Sewer System Design Using Simulated Annealing in Excel

Karovic, Omer; Mays, Larry W.

doi:10.1007/s11269-014-0750-8

Cited by 42 publications

(18 citation statements)

References 25 publications

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“…Since the concept of optimal design for storm sewer networks was first proposed in the late 1960s [5,6], many researchers have concentrated on the optimal design of storm sewer systems, and various optimization techniques were applied to minimize construction costs whilst ensuring the reliability of sewer networks for their solution, such as linear programming [5,7,8], nonlinear programming [6,9], dynamic programming [10][11][12], genetic algorithm (GA) [13][14][15][16][17], cellular automata (CA) [18][19][20][21], ant colony optimization algorithm (ACOA) [22][23][24][25], rebirthing particle swarm optimization (PSO) algorithm [26], and more recently, simulated annealing (SA) [27,28]. The main goal in the optimal design of a storm sewer system is to find the combination of pipe diameters and pipe slopes which leads to the sewer system with the least cost, with the total cost of the sewer network as the objective function subject to proper constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Novel SWMM Based Algorithm Application to Storm Sewer Network Design

Shao

Zhang

et al. 2017

Water

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An automated algorithm based on the dynamic hydrological and hydraulic simulation modules in Storm Water Management Model (SWMM) was developed to aid the design of storm sewer networks, provided that a layout is given. Numerical performance of the proposed algorithm was compared with the existing design methods with two application cases. The automated computation process of the sewer network design was divided into two stages and solved iteratively, determining pipe diameter and pipe slope, respectively. In the first stage, starting with a set of initial values including pipe diameter, pipe cover depth, and ground elevation at manholes, the iteration was carried out from the downstream to the upstream while the pipe slopes of the network were assumed to be fixed and the diameter of each pipe segment was calculated. In the second stage, pipe diameters calculated from the first stage were fixed and the pipe slopes were calculated successively from the downstream pipe segment to the upstream pipe segment. Every time the diameter or slope of a pipe segment was adjusted, the pipe flow rate, velocity, and flow depth were obtained by running SWMM hydrological and hydraulic simulation modules. The iteration terminated once the combination scheme of pipe diameters and slopes met the design ordinance which requires the pipe flows full under gravity in a design return period. A real urban sewer system in a hilly city and a benchmark sewer network from the literature were tested to validate the proposed automated algorithm, and good performance was shown. The automated design results explicitly show that the proposed storm sewer design approach leads to a quality solution with reduced computational effort.

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Section: Literature Reviewmentioning

confidence: 99%

“…This is because the complexity of the optimization task significantly increases when the network layout design is incorporated into the optimization procedure [29]. A variety of optimization methods adopted for optimal storm sewer design were discussed in more detail in Karovic et al [27] and Afshar and Rohani [21].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel SWMM Based Algorithm Application to Storm Sewer Network Design

Shao

Zhang

et al. 2017

Water

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“…Also, compared to the original design, all the sewers designed by SA were able to satisfy the minimum velocity constraint though the resulted construction cost was a little higher. Moreover, Karovic and Mays [130] recently applied SA in Microsoft Excel for the sewer system design to make it more convenient from the engineers' perspective. In [131], the authors proposed an integrated approach to combine the determination of network layout and network components since the two problems are naturally related to each other.…”

Section: ) Nonpopulation-based Metaheuristicsmentioning

confidence: 99%

Optimization of Potable Water Distribution and Wastewater Collection Networks: A Systematic Review and Future Research Directions

Zhao

Beach

Rezgui

2016

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-Potable water distribution networks (WDNs) and wastewater collection networks (WWCNs) are the two fundamental constituents of the complex urban water infrastructure. Such water networks require adapted design interventions as part of retrofitting, extension and maintenance activities. Consequently, proper optimization methodologies are required to reduce the associated capital cost while also meeting the demands of acquiring clean water and releasing wastewater by consumers. In this paper, a systematic review of the optimization of both WDNs and WWCNs, from the preliminary stages of development through to the state-of-the-art, is jointly presented. Firstly, both WDNs and WWCNs are conceptually and functionally described along with illustrative benchmarks. The optimization of water networks across both clean and waste domains is then systematically reviewed and organized, covering all levels of complexity from the formulation of cost functions and constraints, through to traditional and advanced optimization methodologies. The rationales behind employing these methodologies as well as their advantages and disadvantages are investigated. The paper then critically discusses current trends and identifies directions for future research by comparing the existing optimization paradigms within WDNs and WWCNs and proposing common research directions for optimizing water networks. Optimization of urban water networks is a multidisciplinary domain, within which this paper is anticipated to be of great benefit to researchers and practitioners.

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“…Haestad (2004) and Guo, Walters, and Savic (2008) reviewed a significant amount of research works in the field of sewer network design developed in the last 40 years. The methods such as enumeration approaches (Charalambous & Elimam, 1990;Desher & Davis, 1986;Miles & Heaney, 1988), linear programming (Dajani & Hasit, 1974;Elimam, Charalambous, & Ghobrial, 1989), nonlinear programming (Price, 1978;Swamee, 2001), dynamic programming (Botrous, El-Hattab, & Dahab, 2000;Diogo, Walters, Sousa, & Graveto, 2000;Gupta, Mehndiratta, & Khanna, 1983;Kulkarni & Khanna, 1985;Merrit & Bogan, 1973;Templeman & Walters, 1979;Walsh & Brown, 1973;Yen, Cheng, Jun, Voohees, & Wenzel, 1984) and evolutionary algorithms (EA) such as genetic algorithm (Afshar, Afshar, Marino, & Darbandi, 2006;Brand & Ostfeld, 2011;Haghighi & Bakhshipour, 2012;Heaney, Wright, Sample, Field, & Fan, 1999;Liang, Thompson, & Young, 2004), ant colony optimization algorithm (Afshar, 2007(Afshar, , 2010Moeini, 2017), particle swarm optimization (Izquierdo, Montalvo, Perez, & Fuertes, 2008;Navin & Mathur, 2016), simulated annealing (Karovic & Mays, 2014), and cellular automata (Afshar, Shahidi, Rohania, & Sargolzaei, 2011;Afshar, Zaheri, & Kim, 2016;Guo, 2005; have been proposed to solve sewer network design optimization problems. Mays and Tung (1992) pointed out some important limitations of the conventional optimization methods such as lin...…”

Section: Introductionmentioning

confidence: 99%

Hybridizing ant colony optimization algorithm with nonlinear programming method for effective optimal design of sewer networks

Moeini

Afshar

2019

Water Environment Research

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The ant colony optimization algorithm (ACOA) is hybridized with nonlinear programming (NLP) for the optimal design of sewer networks. The resulting problem is a highly constrained mixed integer nonlinear problem (MINLP) presenting a challenge even to the modern heuristic search methods. In the proposed hybrid method, The ACOA is used to determine pipe diameters while the NLP is used to determine the pipe slopes of the network by proposing two different formulations. In the first formulation, named ACOA‐NLP1, a penalty method is used to satisfy the problem constraints while in the second one, named ACOA‐NLP2, the velocity and flow depth constraints are expressed in terms of the slope constraints which are easily satisfied as box constraint of the NLP solver leading to a considerable reduction of the search space size. In addition, the assumption of minimum cover depth at the network inlets is used to calculate the nodal cover depths and the pump and drop heights at the network nodes, if required, leading to a complete solution. The total cost of the constructed solution is used as the objective function of the ACOA, guiding the ant toward minimum cost solutions. Proposed hybrid methods are used to solve three test examples, and the results are presented and compared with those produced by a conventional application of ACOA. The results indicate the effectiveness and efficiency of the proposed formulations and in particular the ACOA‐NLP2 to optimally solve the sewer network design optimization problems. Practitioner points ACOA is hybridized with NLP for the effective optimal design of sewer networks. Here, ACOA is used to determine pipe diameters and NLP is used to determine the network pipe slopes with predefined pipe diameters. In ACOA‐NLP1, a penalty method is used to enforce the problem constraints. In ACOA‐NLP2, velocity and flow depth constrains are expressed in terms of slope constraint.

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Sewer System Design Using Simulated Annealing in Excel

Cited by 42 publications

References 25 publications

A Novel SWMM Based Algorithm Application to Storm Sewer Network Design

A Novel SWMM Based Algorithm Application to Storm Sewer Network Design

Optimization of Potable Water Distribution and Wastewater Collection Networks: A Systematic Review and Future Research Directions

Hybridizing ant colony optimization algorithm with nonlinear programming method for effective optimal design of sewer networks

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