Advanced control of a water supply system: a case study

Bakker, Mark; Rajewicz, T.; Kien, H.; Vreeburg, J.H.G.; Rietveld, L.C.

doi:10.2166/wpt.2014.030

Cited by 13 publications

(11 citation statements)

References 13 publications

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“…Pump 40 uses the curve in Equation 17and assumes an efficiency of 80% at its rated flow. The efficiency after changing the speed can be calculated according to Equations (12)- (14). = 46.67 − 0.001615 (17) Pumps 38 and 39 use the curve in Equation (16) and assume an efficiency of 78% at their rated flow.…”

Section: Calculations and Resultsmentioning

confidence: 99%

“…Pump 40 uses the curve in Equation (17) and assumes an efficiency of 80% at its rated flow. The efficiency after changing the speed can be calculated according to Equations (12)- (14).…”

Section: Calculations and Resultsmentioning

confidence: 99%

“…Dynamic methods were used to control the real-time outlet pressure of pump stations. The comparison before and after the implementation showed that the outlet pressure dropped by 29%, the background leakage decreased by 20%, and the total energy consumption cost of the system decreased by 11.5% on the case network [14]. De Marchis et al (2016) studied the effect of pumps as turbines (PATs) in networks, by mainly considering the influence of energy changes brought by user behaviors on network pressure recovery and PAT positioning.…”

Section: Introductionmentioning

confidence: 99%

“…The heads of reservoirs 26, 27, 28, are 23 m,18 m, and 18 m, respectively. Two types of pumps are chosen according to the maximum water demand.= 53.33 − 0.002191(14)…”

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confidence: 99%

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Leakage Control and Energy Consumption Optimization in the Water Distribution Network Based on Joint Scheduling of Pumps and Valves

Shao

et al. 2019

Energies

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Apart from water quality, leakage control and energy consumption management are the most concerning challenges for water treatment plants (WTPs). The joint scheduling of pumps and pressure reducing valves (PRVs) in the water distribution network can reduce excessive pressure and distribute pressure more evenly, which achieves comprehensive reduction of leakages and energy consumption. Taking into account the main shortcomings of the commonly used methods, such as scheduling pumps or PRVs separately, or optimizing PRV settings when their position is given, etc., this paper has taken the PRV (position and setting) and the working status of variable speed pumps (VSPs) as decision variables and the cost savings contributed by leakage reduction and energy consumption savings as the objective function, which maximized the economic benefits brought by PRV and/or VSP scheduling. A genetic algorithm (GA) was used to optimize the solution under multiple working conditions. The performance of three control strategies (PRV-only scheduling, VSP-only scheduling, and joint scheduling of PRVs and VSPs) are compared to each other based on a small network. Joint scheduling has achieved the best economic benefits in reducing the gross cost (contributed by leakage and energy consumption) of the three control strategies, which results in a leakage reduction of 33.4%, an energy consumption reduction of 25.4%, and a total cost reduction of 33.1%, when compared to the original network, and saving about 1148 m 3 water (7% of the original consumption) and 722 kWh electric energy (25.4% of the original consumption) per day.

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Section: Calculations and Resultsmentioning

confidence: 99%

“…Pump 40 uses the curve in Equation (17) and assumes an efficiency of 80% at its rated flow. The efficiency after changing the speed can be calculated according to Equations (12)- (14).…”

Section: Calculations and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The heads of reservoirs 26, 27, 28, are 23 m,18 m, and 18 m, respectively. Two types of pumps are chosen according to the maximum water demand.= 53.33 − 0.002191(14)…”

mentioning

confidence: 99%

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Leakage Control and Energy Consumption Optimization in the Water Distribution Network Based on Joint Scheduling of Pumps and Valves

Shao

et al. 2019

Energies

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“…Turbidity (the cloudiness of a fluid caused by suspended particles) is one of the simplest measurements associated with suspended particles in a fluid, and it is widely used as a general indicator of performance in water & wastewater treatment systems (Bertrand-Krajewski, 2004;Arévalo et al, 2009;Bakker et al, 2014;Castaño and Higuita, 2016). However, in many cases, measurement procedures are not always conducted as outlined by the Standard Methods (Hannon et al, 2014;Doherty et al, 2017).…”

Section: Turbidity In Fluidsmentioning

confidence: 99%

A novel image processing-based system for turbidity measurement in domestic and industrial wastewater

Mullins

Coburn

Hannon

et al. 2018

Water Science and Technology

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Wastewater treatment facilities are continually challenged to meet both environmental regulations and reduce running costs (particularly energy and staffing costs). Improving the efficiency of operational monitoring at wastewater treatment plants (WWTPs) requires the development and implementation of appropriate performance metrics; particularly those that are easily measured, strongly correlate to WWTP performance, and can be easily automated, with a minimal amount of maintenance or intervention by human operators. Turbidity is the measure of the relative clarity of a fluid. It is an expression of the optical property that causes light to be scattered and absorbed by fine particles in suspension (rather than transmitted with no change in direction or flux level through a fluid sample). In wastewater treatment, turbidity is often used as an indicator of effluent quality, rather than an absolute performance metric, although correlations have been found between turbidity and suspended solids. Existing laboratory-based methods to measure turbidity for WWTPs, while relatively simple, require human intervention and are labour intensive. Automated systems for on-site measuring of wastewater effluent turbidity are not commonly used, while those present are largely based on submerged sensors that require regular cleaning and calibration due to fouling from particulate matter in fluids. This paper presents a novel, automated system for estimating fluid turbidity. Effluent samples are imaged such that the light absorption characteristic is highlighted as a function of fluid depth, and computer vision processing techniques are used to quantify this characteristic. Results from the proposed system were compared with results from established laboratory-based methods and were found to be comparable. Tests were conducted using both synthetic dairy wastewater and effluent from multiple WWTPs, both municipal and industrial. This system has an advantage over current methods as it provides a multipoint analysis that can be easily repeated for large volumes of wastewater effluent. Although the system was specifically designed and tested for wastewater treatment applications, it could have applications such as in drinking water treatment, and in other areas where fluid turbidity is an important measurement.

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A Selective Literature Review on Leak Management Techniques for Water Distribution System

Gupta

Kulat

2018

Water Resour Manage

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Advanced control of a water supply system: a case study

Cited by 13 publications

References 13 publications

Leakage Control and Energy Consumption Optimization in the Water Distribution Network Based on Joint Scheduling of Pumps and Valves

Leakage Control and Energy Consumption Optimization in the Water Distribution Network Based on Joint Scheduling of Pumps and Valves

A novel image processing-based system for turbidity measurement in domestic and industrial wastewater

A Selective Literature Review on Leak Management Techniques for Water Distribution System

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