Water quality sensors help utilities to detect contaminant intrusion and to assess quality problems in water distribution systems by continuously measuring conductivity, PH, concentration of different substances and other related parameters directly from the network.
what should be
taken into account
Two important questions have to be answered in order to properly protect a water network against accidental or provoked contamination events and water quality problems: how many sensors are needed and where to place them. Answering these questions requires a decision about the criteria and requirements to be considered for achieving a good solution.
First of all, it is desired to identify quality problems as soon as possible, it means, to minimize the detection time. Second, at least one sensor should always be able to identify a quality problem no matter where the source of the problem is located. It means that the coverage of protection should be maximized. Additionally, the amount of bad quality water consumption should be minimized. Meaning that high population density areas should get a special attention. Last but not least, the cost should be kept as low as possible and it is directly proportional to the number of sensors to be installed.
how to approach the problem
The objectives that should be reached with a sensor placement solution are not going all in the same direction. Improving one of them will probably result in a detriment of another. For example, maximizing the protection coverage in the network will require either to increase the number of sensors (the cost) or to probably accept a higher value of detection time. As a consequence, the final solution will result from a compromise rather than from a unique “best alternative”. In this case, it is recommended to use a multi-objective approach, not for finding the optimal solution but for finding the Pareto front representing the best compromise that can be done among all objectives involved. Weighting objectives and adding them into a single expression for solving a single objective problem will probably result in finding one point of the Pareto. It is equivalent to make a compromise a priori without having any idea of how the solutions that will be obtained relates to the rest of potential solutions of the problem. For example, it cannot answer if it is worth to buy an additional sensor because it has no way to know how much improvement in protection coverage and detection time will bring an additional sensor. Those are the kind of questions that a multi-objective approach helps to answer. Those are the kind of questions and answers needed to find at the end a solution representing a good compromise among all objectives involved.
how the software
Water-Ing can help
Water-Ing supports utilities on deciding the position and amount of water quality sensors required in their networks. It runs a multi-objective optimization method for finding solutions representing the best compromise among maximizing protection coverage, minimizing detection time and reducing the impact of water quality problems with as less investment cost in sensors as possible.
With Water-Ing you have full flexibility for modifying or extending the way solutions are evaluated. You can introduce new objectives or customize the way current objectives are evaluated. Yes, you have access to the source code of the objective function.
The advanced visualization for multi-objective optimization problems makes Water-Ing a perfect candidate for being used in sensor placement. You can add as many 2D Pareto charts as desired and represent in each of them different relations between costs, detection time, protection coverage and bad water quality impact. Selected solutions in one chart will be automatically selected in the rest of charts indicating how a solution behaves with respect of all aspects involved in the problem.
The algorithm behind the solution search process is a unique combination of multi-objective evolutionary algorithms and data analytics. It intelligently combines the hydraulic engineering knowledge with the optimization process and learn your preferences in order to achieve better results.
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