Uncertainty reduction in water distribution network modelling using system inflow data

Branisavljević, Nemanja; Prodanović, Dušan; Ivetić, Marko

doi:10.1080/15730620802600916

Cited by 13 publications

(8 citation statements)

References 11 publications

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“…The design under uncertainty one of the key area of Process Systems Engineering, and consequently has been applied to different cases studies (Carrero-Parreño et al, 2019;Ruiz-Femenia et al, 2013). Some authors have considered nodal water demand uncertainty (uncorrelated) in the WDN design, and have used genetic algorithms to find the optimal solution (Branisavljević et al, 2009). Other approach is to handle uncertainty (uncorrelated) through fuzzy logic (Geranmehr et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Water Distribution Network Optimization Considering Uncertainties in the Nodes Demands

Salcedo‐Díaz

Ruiz‐Femenia

Caballero

et al. 2020

Computer Aided Chemical Engineering

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The design of Water Distribution Networks (WDN) can be formulated as an optimization problem for the minimization of the total network cost, which depends on the pipe diameters and the pumping power required. The variability in water demand at nodes can be modelled as a set of finite scenarios generated from a multivariate normal distribution assuming correlations between the selected pair nodes of the network. A disjunctive stochastic Mixed Integer Nonlinear Programming (MINLP) model is proposed for the optimal synthesis of WDN considering correlated uncertainties in nodal demands. Strategies for avoiding nonconvex nonlinearities in the equations are applied to avoid unnecessary complexities. We analyse the effect of different correlation matrices to gather insight into how the model faces uncertainty. A case study was used to test the model and the optimization techniques proposed. Results show that under uncertainty the stochastic solution of the WDN improves the deterministic one (i.e. the design obtained for nominal values of nodes demand), evincing that neglecting uncertainty in the optimization process may lead to suboptimal or, even worse, infeasible design of WDNs.

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Section: Introductionmentioning

confidence: 99%

Water Distribution Network Optimization Considering Uncertainties in the Nodes Demands

Salcedo‐Díaz

Ruiz‐Femenia

Caballero

et al. 2020

Computer Aided Chemical Engineering

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“…The same as in [85], fuzzy state estimation for water distribution systems was developed in [86] [87].…”

Section: Uncertainty Analysis Of Water Network Measurement Datamentioning

confidence: 99%

Mixed simulation-state estimation of water distribution systems based on a least squares loop flows state estimator

Arsene

Gabrys

2014

Applied Mathematical Modelling

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This paper presents a novel algorithm for uncertainty quantification of water distribution system measurement data including nodal demands/consumptions as well as real pressure and flow measurements. This procedure, referred to as a Confidence Limit Analysis (CLA), is concerned with a deployment of a Least Squares (LS) state estimator based on the loop corrective flows and the variation of nodal demands as independent variables. The confidence limits obtained for the nodal pressures and the inflows/outflows of a water network are determined with the novel algorithm called Error Maximization (EM) method and are evaluated with respect to two other more established CLA algorithms based on an Experimental Sensitivity Matrix (ESM) and on the sensitivity matrix method obtained with the LS nodal heads equations state estimator. The estimated confidence limits obtained for two real water networks show that the proposed EM algorithm is comparable to the other two CLA benchmark algorithms but due to its computational efficiency it is more suitable for online decision support applications in water distribution systems. Both ESM and EM methods work for any operating point whether arbitrarily or randomly chosen for any water network although EM method has the advantage of being computationally superior and working with any sets of measurements.

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“…This correspondence may be better represented by a fuzzy membership function [ Hall et al , 2007]. For this reason, fuzzy sets have been applied to represent the imprecision in future water demands [e.g., Xu and Goulter , 1999b; Bhave and Gupta , 2004; Branisavljević et al , 2009].…”

Section: Fuzzy Random Variables and Their Application To Future Watermentioning

confidence: 99%

“… Vamvakeridou‐Lyroudia et al [2005] combined a fuzzy multicriteria decision‐making method into a genetic algorithm optimization process to evaluate the benefits of individual design solutions. Branisavljević et al [2009] attempted to reduce model output uncertainty through constraining nodal demands with fuzzy sets in WDS model calibration.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy probabilistic design of water distribution networks

Kapelan

2011

Water Resources Research

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The primary aim of this paper is to present a fuzzy probabilistic approach for optimal design and rehabilitation of water distribution systems, combining aleatoric and epistemic uncertainties in a unified framework. The randomness and imprecision in future water consumption are characterized using fuzzy random variables whose realizations are not real but fuzzy numbers, and the nodal head requirements are represented by fuzzy sets, reflecting the imprecision in customers' requirements. The optimal design problem is formulated as a two‐objective optimization problem, with minimization of total design cost and maximization of system performance as objectives. The system performance is measured by the fuzzy random reliability, defined as the probability that the fuzzy head requirements are satisfied across all network nodes. The satisfactory degree is represented by necessity measure or belief measure in the sense of the Dempster‐Shafer theory of evidence. An efficient algorithm is proposed, within a Monte Carlo procedure, to calculate the fuzzy random system reliability and is effectively combined with the nondominated sorting genetic algorithm II (NSGAII) to derive the Pareto optimal design solutions. The newly proposed methodology is demonstrated with two case studies: the New York tunnels network and Hanoi network. The results from both cases indicate that the new methodology can effectively accommodate and handle various aleatoric and epistemic uncertainty sources arising from the design process and can provide optimal design solutions that are not only cost‐effective but also have higher reliability to cope with severe future uncertainties.

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Uncertainty reduction in water distribution network modelling using system inflow data

Cited by 13 publications

References 11 publications

Water Distribution Network Optimization Considering Uncertainties in the Nodes Demands

Water Distribution Network Optimization Considering Uncertainties in the Nodes Demands

Mixed simulation-state estimation of water distribution systems based on a least squares loop flows state estimator

Fuzzy probabilistic design of water distribution networks

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