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Software platform to optimise drinking water distribution systems


Over the last decades growing attention has been directed at the optimisation of drinking water distribution systems. But the complexity of these systems means that optimal solutions are hard to achieve using traditional (frequently handwork) methods. Numerical optimisation techniques offer help but have not found their way into water practice because of the lack of suitable tools. That is why KWR developed Gondwana: an optimisation software platform that specifically targets drinking water distribution systems. The platform combines flexibility with regard to optimisation problems with a user-friendly graphic interface. Gondwana thus unlocks powerful and frequently complex optimisation techniques for use by KWR’s hydraulic experts, and consequently also for the entire drinking water sector.

Perspective on optimally designed distribution networks

The drinking water distribution network that we have in the ground today is the result of an evolution over time of the materials used, and of the design philosophy and objectives. The design often incorporates experience-based knowledge and extra safety margins. Now that many drinking water companies are facing a mounting replacement challenge, it makes sense to think about the kind of replacement network structure that needs to be installed, and about conducting the design process in a more holistic manner, taking into account the various aspects, such as supply assurance, water quality, energy and, of course, costs. Although experts currently apply good strategies to these ends, numerical optimisation methods applied by experts could result in better designs than the classical approaches.

KWR has developed the Gondwana optimisation platform to bridge the gap between academic research and practical implementability.

Gondwana’s goal is to successfully meet the needs of water companies with regard to flexibility and connection with different application possibilities. Examples of Gondwana’s application possibilities include:

  • designing target structures and transitions from current configurations to target structures, with low costs and good performance as objectives. Performance can be determined, among others, by the level of reliability of supply, desired pressure, water quality, energy consumption and number of failures;
  • determining optimal DMAs (District Metered Areas), with the aim of maximising the detectability of water losses and other anomalies using as few flow meters as possible;
  • determining optimal valve configurations, with the aim of achieving low customer minutes lost (CML) and good section flushability;
  • determining optimal locations for water-quality sensors or sampling, with the aim of minimising detection time, maximising detection probability or coverage level, facilitating source determination, etc.

Different scenarios, such as variations in water demand, failures and contaminations, can be taken into account in all applications.

Optimisation is possible as ‘implicit multiobjective optimisation’, which results in a single design that performs the best regarding the selected combination and weighting of objectives.

‘Explicit multiobjective optimisation’ is also possible; this results in a collection of designs, all of which, in varying proportions, perform optimally regarding the selected objectives. This collection is referred to as a ‘Pareto front’. An example is shown in Figure 1, in which the two axes show the performance on two objectives. Minimisation is desired for both objectives. Each point represents one design, but only the brown points shown left bottom-edge (the Pareto front) perform optimally. The user can compare the different designs on the Pareto front and decide to what extent an improvement regarding one objective justifies a concession on the other.

Figure 1: Trade-offs between two objectives (performances on the horizontal axis against those on the vertical axis), with the optimal solutions on the Pareto front shown in brown.


Gondwana optimisation tool: powerful, flexible and user-friendly

Gondwana is built around EPANET* (for the hydraulic simulation of pipe networks) and the Inspyred library** for the meta-heuristic optimisation methods. By means of a graphic interface and a series of tab pages (Figure 2), the user can set up an optimisation problem in a structured manner: load existing networks, define the requirements the renewed network needs to satisfy, and determine in what way it needs to be improved (objectives).

Figure 2: Gondwana’s main screen with tab pages for the definition of component aspects, performance of calculations and analysis of results. The example network concerns the New York tunnels benchmark problem.


Tab Description

Start of problem definition, loading of initial network, and definition of pipe and nodal groups.

Scenarios Definition of multiple types of scenarios (network modifications, water demand, contamination scenarios).
Requirements Definition/import/export of additional data, such as available materials and costs.
Decision variables Definition of components, operating rules and characteristics that can be adjusted by Gondwana, e.g., pipe diameters and location of flow meters.
Objectives Definition of single or multiple objectives (for implicit or explicit multiobjective optimisation), such as cost minimisation and performance maximisation.
Constraints Definition of constraints for the network, possibly for individual scenarios, such as minimum pressure.
Optimisation Set up of the optimisation process (adjusted  genetic algorithm).
Run Scheduling and running of optimisation calculations, monitoring during calculation process.
Results Analysis, visualisation and export of optimisation results.

Once an optimisation problem is set up, Gondwana can get to work: by randomly making changes to the decision variables, while structurally detecting and further incorporating the several resulting performance improvements, in a process known as an ‘evolutionary algorithm’, the calculations progress, step by step, towards a better performing network. This is a little like ‘natural evolution’, allowing the conduct of a rapid and efficient search through a (large) solutions space. Once the optimisation process is completed, the user can look at the results, compare them with the current design, and write them to the desired format.

The tool is available for the performance of problem calculations by KWR.

Gondwana in practice

Between 2015 and 2017, Gondwana was used for the establishment of DMAs, and the design of and transition to Network blueprints. The results obtained have been promising:

  • The division of the supply areas into DMAs offers insight into the trade-off between the number of flow meters required and the sensitivity of the DMAs for the detection of anomalies. Based on this information, drinking water companies can take well-founded decisions about the DMA division that best meets their requirements and expectations.
  • In the design of target structures, it is apparent that the currently existing pipe networks can be significantly reduced in size. This means that cost-savings can be made, while the performance, in terms of supply assurance and pressure, is improved.
  • The transition from the current structures to the optimal target structures offers insight into the relationship between the number of network locations subjected to annual work and the decrease in the number of failures or the improvement of the network’s hydraulic performance. This information enables the drinking water companies to make better-founded choices.

Looking to the future

In 2018 Gondwana will be further developed with the objective of taking explicit account of aspects like water-demand uncertainty and cyber-attacks in the design and operation of a pipe network.

*Rossman, L. A. (2000). EPANET 2 Users Manual, U.S. Environmental Protection Agency.
**A. L. Garrett, Inspyred 1.0 Documentation, https://pythonhosted.org/inspyred/overview.html, Jan. 2015.