Clean Technologies and Environmental Policy

, Volume 18, Issue 1, pp 339–346 | Cite as

An exact approach for the prioritization process of industrial influents in wastewater systems

  • M. Verdaguer
  • J. Suy
  • M. Villaret
  • N. Clara
  • M. Bofill
  • M. Poch
Brief Report


In wastewater systems, the efficiency of the treatment process is strongly related to the composition of its influent. When the treatment is overloaded (in volume and/or pollutants), its efficiency decreases and the effluent cannot attain the quality required by the receiving waters. This work considers the problem of mixing multiple wastewater streams, with multiple contaminants, into a single stream (the influent) on which various specifications are imposed. The problem has recently been solved by probabilistic methods that can achieve a nearly optimal solution. In this paper, an exact technique is proposed to find the optimal solution with a mixed-integer linear programming solver for the first time. The procedure is applied to a case study with different industrial effluents whose discharges will compose the influent to a treatment plant with constrained capacity (both in volume and pollutant loads). The optimal utility solution achieved describes the discharges that satisfy all constraints. This proposal constitutes an efficient way to manage treatment influents while reducing the computational time required by two orders of magnitude compared to probabilistic methods.


Wastewater management Industrial influents Wastewater systems Mixed-integer linear programming Exact methods 


  1. Alnouri SY, Linke P, El-Halwagi M (2014) Water integration in industrial zones: a spatial representation with direct recycle applications. Clean Technol Environ Policy 16:1637–1659CrossRefGoogle Scholar
  2. Capón-García E, Guillén-Gosálbez G, Espuña A (2013) Integrating process dynamics within batch process scheduling via mixed-integer dynamic optimization. Chem Eng Sci 102:139–150CrossRefGoogle Scholar
  3. Corominas L, Acuña V, Ginebreda A, Poch M (2013) Integration of freshwater environmental policies and wastewater treatment plant management. Sci Total Environ 445:185–191CrossRefGoogle Scholar
  4. Davis BJ, Taylor LA, Manousiouthakis VI (2008) Identification of the attainable región for batch reactor networks. Ind Eng Chem Res 47(10):3388–3400CrossRefGoogle Scholar
  5. Deng C, Feng X, Wen Z (2013) Optimization of water network integrated with process models. Clean Technol Environ Policy 15:473–487CrossRefGoogle Scholar
  6. Devesa F, Comas J, Turon C, Freixó A, Carrasco F, Poch M (2009) Scenario analysis for the role of sanitation infrastructures in integrated urban wastewater management. Environ Model Softw 24:371–380CrossRefGoogle Scholar
  7. Diari Oficial de la Generalitat de Catalunya (DOGC) of November 21 (2003) 4015, 22823–22839Google Scholar
  8. Diari Oficial de la Generalitat de Catalunya (DOGC) of December 31 (2008) 5288, 94876–94877Google Scholar
  9. Dorigo M, Stützle T (2008) The ant colony optimization metaheuristic: algorithms, applications, and advances. In: Glover F, Kochenberger GA (eds) Handbook of metaheuristics. Kluwer Academic Publishers, Dordrech, pp 250–285Google Scholar
  10. Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B 26:29–41CrossRefGoogle Scholar
  11. Estrada V, Di Maggio J, Díaz MS (2011) Water sustainability: a systems engineering approach to restoration of eutrophic lakes. Comput Chem Eng 35:1598–1613CrossRefGoogle Scholar
  12. Fréville A (2004) The multidimensional 0-1 knapsack problem: an overview. Eur J Oper Res 155:1–21CrossRefGoogle Scholar
  13. Fréville A, Hanafi S (2005) The multidimensional 0-1 knapsack problem-bounds and computational aspects. Ann Oper Res 139:195–227CrossRefGoogle Scholar
  14. Hanafi S, Wilbaut C (2011) Improved convergent heuristics for the 0-1 multidimensional knapsack problem. Ann Oper Res 183:125–142CrossRefGoogle Scholar
  15. Handani ZB, Hashim H, Wan Alwi SR, Manan ZA (2011) A mixed integer linear programming (MILP) model for optimal design of water network. In: Proceedings of the fourth international conference on modeling, simulation and applied optimization, ICMSAO’11, Kuala Lumpur, pp 694–699Google Scholar
  16. Hernandez EA, Uddameri V (2013) An assessment of optimal waste load allocation and assimilation characteristics in the Arroyo Colorado River watershed, TX along the US–Mexico border. Clean Technol Environ Policy 15:617–631Google Scholar
  17. Hosomi M (2014) To tighten or loosen water quality effluent standards for pollutants? Clean Technol Environ Policy 16:785CrossRefGoogle Scholar
  18. Jianren Z, Zhaojie C (2010) Optimal design of water networks in process industries using mathematical model. The 2nd international conference on computer and automation engineering (ICCAE), 2, pp 469–473Google Scholar
  19. Justanieah AM, Manousiouthakis VI (2003) IDEAS approach to the synthesis of globally optimal separation networks: application to chromium recovery from wastewater. Adv Environ Res 7(2):549–562CrossRefGoogle Scholar
  20. Karuppiah R, Grossmann I (2008) Global optimization of multiscenario mixed integer nonlinear programming models arising in the synthesis of integrated water networks under uncertainty. Comput Chem Eng 32:145–160CrossRefGoogle Scholar
  21. Ling J, Wu ML, Chen YF, Zhang YY, Dong JD (2014) Identification of spatial and temporal patterns of coastal waters in Sanya Bay, South China sea by chemometrics. J Environ Inform 23(1):37–43CrossRefGoogle Scholar
  22. Muñoz E, Capón-García E, Laínez JM, Espuña A, Puigjaner L (2013) Considering environmental assessment in an ontological framework for enterprise sustainability. J Clean Prod 47:149–164CrossRefGoogle Scholar
  23. Nethercote N, Stuckey PJ, Becket R, Brand S, Duck GJ, Tack G (2007) MiniZinc: towards a standard CP modelling language. Lect Notes Comput Sci 4741:529–543CrossRefGoogle Scholar
  24. Ponce-Ortega JM, Nápoles-Rivera F, El-Halwagi MM, Jiménez-Gutiérrez A (2012) An optimization approach for the synthesis of recycle and reuse water integration networks. Clean Technol Environ Policy 14:133–151CrossRefGoogle Scholar
  25. Putra ZA, Amminudin KA (2008) Two-step optimization approach for design of a total water system. J Ind Eng Chem Res 47:6045–6057CrossRefGoogle Scholar
  26. Shen W, Chen X, Corriou JP (2008) Application of model predictive control to the BSM1 benchmark of wastewater treatment process. Comput Chem Eng 32:2849–2856CrossRefGoogle Scholar
  27. Sotelo-Pichardo C, Ponce-Ortega JM, Nápoles-Rivera F, Serna-González M, El-Halwagi M, Frausto-Hernández S (2014) Optimal reconfiguration of water networks based on properties. Clean Technol Environ Policy 16:303–328CrossRefGoogle Scholar
  28. Tchobanoglous G, Burton FL, Stensel HD (2003) Wastewater engineering: treatment and reuse, 4th edn. McGraw-Hill, BostonGoogle Scholar
  29. Teles JP, Castro PM, Matos HA (2012) Global optimization of water networks design using multiparametric disaggregation. Comput Chem Eng 40:132–147CrossRefGoogle Scholar
  30. Verdaguer M, Clara N, Poch M (2012) Ant colony optimization-based method for managing industrial influents in wastewater systems. AIChE J 58:3070–3079CrossRefGoogle Scholar
  31. Verdaguer M, Clara N, Gutiérrez O, Poch M (2014) Application of ant-colony-optimization algorithm for improved management of first flush effects in urban wastewater systems. Sci Total Environ 485–486:143–152CrossRefGoogle Scholar
  32. Walczyk K, Jeżowski J (2008) A single stage approach for designing water networks with multiple contaminants. In: 18th European symposium on computer aided process engineering-escapeGoogle Scholar
  33. Wilson S, Manousiouthakis VI (2000) IDEAS approach to process network synthesis: application to multi-component MEN. AIChE J 46(12):2408–2416Google Scholar
  34. Xu TY, Qin XS (2013) Solving water quality management problem through combined genetic algorithm and fuzzy simulation. J Environ Inform 22(1):39–48CrossRefGoogle Scholar
  35. Yang YH, Guergachi A, Khan G (2006) Support vector machines for environmental informatics: application to modelling the nitrogen removal processes in wastewater treatment systems. J Environ Inform 7(1):14–23CrossRefGoogle Scholar
  36. Yu JQ, Chen Y, Shao S, Zhang Y, Liu SL, Zhang SS (2014) A study on establishing an optimal water network in a dyeing and finishing industrial park. Clean Technol Environ Policy 16:45–57CrossRefGoogle Scholar
  37. Zhou W, Manousiouthakis VI (2008) Global capital/total annualized cost minimization of homogeneous and isothermal reactor networks. Ind Eng Chem Res 47(10):3771–3782CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • M. Verdaguer
    • 1
  • J. Suy
    • 2
  • M. Villaret
    • 2
  • N. Clara
    • 2
  • M. Bofill
    • 2
  • M. Poch
    • 1
  1. 1.Laboratory of Chemical and Environmental Engineering (LEQUIA), Institute of the EnvironmentUniversity of GironaGironaSpain
  2. 2.Department of Computer Science, Applied Mathematics and StatisticsUniversity of GironaGironaSpain

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