Uncertainty analysis of water distribution networks using type-2 fuzzy sets and parallel genetic algorithm

Geranmehr, Mohammadali; Asghari, Keyvan; Chamani, Mohammad R.

doi:10.1080/1573062x.2019.1648527

Cited by 12 publications

(12 citation statements)

References 31 publications

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“…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). Furthermore, the variability of the water demand (and head) has been represented using log-normal probability distribution uncorrelated functions (Marquez Calvo et al, 2018), but to best our knowledge water demand uncertainty implemented by a set of correlated scenarios with desired marginal distributions for each uncertain 2 R. Salcedo-Diaz et al parameter has not been considered in WDN optimization (for an exhaustive list of works in WDN optimization see Mala-Jetmarova et al, 2018).…”

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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“…. c), while its degree of membership of the particular cluster m ik , is greater than its membership values of all other clusters (Polomcǐćet al 2017;Arkajyoti & Swagatam 2018;Geranmehr et al 2019).…”

Section: Fuzzy C-mean Algorithmmentioning

confidence: 95%

“…In addition, when m ¼ 1, the partition is hard, and for m . 1, the partition is fuzzy and increasing m causes the partition to become fuzzie (Aree et al 2001;Liou et al 2003;Dzung et al 2015;Janmenjoy et al 2017;Polomcǐćet al 2017;Geranmehr et al 2019;Tolentino and Gerardo 2019;Xing & Li 2019).…”

Section: Determination Of Optimal Fuzziness Index Mmentioning

confidence: 99%

“…Therefore distribution uniformity of DO concentration in a cylindrical aeration tank involves the above factors and the function relationships between them are hard to be deduced directly by conventional calculation methods. Owing to its ability to show the complex interaction between interrelated factors, fuzzy theory is more appropriate for developing functions considering both overall efficiency of oxygen mass transfer and distribution uniformity of DO concentration than other mathematical methods (Arkajyoti & Swagatam 2018;Geranmehr et al 2019;Li et al 2019). In addition fuzzy theory has such following advantages.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, its calculation mode is simple and efficient,especially it is capable of dealing with compound multi-variable interactions. Considering the complicated interplay of the factors such as DH, DV, DA and RTT, fuzzy c-means (FCM) algorithm is fitter for this study than k-means algorithm ( Janmenjoy et al 2017;Arkajyoti & Swagatam 2018;Geranmehr et al 2019;Tolentino & Gerardo 2019;Xing & Li 2019).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating the performance of fine bubble diffused aeration systems in cylindrical aeration tanks by fuzzy c-means algorithm

Zeng

2021

Water Science and Technology

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In order to make a comprehensive evaluation of the performance of fine bubble diffused aeration systems in cylindrical aeration tanks, the following parameters are considered: distribution of DO concentration in the horizontal direction of the different water depth of an aeration tank, distribution of DO concentration in the vertical direction of the aeration tank, distribution of DO concentration in all the points of the aeration tank and ratio of total mass increment of DO in the aeration tank to total mass of oxygen in aeration air. The aeration characteristic criterion (ACC) is proposed to synthesize these parameters, and weighted sums of the similarity degrees derived from the extensions of fuzzy c-means (FCM) are used to construct ACC. The results show that compared with total volumetric oxygen transfer coefficient () and specific standard oxygen transfer efficiency (), ACC is demonstrated to be more remarkable and sensitive for the performance evaluation of the fine bubble diffused aeration systems equipped with fine-pore aeration tubes. Moreover the performance of aeration systems equipped with different layouts of fine-pore aeration tubes is comparatively studied, and their performance from good to bad is ring-type diffuser > square-type diffuser > parallel-lines-type diffuser >cross-type diffuser.

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Water Distribution Networks Optimization Considering Uncertainties in the Demand Nodes

Cassiolato,

Ruiz-Femenia,

Salcedo-Diaz

et al. 2024

Water Resour Manage

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The fluctuation in the consumption of treated water is a situation that distribution networks gradually face. In times of greater demand, this consumption tends to suffer unnecessary impacts due to the lack of water. The uncertainty that occurs in water consumption can be mathematically modeled by a finite set of scenarios generated by a normal distribution and attributed to the network design. This study presents an optimization model to minimize network installation and operation costs under uncertainties in water demands. A Mixed Integer Nonlinear Programming model is proposed, considering the water flow directions in the pipes as unknown. A deterministic approach is used to solve the problem in three steps: First, the problem is solved with a nominal value for each uncertain parameter. In the second stage, the problem is solved for all scenarios, with the independent variables of the scenario being fixed and obtained from the solution reached in the first stage, known as the deterministic solution. Finally, all scenarios are solved without fixing any variable values, in a stochastic approach. Two case studies were used to test the applicability of the model and global optimization techniques were used to solve the problem. The results show that the stochastic solution can lead to optimal solutions for robust and flexible water distribution networks, capable of working under different conditions, considering the uncertainties of node demand and variable pipe directions.

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Uncertainty analysis of water distribution networks using type-2 fuzzy sets and parallel genetic algorithm

Cited by 12 publications

References 31 publications

Water Distribution Network Optimization Considering Uncertainties in the Nodes Demands

Water Distribution Network Optimization Considering Uncertainties in the Nodes Demands

Evaluating the performance of fine bubble diffused aeration systems in cylindrical aeration tanks by fuzzy c-means algorithm

Water Distribution Networks Optimization Considering Uncertainties in the Demand Nodes

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