Optimal design of runoff storage/release systems

• Published in: Water science and technology Vol. 32; no. 1; pp. 193 - 199
• Main Author: Segarra, Rafael I.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: London Elsevier Ltd 1995; IWA Publishing
• Subjects: Basins; Capacity; Catchments; Constraining; Constraints; Cost analysis; Cost engineering; Design; Design engineering; Design optimization; Efficiency; Frameworks; Hydrology; marginal analysis; Mathematical models; Methods; Minimum cost; modeling; Optimization; Parameter identification; Parameter sensitivity; Parameters; Pollutants; Process parameters; production function; Q1; Resilience; Runoff; Sedimentation; Sensitivity; Sensitivity analysis; Solutions; Storage; Storage capacity; Storage conditions; Storms; Stormwater; Trap efficiency; Traps; treatment; Water treatment; Watersheds; USA, Minnesota, Minneapolis; Hydrology; treatment; modeling; trap efficiency; optimization; runoff; storage; marginal analysis; production function
• ISSN: 0273-1223; 1996-9732
• DOI: 10.1016/0273-1223(95)00555-2

An optimization methodology is developed for obtaining the optimal allocation of storage capacity and release rate for the design of storm water detention facilities. A trap efficiency expression for the detention unit relates process hydrologic variables with the design variables of total storage capacity and release rate. The optimization model, formulated within the framework of production function theory and marginal analysis, yields least cost combinations of storage capacity and release rate under the performance constraint of runoff or pollutant trap efficiency. The whole procedure is implemented in an electronic spreadsheet. The model is applied to a 640 acre catchment in Minneapolis, where a minimum cost earthen basin is designed for storing runoff to provide a uniform release rate to a sedimentation facility. The methodology provides an efficient table top procedure for identifying optimal solutions for a given set of process parameters. Sensitivity and resilience can be promptly assessed through a simple manipulation of input parameters.