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Sewer System Design Using Simulated Annealing in Excel

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Abstract

An optimization procedure has been developed for branching storm and sanitary sewer systems with a pre-determined layout for determining the minimum total cost. The model was developed within Microsoft Excel using simulated annealing as the optimization procedure. The total cost of the storm sewer system that was obtained with this optimal design procedure was compared to the total cost of the system as obtained from the conventional straight slope design procedure. Applying the simulated annealing optimizer to the design of the branching storm sewer network resulted in a cost savings of over $77,100 or about 7 % (a reduction from $1,117,700 to $1,040,600). These significant savings were realized by simply going an extra step and implementing an optimization technique during the design phase. Use of Excel should enhance the availability and the usage of such an optimization model for the design of storm and sanitary sewer systems by consulting engineers and various agencies.

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References

  • Argaman Y, Shamir U, Spivak E (1973) Design of optimal sewerage systems. J Environ Eng ASCE 99(5):703–716

    Google Scholar 

  • Afshar MH (2006) Application of a genetic algorithm to storm sewer network optimization. Sci Iran 13(3):234–244

    Google Scholar 

  • Afshar MH (2010) A parameter free continuous Ant colony optimization algorithm for the optimal design of storm sewer networks. Adv Eng Sof 41:188–195

    Article  Google Scholar 

  • Afshar MH, Afshar A, Mariño MA, Darbandi AAS (2006) Hydrograph-based storm sewer design optimization by genetic algorithm. Can J Civ Eng 33(3):319–325

    Article  Google Scholar 

  • Barlow JF (1972) Cost optimization of pipe sewerage systems. Proc Inst Civ Eng 53:57–64

    Article  Google Scholar 

  • Brown KG, Koussis AD (1987) LOTUS spreadsheet design for storm drain networks. J Comput Civ Eng-ASCE 1(3):197–213

    Article  Google Scholar 

  • Butler D, Davies J (2000) Urban drainage. E & FN Spon, London, UK

    Book  Google Scholar 

  • Cembrowicz, RG. and Krauter GE (1987). Design of cost optimal sewer networks. In: Gujer, W. et al. (eds.), Proc. 4th Int. Conf. on Urban Storm Drainage, Lausanne, Switzerland, pp. 367–372

  • Chau KW (1992) Robust computer-aided design package for municipal stormwater drainage networks. Adv Eng Softw 15(1):43–53

    Article  Google Scholar 

  • Dajani JS, Hasit Y (1974) Capital cost minimization of drainage networks. J Environ Eng-ASCE 100(2):325–337

    Google Scholar 

  • Dougherty DE, Marryott RA (1991) Optimal groundwater management - 1. Simul Ann Water Resour Res 27(10):2493–2508

    Article  Google Scholar 

  • Deininger RA (1966). Computer aided design of waste collection and treatment systems. In: Proc. 2nd Annual Conf. of American Water Resources, Chicago, USA, pp. 247–258.

  • Deininger RA (1970) Systems analysis for water supply and pollution control, natural resource systems models in decision making. Toebes, Water Resources Center, Purdue University, Edited by G.H

    Google Scholar 

  • Diogo AF, Graveto VM (2006) Optimal layout of sewer systems: a deterministic versus a stochastic model. J Hydraul Eng-ASCE 132(9):927–943

    Article  Google Scholar 

  • Farmani R, Savic DA, Walters GA (2006) A hybrid technique for optimization of branched urban water systems. In Proc 7th Int Conf of Hydroinformatics Nice France 1:985–992

    Google Scholar 

  • Gidley JS (1986) Optimal design of sanitary sewers. In Proc. 4th ASCE Conf. on Computing in Civil Engineering, Boston, USA, pp. 162–177

  • Guo Y, Walters GA, Khu ST, Keedwell EC (2007) A novel cellular automata based approach to storm sewer design. Eng Optimiz 39(3):345–364

    Article  Google Scholar 

  • Guo Y, Walters GA, Savic D (2008) Optimal design of storm sewer networks: past, present and future, proceedings of the 11th international conference on urban drainage. Edinburgh, Scotland, UK

    Google Scholar 

  • Gupta A, Mehndiratta SL, Khanna P (1983) Gravity wastewater collection systems optimisation. J Environ Eng ASCE 109(5):1195–1209

    Article  Google Scholar 

  • Haghighi A (2013) Loop-by-loop cutting algorithm to generate layouts for urban drainage systems. J Water Resour Plan Manag ASCE 139(6):693–703

    Article  Google Scholar 

  • Haghighi A, Bakhshipour AE (2012) Optimization of sewer networks using an adaptive genetic algorithm. Water Resour Manag 26:3441–3456

    Article  Google Scholar 

  • Hatchett J, Dietrich K. and Walski T (2002). Advantages and limitations of automated storm sewer design. In: Strecker E.W., Huber W.C. (eds.) Proc. 9th Int. Conf. on Urban Drainage, Portland, Oregon, USA

  • Heaney JP, Pitt R, Field R (2000) Innovative urban wet-weather flow management systems. EPA/600/R-99/029, U.S. Environmental Protection Agency, Cincinnati, USA

  • Holland ME (1966) Computer models of wastewater collection systems. Harvard University, Cambridge, Massachusetts, USA, PhD dissertation

    Google Scholar 

  • Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  Google Scholar 

  • Liang LY, Thompson RG, Young DM (2004) Optimising the design of sewer networks using Genetic Algorithms and Tabu search. J Eng Constr Archit Manag 11(2):101–112

    Article  Google Scholar 

  • Liebman JC (1967). A heuristic aid for the design of sewer networks, Journal of the Sanitary Engineering Division, ASCE, Vol. 93, No SA4, pp. 81–90.

  • Li GY, Matthew RGS (1990) New approach for optimisation of urban drainage systems. J Environ Eng ASCE 116(5):927–944

    Article  Google Scholar 

  • Lowsley Jr. IH (1973) An implicit enumeration algorithm for optimal sewer layout, Ph. D. Thesis, Johns Hopkins University, Baltimore, MD.

  • Mays LW (1976). Optimal layout and design of storm sewer systems, Ph.D. Thesis, University of Illinois at Urbana-Champaign, Illinois.

  • Mays LW (ed) (2001) Stormwater Collection Systems Design Handbook. McGraw-Hill Companies, Inc., New York

    Google Scholar 

  • Mays LW, Wenzel HG (1976) Optimal design of multi-level branching sewer systems. Water Resour Res 12(5):913–917

    Article  Google Scholar 

  • Mays LW, Yen BC (1975) Optimal cost design of branched sewer systems. Water Resour Res 11(1):37–47

    Article  Google Scholar 

  • Mays LW, Tang WH, and Yen BC (1975). Optimal Risk-Based Design of Storm Sewer Networks, Journal of Environmental Engineering Division, ASCE, Vol. 101, No. EE3, pp. 381–398.

  • Mays LW, Wenzel HG, and Liebman JC (1976). Model for layout and design of sewer systems, Journal of the Water Resources Planning and Management Division, ASCE, Vol. 102, No. WR2, pp. 385–405, November 1976

  • Meredith DD (1971). Dynamic programming with case study on planning and design of urban water facilities, SEC. IX, Treatise on Urban Water Systems, Colorado State University, pp. 590–652.

  • Merritt LB and Bogan RH (1973). Computer-based optimal design of sewer systems, Journal of Environmental Engineering Division, ASCE, Vol. 99, No. EE1, pp. 35–53.

  • Miles SW, Heaney JP (1988) Better than ‘optimal’ method for designing drainage systems. J Water Resour Plan Manag ASCE 114(5):477–499

    Article  Google Scholar 

  • Moeini R, Afshar MH (2012) Layout and size optimization of sanitary sewer network using intelligent ants. Adv Eng Softw 51:49–62

    Article  Google Scholar 

  • Nzewi EU, Gray DD, Houck MH (1985) Optimal design program for gravity sanitary sewers. Civ Eng Syst 2:132–141

    Article  Google Scholar 

  • Pan TC, Kao JJ (2009) GA-QP model to optimize sewer system design. J Environ Eng 135(1):17–24

    Article  Google Scholar 

  • Price RK (1978). Design of storm water sewers for minimum construction cost. In Proc. 1st Int. Conf. on Urban Strom Drainage, Southampton, UK, pp. 636–647

  • Walters GA, Lohbeck T (1993) Optimal layout of tree networks using genetic algorithms. Eng Optim 22(1):27–48

    Article  Google Scholar 

  • Walters GA, Smith DK (1995) Evolutionary design algorithm for optimal layout of tree networks. Eng Optim 24(4):261–281

    Article  Google Scholar 

  • Walters GA, Templeman AB (1979) Non-optimal dynamic programming algorithms in the design of minimum cost drainage systems. Eng Optim 4:139–148

    Article  Google Scholar 

  • Yen BC (2001) Hydraulics of sewer systems. In: Mays LW (ed) Stormwater Collection Systems Design Handbook. McGraw-Hill Companies, New York

    Google Scholar 

  • Yen BC, Wenzel HG, Mays LW, Tang WH (1976) 112, Water Resources Center. Illinois, University of Illinois, Urbana, Advanced Methodologies for Design of Storm Sewer Systems, Research Report No

    Google Scholar 

  • Yen BC, Cheng T-T, Jun B-H, Voorhees ML, Wenzel HG, Mays LW (1984) S-70, Water Resources Center. Illinois, University of Illinois, Illinois Least-Cost Sewer System Design Model: ILSD-1 and 2 User’s Guide, Research Report Project No

    Google Scholar 

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Authors and Affiliations

  1. School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZP, Arizona

    Omer Karovic & Larry W. Mays

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  1. Omer Karovic
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  2. Larry W. Mays
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Correspondence to Larry W. Mays.

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Karovic, O., Mays, L.W. Sewer System Design Using Simulated Annealing in Excel. Water Resour Manage 28, 4551–4565 (2014). https://doi.org/10.1007/s11269-014-0750-8

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