Determination of Optimal Location and Capacity of Detention Facilities

Lim, Sanghun; Ho, Vincent; Lee, S.Y.; Yoo, Do Guen; Kim, J.H.

doi:10.1016/j.proeng.2014.02.115

Cited by 12 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is important to note that in-line detention without RTC (option two) has on the contrary the lowest effect on the peak flow reduction (26%). Traditional off-line detention gained the result of 39%, which correlates with the numbers presented in other research articles [7,9]. From the economic perspective it is quite obvious that the base scenario has the lowest IC as this solution has no costly facilities for the flow detention ( Figure 5).…”

Section: Resultssupporting

confidence: 76%

“…Free capacity of the drainage collectors has been often considered as in-line storage. Although effectiveness and optimal distribution of the off-line facilities is the objective of many recent studies [7][8][9], in-line facilities are mainly considered as a possibility of utilizing the excess capacity of the pipeline, not specially designed for a storage [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Smart In-Line Storage Facilities in Urban Drainage Network

et al. 2018

View full text Add to dashboard Cite

Urban stormwater drainage systems (UDS) are severely affected by the changing climate bringing along inter alia more intense rainfall events. The conduits, usually having limited capacity, are unable to cope with these excessive flowrates. Therefore, measures must be undertaken to temporarily accumulate extra flowrates in order to avoid the flooding. There are several options available to tackle this challenge, e.g., low impact development (LID) solutions, best management practices (BMP), stormwater real-time control measures (RTC). In this study the efficiency of in-line and off-line detention tanks are analyzed. Moreover, new concept of smart in-line storage system is created and evaluated. This solution shows significant reduction in peak flow, economic benefit and is particularly suitable for the districts with limited construction space. The concept has been successfully tested in 10 ha dense urban development area in Estonian capital Tallinn.

show abstract

Section: Resultssupporting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Smart In-Line Storage Facilities in Urban Drainage Network

et al. 2018

View full text Add to dashboard Cite

show abstract

“…This may not be the most effective design and it is recognised that greater benefit may be achieved from more appropriately located and sized tanks; however, whilst there have been studies on the sizing and placement of storage tanks (e.g. Duan et al 2016, Li et al 2015, Wang et al 2017, there is no standard for determining the required location and capacities of storage facilities within a sewer network (Lim et al 2014) and to enable comparison of the VCSs, interventions must be applied to each in the 0 = 1 − × (1) same manner. Whilst not providing optimum designs, the chosen approach facilitates a simple evaluation of the effectiveness of distributed storage in a wide range of case studies.…”

Section: Reduce Impervious Surface Areamentioning

confidence: 99%

Attribute-based intervention development for increasing resilience of urban drainage systems

Sweetapple

Farmani

et al. 2018

Water Science and Technology

View full text Add to dashboard Cite

Resilience building commonly focuses on attributes such as redundancy. Whilst this may be effective in some cases, provision of specific attributes does not guarantee resilient performance and research is required to determine the suitability of such approaches. This study uses 250 combined sewer system virtual case studies to explore the effects of two attribute-based interventions (increasing distributed storage and reducing imperviousness) on performance-based resilience measures. These are found to provide improvement in performance under system failure in the majority of case studies, but it is also shown that attribute-based intervention development can result in reduced resilience.

show abstract

“…To achieve strong results, uncertainty impacts should be considered in drainage network optimal rehabilitation. Lim et al [24] presented a drainage network multi-objective rehabilitation approach considering the installation of detention facilities and the optimization of their locations and capacity by a multi-objective genetic algorithm. The aim was total overflow reduction while minimizing investment.…”

Section: Introductionmentioning

confidence: 99%

Urban Drainage Networks Rehabilitation Using Multi-Objective Model and Search Space Reduction Methodology

2019

View full text Add to dashboard Cite

The drainage network always needs to adapt to environmental and climatic conditions to provide best quality services. Rehabilitation combining pipes substitution and storm tanks installation appears to be a good solution to overcome this problem. Unfortunately, the calculation time of such a rehabilitation scenario is too elevated for single-objective and multi-objective optimization. In this study, a methodology composed by search space reduction methodology whose purpose is to decrease the number of decision variables of the problem to solve and a multi-objective optimization whose purpose is to optimize the rehabilitation process and represent Pareto fronts as the result of urban drainage networks optimization is proposed. A comparison between different model results for multi-objective optimization is made. To obtain these results, Storm Water Management Model (SWMM) is first connected to a Pseudo Genetic Algorithm (PGA) for the search space reduction and then to a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization. Pareto fronts are designed for investment costs instead of flood damage costs. The methodology is applied to a real network in the city of Medellin in Colombia. The results show that search space reduction methodology provides models with a considerably reduced number of decision variables. The multi-objective optimization shows that the models’ results used after the search space reduction obtain better outcomes than in the complete model in terms of calculation time and optimality of the solutions.

show abstract

Determination of Optimal Location and Capacity of Detention Facilities

Cited by 12 publications

References 5 publications

Smart In-Line Storage Facilities in Urban Drainage Network

Smart In-Line Storage Facilities in Urban Drainage Network

Attribute-based intervention development for increasing resilience of urban drainage systems

Urban Drainage Networks Rehabilitation Using Multi-Objective Model and Search Space Reduction Methodology

Contact Info

Product

Resources

About