Lost in optimisation of water distribution systems? A literature review of system operation
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Citations
Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization
A Coevolutionary Framework for Constrained Multiobjective Optimization Problems
Introductory overview: Optimization using evolutionary algorithms and other metaheuristics
Sustainable closed-loop supply chain network for an integrated water supply and wastewater collection system under uncertainty.
Lost in Optimisation of Water Distribution Systems? A Literature Review of System Design
References
Optimal Pump Scheduling for Water Supply Using Genetic Algorithms
Optimal Pump Scheduling For Water Distribution Systems
Parallel Programming Techniques Applied to Water Pump Scheduling Problems
Computer‐Aided Optimal Pump Selection for Water Distribution Networks
Scalable Parallel Computing Framework for Pump Scheduling Optimization
Related Papers (5)
Frequently Asked Questions (15)
Q2. What are the future works mentioned in the paper "Lost in optimisation of water distribution systems? a literature review of system operation" ?
Future research challenges for operational optimisation of WDSs are listed in Figure 6 and grouped according to steps involved in optimisation: ( i ) simulation model, ( ii ) optimisation model, ( iii ) optimisation method, and ( iv ) solution postprocessing. Regarding optimisation problems with water quality aspects, future research may consider the development of an optimisation model with an inbuilt flexibility for a general WDS, which could be customised for a specific WDS. A further research challenge is to analyse relationships between pumping costs and water quality using a set of realistic case studies to ascertain whether they are conflicting objectives or they can be somehow integrated, leading to reduced optimisation problem complexity. A methodology for an objective comparison of optimisation methods should be developed, so the best optimisation method for a particular case can be selected.
Q3. Why have deterministic methods started to appear in recent years?
In recent years however, deterministic methods have started toreappear, because they are more computationally efficient, thus more suitable for real-time control, as well asother applications (Creaco and Pezzinga 2015).
Q4. What are the constraints included in the optimisation models?
Please note that hydraulic constraints (such as conservation of mass of flow, conservation of energy, andconservation of mass of constituent) were not included in these statistics as they are normally included asimplicit constraints and forced to be satisfied by WDS modelling tool, such as EPANET.
Q5. What is the common type of surrogate model?
which are by far the most commonly used surrogate models, are based upon real neurologicalstructures and can be represented as directed graphs.
Q6. What is the proportion of objectives included in optimisation models?
The number of objectives included in optimisation models ranges from one to four, with a vast majorityof models (84%) being single-objective.
Q7. What is the importance of water distribution systems?
Water distribution systems (WDSs) represent a vast infrastructure worldwide, which is critical forcontemporary human existence from all social, industrial and environmental aspects.
Q8. What is the popular example of a hypothetical WDS?
Regarding optimisation problems with water quality aspects,future research may consider the development of an optimisation model with an inbuilt flexibility for ageneral WDS, which could be customised for a specific WDS.
Q9. What are the main types of surrogate models used to replace and approximate network simulations?
Surrogate models are efficienttools used to replace and approximate network simulations which can be very computationally expensiveand/or may become an obstacle in real-time implementations.
Q10. What is the level of flexibility in the WDSs?
A level of flexibility exists in the WDSs, which enables the supply of required water underdifferent operational schedules, more or less economically.
Q11. What are the main criteria for the classification of water quality optimisation for WDSs?
Based on the selected literature analysis, the following are the four main criteria for the classification ofoperational optimisation for WDSs: (i) application area, (ii) optimisation model, (iii) solution methodologyand (iv) test network.
Q12. What are the main application areas for optimisation of water quality?
As described in Section 3, there are three application areas: pump operation (Section 3.1), water qualitymanagement (Section 3.2) and valve control (Section 3.3).
Q13. What were the first optimisation models for multiquality WDSs?
The first optimisation models formultiquality WDSs considered pump operating costs only (Mehrez et al. 1992; Percia et al. 1997).
Q14. What is the common type of optimisation of water quality?
Optimisation of water quality exclusive of any other operational controls (i.e. pumps and/or valves) isaddressed in 15% of papers.
Q15. What is the promising way for selecting the solution from the Pareto set?
This mismatch leads to the research question of what is the most promising way for selecting the bestsolution from the Pareto set, which may involve providing the decision makers with a globally representativesubset of the non-dominated set that is sufficiently small to be tractable.