Multiobjective optimization of water distribution systems accounting for economic cost, hydraulic reliability, and greenhouse gas emissions

In this paper, three objectives are considered for the optimization of water distribution systems (WDSs): the traditional objectives of minimizing economic cost and maximizing hydraulic reliability and the recently proposed objective of minimizing greenhouse gas (GHG) emissions. It is particularly important to include the GHG minimization objective for WDSs involving pumping into storages or water transmission systems (WTSs), as these systems are the main contributors of GHG emissions in the water industry. In order to better understand the nature of tradeoffs among these three objectives, the shape of the solution space and the location of the Pareto-optimal front in the solution space are investigated for WTSs and WDSs that include pumping into storages, and the implications of the interaction between the three objectives are explored from a practical design perspective. Through three case studies, it is found that the solution space is a U-shaped curve rather than a surface, as the tradeoffs among the three objectives are dominated by the hydraulic reliability objective. The Pareto-optimal front of real-world systems is often located at the "elbow" section and lower "arm" of the solution space (i.e., the U-shaped curve), indicating that it is more economic to increase the hydraulic reliability of these systems by increasing pipe capacity (i.e., pipe diameter) compared to increasing pumping power. Solutions having the same GHG emission level but different cost-reliability tradeoffs often exist. Therefore, the final decision needs to be made in conjunction with expert knowledge and the specific budget and reliability requirements of the system. © 2013. American Geophysical Union. All Rights Reserved.Wenyan Wu, Holger R. Maier, and Angus R. Simpso

Influenza epidemiology, vaccine coverage and vaccine effectiveness in children admitted to sentinel Australian hospitals in 2014: The influenza complications alert network (FluCAN)

The Influenza Complications Alert Network (FluCAN) is a sentinel hospital-based surveillance programme operating in all states and territories in Australia. We summarise the epidemiology of children hospitalised with laboratory-confirmed influenza in 2014 and reports on the effectiveness of inactivated trivalent inactivated vaccine (TIV) in children. In this observational study, cases were defined as children admitted with acute respiratory illness (ARI) with influenza confirmed by PCR. Controls were hospitalised children with ARI testing negative for influenza. Vaccine effectiveness (VE) was estimated as 1 minus the odds ratio of vaccination in influenza positive cases compared with test-negative controls using conditional logistic regression models. From April until October 2014, 402 children were admitted with PCR-confirmed influenza. Of these, 28% were aged < 1 year, 16% were Indigenous, and 39% had underlying conditions predisposing to severe influenza. Influenza A was detected in 90% of cases of influenza; influenza A(H1N1)pdm09 was the most frequent subtype (109/141 of subtyped cases) followed by A(H3N2) (32/141). Only 15% of children with influenza received antiviral therapy. The adjusted VE of one or more doses of TIV for preventing hospitalised influenza was estimated at 55.5% (95% confidence intervals (CI): 11.6–77.6%). Effectiveness against influenza A(H1N1)pdm09 was high (91.6%, 95% CI: 36.0–98.9%) yet appeared poor against H3N2. In summary, the 2014 southern hemisphere TIV was moderately effective against severe influenza in children. Significant VE was observed against influenza A(H1N1)pdm0

Shape-optimization of 2D hydrofoils using an isogeometric BEM solver

In this paper, an optimization procedure, based on an Isogeometric BEM solver for the potential flow, is developed and used for the shape optimization of hydrofoils. The formulation of the exterior potential-flow problem reduces to a Boundary-Integral Equation (BIE) for the associated velocity potential exploiting the null-pressure jump Kutta condition at the trailing edge. The numerical solution of the BIE is performed by an Isogeometric Boundary-Element Method (BEM) combining a generic B-splines parametric modeler for generating hydrofoil shapes, using a set of eight parameters, the very same basis of the geometric representation for representing the velocity potential and collocation at the Greville abscissas of the knot vector of the hydrofoil's B-splines representation. Furthermore, the optimization environment is developed based on the geometric parametric modeler for the hydrofoil, the Isogeometric BEM solver and an optimizer employing a controlled elitist genetic algorithm. Multi-objective hydrofoil shape optimization examples are demonstrated with respect to the criteria (i) maximum lift coefficient and (ii) minimum deviation of the hydrofoil area from a reference area

Improved understanding of the searching behavior of ant colony optimization algorithms applied to the water distribution design problem

[1] Evolutionary algorithms (EAs) have been applied successfully to many water resource problems, such as system design, management decision formulation, and model calibration. The performance of an EA with respect to a particular problem type is dependent on how effectively its internal operators balance the exploitation/exploration trade-off to iteratively find solutions of an increasing quality. For a given problem, different algorithms are observed to produce a variety of different final performances, but there have been surprisingly few investigations into characterizing how the different internal mechanisms alter the algorithm’s searching behavior, in both the objective and decision space, to arrive at this final performance. This paper presents metrics for analyzing the searching behavior of ant colony optimization algorithms, a particular type of EA, for the optimal water distribution system design problem, which is a classical NP-hard problem in civil engineering. Using the proposed metrics, behavior is characterized in terms of three different attributes: (1) the effectiveness of the search in improving its solution quality and entering into optimal or near-optimal regions of the search space, (2) the extent to which the algorithm explores as it converges to solutions, and (3) the searching behavior with respect to the feasible and infeasible regions. A range of case studies is considered, where a number of ant colony optimization variants are applied to a selection of water distribution system optimization problems. The results demonstrate the utility of the proposed metrics to give greater insight into how the internal operators affect each algorithm’s searching behavior.A.C. Zecchin, A.R. Simpson, H.R. Maier, A. Marchi and J.B. Nixo

GALAXY: A new hybrid MOEA for the Optimal Design of Water Distribution Systems

This is the final version of the article. Available from American Geophysical Union via the DOI in this record.The first author would like to appreciate the financial support given by both the University of Exeter and the China Scholarship Council (CSC) toward the PhD research. We also appreciate the three anonymous reviewers, who help improve the quality of this paper substantially. The source code of the latest versions of NSGA-II and ε-MOEA can be downloaded from the official website of Kanpur Genetic Algorithms Laboratory via http://www.iitk.ac.in/kangal/codes.shtml. The description of each benchmark problem used in this paper, including the input file of EPANET and the associated best-known Pareto front, can be accessed from the following link to the Centre for Water Systems (http://tinyurl.com/cwsbenchmarks/). GALAXY can be accessed via http://tinyurl.com/cws-galaxy

CPX based synthesis for binaural auralization of vehicle rolling noise to an arbitrary positioned stander-by receiver

Virtual reality is becoming an important tool for studying the interaction between pedestrians and road vehicles, by allowing the analysis of potentially hazard situations without placing subjects in real risk. However, most of the current simulators are unable to accurately recreate traffic sounds that are congruent with the visual scene. This has been recognized as a fault in the virtual audio-visual scenarios used in such contexts. This study proposes a method for delivering a binaural auralization of the noise generated by a moving vehicle to an arbitrarily located moving listener (pedestrian). Building on previously developed methods, the proposal presented here integrates in a novel way a dynamic auralization engine, thus enabling real-time update of the acoustic cues in the binaural signal delivered via headphones. Furthermore, the proposed auralization routine uses Close ProXimity (CPX) tyre-road noise signal as sound source input, facilitating the quick interchangeability of source signals, and easing the noise collection procedure. Two validation experiments were carried out, one to quantitatively compare field signals with CPX-derived virtual signal recordings, and another to assess these same signals through psychoacoustic models. The latter aims to assure that the reproduction of the synthesized signal is perceptually similar to one occurring on pedestrian/vehicle interactions during situations of street crossing. Discrepancies were detected, and emphasized when the vehicle is within close distance from the receiver (pedestrian). However, the analysis indicated that these pose no hindrance to the study of vehicle–pedestrian interaction. Improvements to the method are identified and further developments are proposed.This work was supported by the ‘‘Fundação para a Ciência e a Tecnologia” [PTDC/ECM-TRA/3568/2014, SFRH/BD/131638/2017, UIDB/04029/2020] This work is part of the activities of the research project AnPeB – ‘‘ANalysis of PEdestrians Behaviour based on simulated urban environments and its incorporation in risk modelling” (PTDC/ECM TRA/3568/2014), funded by the ‘‘Promover a Produção Científica e Desenvolvimento Tecnológico e a Constituição de Redes Temáti cas” (3599-PPCDT) project and supported by the ‘‘European Com munity Fund FEDER” and the doctoral scholarship SFRH/ BD/131638/2017, funded by ‘‘Fundação para a Ciência e a Tecnolo gia (FCT)”

Single-objective versus multiobjective optimization of water distribution systems accounting for greenhouse gas emissions by carbon pricing

Previous research has demonstrated that there are significant trade-offs between the competing objectives of minimizing costs and greenhouse gas (GHG) emissions for water distribution system (WDS) optimization. However, upon introduction of an emission trading scheme, GHG emissions are likely to be priced at a particular level. Thus, a monetary value can be assigned to GHG emissions, enabling a single-objective optimization approach to be used. This raises the question of whether the introduction of carbon pricing under an emission trading scheme will make the use of a multiobjective optimization approach obsolete or whether such an approach can provide additional insights that are useful in a decision-making context. In this paper, the above questions are explored via two case studies. The optimization results obtained for the two case studies using both single-objective and multiobjective approaches are analyzed. The analyses show that the single-objective approach results in a loss of trade-off information between the two objectives. In contrast, the multiobjective approach provides decision makers with more insight into the trade-offs between the two objectives. As a result, a multiobjective approach is recommended for the optimization of WDSs accounting for GHG emissions when considering carbon pricing. © 2010 ASCE.Wenyan Wu, Holger R. Maier, and Angus R. Simpso

Accounting for greenhouse gas emissions in multiobjective genetic algorithm optimization of water distribution systems

Considerable research has been carried out on the optimization of water distribution systems (WDSs) over the last three decades. In previous research, attention has mainly focused on the minimization of cost, due to the high expenditure associated with the construction and maintenance of such systems. However, the impacts of WDSs on the environment usually have not been considered adequately. The recent increasing awareness of sustainability and climate change, especially global warming, has led to research where greenhouse gas (GHG) emissions are considered. In the study described in this paper a multiobjective genetic algorithm for WDS optimization has been used as an explorative tool to investigate the trade-offs between the traditional economic objective of minimizing costs and an additional environmental objective of minimizing GHG emissions. The impacts of minimizing GHG emissions on the results of WDS optimization have been explored for a case study in this paper. The results indicate that the inclusion of GHG emission minimization as one of the objectives results in significant trade-offs between the economic and environmental objectives. Furthermore, a sensitivity analysis has been conducted by using different discount rates in a present value analysis for computing both ongoing costs and GHG emissions. The results obtained show that the Pareto-optimal front is very sensitive to the discount rates used. As a result, the selection of discount rates has a significant impact on final decision making. © 2010 ASCE.Wenyan Wu, Angus R. Simpson, and Holger R. Maie

A combined NLP-differential evolution algorithm approach for the optimization of looped water distribution systems

This paper proposes a novel optimization approach for the least cost design of looped water distribution systems (WDSs). Three distinct steps are involved in the proposed optimization approach. In the first step, the shortest-distance tree within the looped network is identified using the Dijkstra graph theory algorithm, for which an extension is proposed to find the shortest-distance tree for multisource WDSs. In the second step, a nonlinear programming (NLP) solver is employed to optimize the pipe diameters for the shortest-distance tree (chords of the shortest-distance tree are allocated the minimum allowable pipe sizes). Finally, in the third step, the original looped water network is optimized using a differential evolution (DE) algorithm seeded with diameters in the proximity of the continuous pipe sizes obtained in step two. As such, the proposed optimization approach combines the traditional deterministic optimization technique of NLP with the emerging evolutionary algorithm DE via the proposed network decomposition. The proposed methodology has been tested on four looped WDSs with the number of decision variables ranging from 21 to 454. Results obtained show the proposed approach is able to find optimal solutions with significantly less computational effort than other optimization techniques.Feifei Zheng, Angus R. Simpson and Aaron C. Zecchi