% (average T2 = 28.86) followed by TKN 25% (average T2 = 31.12). The assignable causes for the observed abnormalities were the result of seasonal variations with respect to the temperature in such hot climates. Control charts proved their applicability for the wastewater process as a quick and efficient monitoring strategy despite the complex nature of the wastewater and the contribution of the hot climate in the Arabian Gulf region.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References:

  1. Paniagua-Michel, J.: Bioremediation with microalgae: Toward sustainable production of biofuels. In: Handbook of Marine Microalgae: Biotechnology Advances, pp. 471–481. Elsevier Inc. (2015). https://doi.org/10.1016/B978-0-12-800776-1.00031-5.

  2. Bărbulescu, A.; Barbeş, L.: Statistical methods for assessing water quality after treatment on a sequencing batch reactor. Sci. Total Environ. 752, 141991 (2021). https://doi.org/10.1016/j.scitotenv.2020.141991

    Article  Google Scholar 

  3. Newhart, K.B.; Holloway, R.W.; Hering, A.S.; Cath, T.Y.: Data-driven performance analyses of wastewater treatment plants: A review. Water Res. 157, 498–513 (2019). https://doi.org/10.1016/j.watres.2019.03.030

    Article  Google Scholar 

  4. Smeti, E.M.; Thanasoulias, N.C.; Kousouris, L.P.; Tzoumerkas, P.C.: An approach for the application of statistical process control techniques for quality improvement of treated water. Desalination 213, 273–281 (2007). https://doi.org/10.1016/j.desal.2006.03.613

    Article  Google Scholar 

  5. Aizenchtadt, E.; Ingman, D.; Friedler, E.: Quality control of wastewater treatment: a new approach. Eur. J. Oper. Res. 189(2), 445–458 (2008). https://doi.org/10.1016/j.ejor.2007.06.001

    Article  MATH  Google Scholar 

  6. Usman, A.; Kontagora, N.M.: Statistical process control on production: A case study of some basic chemicals used in pure water production. Pak. J. Nutr. 9(4), 387–391 (2010). https://doi.org/10.3923/pjn.2010.387.391

    Article  Google Scholar 

  7. Acuña Chinchilla, S.R., et al.: Statistical process control in the assessment of drip irrigation using wastewater. Engenharia Agrícola 38(1), 47–54 (2018). https://doi.org/10.1590/1809-4430-eng.agric.v38n1p47-54/2018

    Article  Google Scholar 

  8. Corbett, C.J.; Pan, J.N.: Evaluating environmental performance using statistical process control techniques. Eur. J. Oper. Res. 139(1), 68–83 (2002). https://doi.org/10.1016/S0377-2217(01)00155-2

    Article  MATH  Google Scholar 

  9. da Conceição, K.Z.; Boas, M.A.V.; Sampaio, S.C.; Remor, M.B.; Bonaparte, D.I.: Statistical control of the process applied to the monitoring of the water quality index. Engenharia Agricola 38(6), 951–960 (2018). https://doi.org/10.1590/1809-4430-Eng.Agric.v38n6p951-960/2018

    Article  Google Scholar 

  10. Szekut, F.D., et al.: Monitoring of drippers during wastewater application through statistical quality control. Aust. J. Crop Sci. 14(4), 551–556 (2020). https://doi.org/10.21475/ajcs.20.14.04.p1237

    Article  Google Scholar 

  11. Lee, J.M.; Yoo, C.K.; Lee, I.B.: Efficient fault detection using multivariate exponentially weighted moving average and independent component analysis. Comput. Aided Chem. Eng. 15(C), 916–921 (2003). https://doi.org/10.1016/S1570-7946(03)80424-8

    Article  Google Scholar 

  12. Nugraha, J.; Fatimah, I.; Prabowo, R.G.: Control of wastewater using multivariate control chart. AIP Conf. Proc. 1823(1), 020126 (2017). https://doi.org/10.1063/1.4978199

    Article  Google Scholar 

  13. Garcia-Alvarez, D.; Fuente, M.J.; Vega, P.; Sainz, G.: Fault detection and diagnosis using multivariate statistical techniques in a wastewater treatment plant. IFAC Proc. Vol. 42(11), 952–957 (2009). https://doi.org/10.3182/20090712-4-TR-2008.00156

    Article  Google Scholar 

  14. Capilla, C.: Application and simulation study of the Hotelling’s T2 control chart to monitor a wastewater treatment process. Environ. Eng. Sci. 26(2), 333–342 (2009). https://doi.org/10.1089/EES.2007.0358

    Article  MathSciNet  Google Scholar 

  15. Aguado, D.; Rosen, C.: Multivariate statistical monitoring of continuous wastewater treatment plants. Eng. Appl. Artif. Intell. 21(7), 1080–1091 (2008). https://doi.org/10.1016/J.ENGAPPAI.2007.08.004

    Article  Google Scholar 

  16. Montgomery, D.C.: Introduction to Statistical Quality Control, 7th edn. Wiley, New York (2013)

    MATH  Google Scholar 

  17. Tran, K.P.: Designing of Run Rules t control charts for monitoring changes in the process mean. Chemom. Intell. Lab. Syst. 174, 85–93 (2018). https://doi.org/10.1016/j.chemolab.2018.01.009

    Article  Google Scholar 

  18. Navidi, W.C.: Statistics for Engineers and Scientists, 5th edn. McGraw-Hill Education, New York (2020)

    Google Scholar 

  19. Gülbay, M.; Kahraman, C.: Development of fuzzy process control charts and fuzzy unnatural pattern analyses. Comput. Stat. Data Anal. 51(1), 434–451 (2006). https://doi.org/10.1016/j.csda.2006.04.031

    Article  MathSciNet  MATH  Google Scholar 

  20. Noskievičová, D.: Complex control chart interpretation. Int. J. Eng. Bus. Manag. (2013). https://doi.org/10.5772/56441

    Article  Google Scholar 

  21. Fu, X.; Wang, R.; Dong, Z.: Application of a Shewhart control chart to monitor clean ash during coal preparation. Int. J. Min. Process. 158, 45–54 (2017). https://doi.org/10.1016/J.MINPRO.2016.11.019

    Article  Google Scholar 

  22. Howard, J.P.; Beyers, J.F.; Smith, C.E.; Weems, K.S.; Moore, R.H.: Statistics for engineers. Teach. Learn. Math. (2020). https://doi.org/10.1201/9781351245586-8

    Article  Google Scholar 

  23. Chou, Y.-M.; Polansky, A.M.; Mason, R.L.: Transforming non-normal data to normality in statistical process control. J. Qual. Technol. 30(2), 133–141 (1998). https://doi.org/10.1080/00224065.1998.11979832

    Article  Google Scholar 

  24. Frontiers in Statistical Quality Control 6. Physica-Verlag HD, 2001. doi: https://doi.org/10.1007/978-3-642-57590-7.

  25. Ebrahimi, M.; Gerber, E.L.; Rockaway, T.D.: Temporal performance assessment of wastewater treatment plants by using multivariate statistical analysis. J. Environ. Manag. 193, 234–246 (2017). https://doi.org/10.1016/J.JENVMAN.2017.02.027

    Article  Google Scholar 

  26. Kane, V.E.: Defect Prevention: Use of Simple Statistical Tools, 1st edn. CRC Press, New York (1989)

    Google Scholar 

  27. Anhøj, J.; Olesen, A.V.: Run charts revisited: A simulation study of run chart rules for detection of non-random variation in health care processes. https://doi.org/10.1371/journal.pone.0113825

  28. Williams, E.: Understanding variation: Part1—the run chart. Curr. Probl. Pediatr. Adolesc. Health Care 48(7), 186–190 (2018). https://doi.org/10.1016/j.cppeds.2018.08.012

    Article  Google Scholar 

  29. Flott, L.W.: Run charts as a test of system performance. Met. Finish. 110(9), 36–38 (2012). https://doi.org/10.1016/S0026-0576(13)70191-0

    Article  Google Scholar 

  30. Mucha, Z.; Kułakowski, P.: Turbidity measurements as a tool of monitoring and control of the SBR effluent at the small wastewater treatment plant—preliminary study. Arch. Environ. Protect. 42(3), 33–36 (2016)

    Article  Google Scholar 

  31. Seborg, D.E.; Edgar, T.F.; Mellichamp, D.A.; Doyle, F.J.: Process dynamics and control, p. 502.

  32. Flott, L.W.: Introduction to control charts. Met. Finish. 110(6), 36–38 (2012). https://doi.org/10.1016/S0026-0576(13)70220-4

    Article  Google Scholar 

  33. Khatri, N.; Khatri, K.K.; Sharma, A.: Artificial neural network modelling of fecal coliform removal in an intermittent cycle extended aeration system-sequential batch reactor based wastewater treatment plant. J. Water Process Eng. 37, 101477 (2020). https://doi.org/10.1016/j.jwpe.2020.101477

    Article  Google Scholar 

  34. Jackson, M.R.; Meschke, J.S.; Simmons, J.; Isaksen, T.B.: Fecal coliform concentrations in effluent from ultraviolet disinfection units installed in onsite wastewater treatment systems. J. Water Health 17(1), 113–123 (2019). https://doi.org/10.2166/WH.2018.256

    Article  Google Scholar 

  35. Farrell, C.; Hassard, F.; Jefferson, B.; Leziart, T.; Nocker, A.; Jarvis, P.: Turbidity composition and the relationship with microbial attachment and UV inactivation efficacy. Sci. Total Environ. 624, 638–647 (2018). https://doi.org/10.1016/J.SCITOTENV.2017.12.173

    Article  Google Scholar 

  36. Alisawi, H.A.O.: Performance of wastewater treatment during variable temperature. Appl Water Sci 10(4), 89 (2020). https://doi.org/10.1007/s13201-020-1171-x

    Article  Google Scholar 

  37. Guo, J.; Zhang, L.; Chen, W.; Ma, F.; Liu, H.; Tian, Y.: The regulation and control strategies of a sequencing batch reactor for simultaneous nitrification and denitrification at different temperatures. Biores. Technol. 133, 59–67 (2013). https://doi.org/10.1016/j.biortech.2013.01.026

    Article  Google Scholar 

  38. Dutta, A.; Sarkar, S.: Sequencing batch reactor for wastewater treatment: recent advances. Curr. Pollut. Rep. 1(3), 177–190 (2015). https://doi.org/10.1007/s40726-015-0016-y

    Article  Google Scholar 

  39. Wąsik, E.; Jurík, Ľ; Chmielowski, K.; Operacz, A.; Bugajski, P.: Infrastructure and ecology of rural areas statistical process control of removal of nitrogen compounds in the wastewater treatment plant in krosno. Pol. Acad. Sci. 4, 1699–1711 (2017). https://doi.org/10.14597/infraeco.2017.4.2.128

    Article  Google Scholar 

  40. Ostad-Ali-Askari, K.; Shayannejad, M.; Ghorbanizadeh-Kharazi, H.: Artificial neural network for modeling nitrate pollution of groundwater in marginal area of Zayandeh-rood River, Isfahan, Iran. KSCE J. Civ. Eng. 21(1), 134–140 (2016). https://doi.org/10.1007/S12205-016-0572-8

    Article  Google Scholar 

  41. Prinčič, A.; Mahne, I.; Megušar, F.; Paul, E.A.; Tiedje, J.M.: Effects of pH and oxygen and ammonium concentrations on the community structure of nitrifying bacteria from wastewater. Appl. Environ. Microbiol. 64(10), 3584–3590 (1998). https://doi.org/10.1128/aem.64.10.3584-3590.1998

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Chemical Engineering, College of Engineering, University of Bahrain, P.O. Box 32038, Zallaq, Kingdom of Bahrain

    Zainab Mohammed Redha, Qais Bu-Ali, Fatema Ali Ebrahim, Batool Hameed Jaafar & Sara Ruknudin Khattak

Authors
  1. Zainab Mohammed Redha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qais Bu-Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fatema Ali Ebrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Batool Hameed Jaafar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sara Ruknudin Khattak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zainab Mohammed Redha.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohammed Redha, Z., Bu-Ali, Q., Ebrahim, F.A. et al. The Application of Statistical Process Control Techniques for Quality Improvement of the Municipal Wastewater-Treated Process. Arab J Sci Eng 48, 8613–8628 (2023). https://doi.org/10.1007/s13369-022-07122-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-022-07122-8

Keywords

Search

Navigation