Smart Water Grid Anomaly Detection and Localization
With the advancement of Internet of Things (IoT) technology, a large amount of data, often referred to as Big Data, is collected and will need to be analyzed for improving system operations and water service to Singaporeans. This project is proposed to develop and apply data analytics to truly enable Smart Water Grid (SWG) as an integral component of smart Singapore. The objective of this project is to operationalize the outcomes of the ongoing collaboration and enhance the SWG operation. The specific objectives are (1) to enable the near real-time analysis of PUB SWG monitoring data; (2) to detect the anomaly events for the SWG operation; (3) to localize anomaly event hotspots for efficiently pinpointing the event in the field, and (4) to enhance PUB SWG operation and management with the hydraulic models calibrated using monitoring data.
This project has been jointly funded by Bentley Systems and Singapore National Research Foundation under its Competitive Research Programme (CRP) (Water) and administered by PUB (PUB-1804-0087), Singapore’s national water agency. We officially kicked off the project on March 1, 2020, since then, a team of R&D engineers have been hired and onboarded for developing the software solution that is developed and deployed to Singapore Government Commercial Cloud and tested/benchmarked in three water supply zones in Singapore.
In addition to software development, the team has actively pursued the through leadership by publishing peer-reviewed technical papers, giving presentations at international conferences and filing invention patents, as follows.
Hydrant Selection Optimization for Flow Testing
We see hydrants on the side of streets. They do not look great, some of them are even rusty. However, it is hydrants that enable fire fighters to connect fire engine hoses with underground water pipelines and help with extinguishing the fire whenever it occurs. Fire hydrants are installed throughout urban water systems. It is standard practice to install hydrants every 500 ft in USA. Thus, there are hundreds and even thousands of hydrants installed in a urban water distribution system depending on the size of the city. Hydrant flow testing is not only routinely required (and widely adopted) for estimating the available fire flow, but also used for collecting pressure data for hydraulic model calibration, as well as flushing the pipelines to ensure adequate water quality. In order to collect good quality of field data, it is usually recommended that flow testing be conducted with the hydrants located at the outskirt of the distribution systems, and hydrant flow should result in a significant pressure drop, e.g. at least 10 psi or 70 kPa. With hundreds or even thousands of hydrants in a water distribution system, it is not straightforward task to select which hydrants are to be tested or flushed so that good quality of pressure data can be collected, and pipelines are cleaned up. In order to maximize the performance of the flow testing, in particular for the purpose of hydraulic model calibration, a new method is developed to search for a combination of the hydrants for flow testing. The implemented method is tested on a benchmark of water distribution system model. The results obtained for a benchmark system are compared with the hydrant selection solution by the experienced engineers. The performance of the optimized hydrant flow testing is significantly improved when comparing with the solution provided by the engineers.
Published Papers:
Parallelized Hydraulic and Water Quality Analysis
The BWN-II calls for teams/individuals from academia, consulting firms, and utilities to propose a design methodology and apply it to a real water distribution system. The results of the BWN-II will be presented at a special session of the upcoming 14th Water Distribution Systems Analysis Symposium. The problem is to challenge the researchers to come up with solution to optimize (1) new pipe sizes; (2) parallel pipes; (3) pump station expansion; (4) tank expansion and (5) pump and valve controls over 168 hours (one week). The solution needs to satisfy the normal operating condition and abnormal conditions (power failure at any hour over 168 hours). Three criteria/objective functions, including least cost (capital and operational), least CO2 emission and least water age (retention time). I started to research the method to solve the problem.