The Sanitation Company of Paraná (Sanepar) required the development of a long-term master plan to ensure reliable water supply for the Curitiba metropolitan region, which was projected to serve approximately 4.1 million inhabitants by 2040.
At the time of the project, the Integrated Supply System of Curitiba and the Metropolitan Region (SAIC) was already supplying water to 12 of the 14 cities in the urban core, producing 9.5 cubic meters per second to support a population of 3 million. However, increasing demand, system inefficiencies, and the need for future scalability created significant challenges in maintaining a balanced and sustainable water supply.
The project required analyzing a large 642-square-kilometer supply area to evaluate system performance, reduce water losses, improve energy efficiency, and ensure that supply could meet future demand. This level of complexity demanded a data-driven approach capable of accurately simulating hydraulic behavior across the network.
To address these challenges, Proensi developed a hydraulic model of the water distribution system using WaterCAD, enabling detailed simulation and analysis of system performance.
The team used the model to evaluate supply and demand conditions, analyze system behavior under different scenarios, and define the optimal configuration of the network. This included detailed assessment of distribution strategies, infrastructure requirements, and operational improvements needed to enhance system efficiency.
By leveraging hydraulic simulations, the team was able to identify opportunities to optimize network design, reduce inefficiencies, and support strategic planning decisions. The digital modeling approach provided a reliable foundation for developing a scalable and efficient master plan for the metropolitan water system.
The use of hydraulic modeling enabled the project team to significantly improve system efficiency while optimizing infrastructure investment.
Within a 15-square-kilometer pilot area, the optimized design avoided USD 1.06 million in new infrastructure costs, demonstrating the effectiveness of data-driven planning. In addition, peak-hour energy consumption was reduced by 39 percent, highlighting substantial improvements in operational efficiency.
These results validated the effectiveness of the modeling approach and supported the development of a long-term, scalable water supply strategy capable of meeting future population growth while maintaining reliable service delivery.
