Use of an artificial neural network to capture the domain knowledge of a conventional hydraulic simulation model

Rao, Z.; Alvarruiz, Fernando

doi:10.2166/hydro.2006.014

Cited by 74 publications

(51 citation statements)

References 20 publications

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“…Most of the previous studies focused on a small-scale elevated tanks [5,10,28]. A small portion of real systems are similar to small test networks of these researches, but most of the time we face large networks with a couple of hundred pipes, junctions, and a considerable number of pumps, valves, tanks, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

PEPSO: Reducing Electricity Usage and Associated Pollution Emissions of Water Pumps

Miller

2017

Water

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Using metaheuristic optimization methods has enabled researchers to reduce the electricity consumption cost of small water distribution systems (WDSs). However, dealing with complicated WDSs and reducing their environmental footprint remains a challenge. In this study a multi-objective version of Pollution Emission Pump Station Optimization tool (PEPSO) is introduced that can reduce the electricity cost and pollution emissions (associated with the energy consumption) of pumps of WDSs. PEPSO includes a user-friendly graphical interface and a customized version of the non-dominated sorting genetic algorithm. A measure that is called "Undesirability Index" (UI) is defined to assist the search for a promising optimization path. The UI also ensures that the final results are desirable and practical. The various features of PEPSO are tested under six scenarios for optimizing the WDS of Monroe City, MI, and Richmond, UK. The test results indicate that in a reasonable amount of time, PEPSO can optimize and provide practical results for both WDSs.

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Section: Literature Reviewmentioning

confidence: 99%

PEPSO: Reducing Electricity Usage and Associated Pollution Emissions of Water Pumps

Miller

2017

Water

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“…In the preceding paper, Rao & Alvarruiz (2007) described the methodology adopted for rapidly predicting the consequences of different control settings on the performance of the network, which is based on replicating a detailed hydraulic simulation model by means of an artificial neural network (ANN). This paper focuses on selecting the best combination of control settings not only for the present situation but also the expected conditions up to a given operating horizon in order to minimize the overall pumping costs.…”

Section: Approach Adoptedmentioning

confidence: 99%

Development of a real-time, near-optimal control process for water-distribution networks

Rao

Salomons

2007

Journal of Hydroinformatics

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This paper presents a new approach for the real-time, near-optimal control of water-distribution networks, which forms an integral part of the POWADIMA research project. The process is based on the combined use of an artificial neural network for predicting the consequences of different control settings and a genetic algorithm for selecting the best combination. By this means, it is possible to find the optimal, or at least near-optimal, pump and valve settings for the present time-step as well as those up to a selected operating horizon, taking account of the short-term demand fluctuations, the electricity tariff structure and operational constraints such as minimum delivery pressures, etc. Thereafter, the near-optimal control settings for the present time-step are implemented. Having grounded any discrepancies between the previously predicted and measured storage levels at the next update of the monitoring facilities, the whole process is repeated on a rolling basis and a new operating strategy is computed. Contingency measures for dealing with pump failures, pipe bursts, etc., have also been included. The novelty of this approach is illustrated by the application to a small, hypothetical network. Its relevance to real networks is discussed in the subsequent papers on case studies.

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“…Whilst the numbers of neurons in the input and output layers are fixed by the nature of the application, the number in the hidden layer is to some extent based on trial-and-error, so in that sense, the process is somewhat heuristic. Details of the actual process used to replicate a conventional hydraulic simulation model can be found in the second paper of this special edition (Rao & Alvarruiz 2007).…”

Section: Developing the Ann Predictormentioning

confidence: 99%

Optimizing the operation of the Haifa-A water-distribution network

Salomons

Goryashko

Shamir

et al. 2007

Journal of Hydroinformatics

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Haifa-A is the first of two case studies relating to the POWADIMA research project. It comprises about 20% of the city's water-distribution network and serves a population of some 60,000 from two sources. The hydraulic simulation model of the network has 126 pipes, 112 nodes, 9 storage tanks, 1 operating valve and 17 pumps in 5 discrete pumping stations. The complex energy tariff structure changes with hours of the day and days of the year. For a dynamically rolling operational horizon of 24 h ahead, the real-time, near-optimal control strategy is calculated by a software package that combines a genetic algorithm (GA) optimizer with an artificial neural network (ANN) predictor, the latter having replaced a conventional hydraulic simulation model to achieve the computational efficiency required for real-time use. This paper describes the Haifa-A hydraulic network, the ANN predictor, the GA optimizer and the demand-forecasting model that were used. Thereafter, it presents and analyses the results obtained for a full (simulated) year of operation in which an energy cost saving of some 25% was achieved in comparison to the corresponding cost of current practice. Conclusions are drawn regarding the achievement of aims and future prospects.

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Use of an artificial neural network to capture the domain knowledge of a conventional hydraulic simulation model

Cited by 74 publications

References 20 publications

PEPSO: Reducing Electricity Usage and Associated Pollution Emissions of Water Pumps

PEPSO: Reducing Electricity Usage and Associated Pollution Emissions of Water Pumps

Development of a real-time, near-optimal control process for water-distribution networks

Optimizing the operation of the Haifa-A water-distribution network

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