Optimal discharge protocol for urban stormwater settling tank across different scenarios under limited data aided with Monte-Carlo simulation incorporated mathematical model

Abstract

Nonpoint source contamination from early stormwater runoff in the urban area has been frequently reported, and a comprehensive urban stormwater treatment system in high population density location is essential. However, there is an absence of a comprehensive discharge protocol for the settling tank that leads to inefficient treatment process with a non-adaptive practice that fixing certain discharge period. A case study in a large-scale urban settling tank located at Y-city, South Korea is considered to validate the discharge protocol proposed. The stormwater is first analyzed with its respective characteristics from various interception facilities, which act as buffers before entering settling tank. Thereupon, homogenous stormwater entered from multiple interception facilities is collected accordingly from the settling tank to assess the settling performance by measuring the suspended solid (SS) concentration in different timeframes. A mathematical model targeting the settling tank is then developed and further calibrated at different settling tank layers across different settling periods with multi-objective genetic algorithms based on the settling parameters considered. An uncertainty analysis is then conducted due to the relatively small dataset collected by incorporating 10,000 attempts of Monte-Carlo simulation, varying the SS concentration at different layers in the settling tank. As a result, discharge time for the top and middle layer by obeying the discharge limit falls at the 18th and 58th hour from the initial stormwater entry to the settling tank. This study successfully demonstrated the approach on identifying optimal discharge protocol for urban settling tank based on the initial SS concentration under limited data.

ZiYu Lin

Ph.D Candidate (she/her/hers)

Life-long learner and enthusiastic to provide solutions to enhance environment quality.