Speeding Up the Stochastic Analysis of Water Distribution Systems Using a Compression Heuristic

Chang, Ching-Chiao; Zyl, J. E. van

doi:10.1061/41203(425)128

Cited by 1 publication

(18 citation statements)

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“…Chapter 3 provides a basic overview of the methodology used in the study. This includes a brief description and motivation for the compression heuristic method (Chang & van Zyl, 2010), and an investigation of Latin Hypercube sampling. Chapter 4 and Chapter 5 deal with the two components of the compression heuristic, which are the pre-run and events run respectively.…”

Section: Dissertation Layoutmentioning

confidence: 99%

“…However, with current advances in technology, this is becoming less of a problem and stochastic simulation is steadily gaining feasibility as a modelling technique. Nevertheless, an attempt has been made to reduce the computational effort required by the stochastic analysis model developed by van Zyl et al (2008) through the use of a compression heuristic (Chang & van Zyl, 2010).…”

Section: Background and Motivation For Researchmentioning

confidence: 99%

“…One way of speeding up the stochastic analysis of storage reservOlrs is by using a compression heuristic proposed by Chang & van Zyl (2010). In the stochastic model (van Zyl et al, 2008), the entire duration of the study period is simulated in full using the Monte Carlo method.…”

Section: Compression Heuristicmentioning

confidence: 99%

“…The failure characteristics are used to assess the reliability, which can then be used as a sizing criterion for the design of the storage reservoir. This model was then used to explore the effect of varying the user demand parameters on storage reservoirs (van Zyl et al, 2010). VI ok (2010) applied this stochastic model to a bulk water supply system to investigate the financial implications associated with the use of risk-based analysis techniques.…”

Section: 1 Monte Carlo Simulation For Reliability Analysismentioning

confidence: 99%

“…• To reduce the computational effort of the stochastic model developed by van Zyl et al (2008) for the reliability analysis of municipal storage reservoirs, through additional work on the compression heuristic (Chang & van Zyl, 2010) to refine the model so that it ensures that accurate and reliable results are obtained quickly. Applying the compression heuristic will allow the practical use of the stochastic analysis model within the optimisation run.…”

Section: Research Goal and Objectivesmentioning

confidence: 99%

See 4 more Smart Citations

Optimal Reliability-Based Design of Bulk Water Supply Systems

Chang

Zyl

2014

J. Water Resour. Plann. Manage.

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DJ :;sc rt(l 1I UIl s uhnlltt ed In p a rt i~l fulfi lm en t o~' th e reqlllrcrnen ts fu r tht., degree ofM~s t e r ofSClence In Enginee ring AbstractBulk water supply systems are usually designed according to deterministic design guidelines. In South Africa, design guidelines specify that a bulk storage reservoir should have a storage capacity of 48 hours of annual average daily demand (AADD), and the feeder pipe a capacity of 1.5 times AADD (CSIR, 2000). Nel & Haarhoff (1996) proposed a stochastic analysis method that allowed the reliability of a reservoir to be estimated based on a Monte Carlo analysis of consumer demand, fire water demand and pipe failures. Van Zyl et al. (2008) developed this method further and proposed a design criterion of one failure in ten years under seasonal peak conditions.In this study, a method for the optimal design of bulk water supply systems is proposed with the design variables being the configuration of the feeder pipe system, the feeder pipe diameters (i.e. capacity), and the size of the bulk storage reservoir. The stochastic analysis method is applied to determine a trade-off curve between system cost and reliability, from which the designer can select a suitable solution.Optimisation of the bulk system was performed using the multi-objective genetic algorithm, NSGA-II. As Monte Carlo sampling can be computationally expensive, especially when large numbers of simulations are required in an optimisation exercise, a compression heuristic was implemented and refined to reduce the computational effort required of the stochastic simulation. Use of the compression heuristic instead of full Monte Carlo simulation in the reliability analysis achieved computational time savings of around 75% for the optimisation of a typical system.Application of the optimisation model showed that it was able to successfully produce a set of Pareto-optimal solutions ranging from low reliability, low cost solutions to high reliability, high cost solutions. The proposed method was first applied to a typical system, resulting in an optimal reservoir size of approximately 22 h AADD and feeder pipe capacity of 2 times AADD. This solution achieved 9% savings in total system cost compared to the South African design guidelines. In addition, the optimal solution proved to have better reliability that one designed according to South African guidelines.A sensitivity analysis demonstrated the effects of changing various system and stochastic parameters from typical to low and high values. The sensitivity results revealed that the length of the feeder pipe system has the greatest impact on both the cost and reliability of the bulk system. It was also found that a single feeder pipe is optimal in most cases, and that parallel feeder pipes are only optimal for short feeder pipe lengths.The optimisation model is capable of narrowing down the search region to a handful of possible design solutions, and can thus be used by the engineer as a tool to assist with the design of the final system.

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Section: Dissertation Layoutmentioning

confidence: 99%

Section: Background and Motivation For Researchmentioning

confidence: 99%