Innovyze Further Expands RDII Analyst Functionality, Setting New Standard for Sanitary and Combined Sewer System Model Calibration

New Features Allow Unprecedented Analysis and Comparison of Rainfall-Derived Inflow and Infiltration Data, Parameters for Complex Sewers

Innovyze has announced the newest release of its RDII Analyst (Rainfall-Derived Inflow and Infiltration) for InfoSWMM and H2OMAP SWMM. The new version delivers expanded functionality, incorporating many advanced Genetic Algorithm (GA) optimization features. It increases its unmatched productivity by openly inviting the users to further adjust the dry weather flow (DWF) and RTK parameters (with initial and maximum monthly storages for continuous simulation) to achieve a better fit and ultimately a better model based on their experiences. The release confirms Innovyze’s commitment to giving the world the most complete toolset for modeling current sanitary and combined sewer collection systems.

Excessive wet weather flow from rainfall-derived manhole and pipe defect inflow and infiltration is a major source of sanitary and combined sewer overflows. Controlling these overflows is vital in reducing risks to public health and protecting the environment from water pollution. Computer modeling plays an important role in determining sound and economical remedial solutions that reduce RDII; improve system integrity, reliability and performance; and avoid overflows.

The processes for converting rainfall to RDII flow in sanitary sewer systems are very complicated. In addition to rainfall and antecedent moisture conditions, factors controlling RDII responses include depth to groundwater, depth to bedrock, land slope, number and size of sewer system defects, type of storm drainage system, soil characteristics, and type of sewer backfill. Given this degree of complexity, flow-monitoring data must be combined with mathematical modeling and analytics to provide accurate results. The wastewater flow monitoring data obtained by sewer collection systems consists of dry-weather flow components, ground water flow and twelve (12) RDII flow components. A crucial step in successfully modeling sewer collection systems is the ability to decompose flow-monitoring data into RDII flow, ground water flow and dry weather flow and its flow pattern.

Significantly superior to the EPA Sanitary Sewer Overflow Analysis and Planning (SSOAP) program and powered by advanced GA optimization and comprehensive data analytics and scenario management, RDII Analyst provides the ability to quickly and reliably perform these types of advanced flow decomposition data monitoring. It has been updated with tabular comparisons between the observed and calibrated RDII data for each event, including R value, peak flow, hydrograph volume and depth. This allows the user to better evaluate simulated and monitored data and judge how well it correlates on a per event basis. The user can also directly edit estimated DWF mean values to apply site specific knowledge to the RDII Analyst DWF extraction algorithm. These altered DWF values can then be used to estimate the wet weather flow component of the monitored flow, using a combination of the DWF extraction algorithm and site-specific knowledge. The new version also allows direct edits to the twelve RTK and storage parameters plus manual curve fitting to apply site specific knowledge to the genetic algorithm parameter estimation. Manual curve fitting is valuable in timing differences between monitored and calibrated wet weather flow components and employing previous experience in estimating RTK parameters.

“Innovyze continues to listen to our customers, invest very heavily in R&D, and deliver the advanced tools they need to effectively support their wastewater and urban drainage modeling and management challenges,” said Paul F. Boulos, Ph.D., BCEEM, Hon.D.WRE, Dist.D.NE, Dist.M.ASCE, NAE, President, COO and Chief Technical Officer of Innovyze. “We are very excited that our vast worldwide customer base will now be able to use the powerful new features in RDII Analyst to enhance their modeling experiences, wrap better projects faster, and strengthen our communities’ sewer systems.”