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Project Type
Key Points
System Description
The analysis is being conducted on over 20 unique pressure zones distributed across East Bay MUD’s water system, which serves approximately 1.4 million customers. These zones of analysis range in size from 1,000 – 40,000 pipes.
Purpose
As a part of East Bay MUD’s Collaborative and Holistic Pipeline Plan (CHPP), future blueprints and digital master plans are being curated to address pipeline sizing criteria (called Hydraulic Modeling Criteria) developed from District Engineering Standard Practices, master plans, and procedures with collaboration between WDPD, PID, and O&M. Utilizing a stitched demand-driven fire-flow scenario as an input model, Optimizer is being leveraged to examine the optimal pipe size configuration across the entirety of the pressure zone to ensure adequate hydraulic performance under fire-flow conditions at each hydrant in the network of interest.
Illustration of the two optimization objectives and the list of variables that make up each component
Project Scope
For this initiative, the optimization was focused on resolving the trade-off between the hydraulic performance criteria associated with providing adequate service under fire-flow conditions and the costs associated with pipeline upgrades required to meet those needs across the entirety of each individual pressure zone. The hydraulic performance criteria thresholds for minimum pressure and maximum velocity were not uniform across a given pressure zone; therefore they had to be differentiated by source type and location.
Outcomes
Optimizer was utilized to develop an efficient, transparent, and repeatable process which East Bay MUD engineers and hydraulic modelers could leverage to quickly identify the most cost-effective plan solutions for each pressure zone within their large, complex water distribution network. Through leveraging Optimizer, users can confidently identify solutions that minimize the hydraulic deficiencies under fire-flow scenarios and defend those plans utilizing transparent outputs like the one shown below. This approach has been built into a larger innovative process that the utility has designed to streamline its asset management and master planning approach, which will help provide reliable and affordable service to their customers far into the future.
Example optimization output from the web-based data visualization platform. Illustration shows the Pareto optimal front of solutions developed by Optimizer and the geospatial distribution of the plan recommendations.
