Impact of Water Demand Pattern on Calibration Process

Bartkiewicz, Ewelina; Zimoch, Izabela

doi:10.3390/ecws-2-04961

Cited by 3 publications

(1 citation statement)

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“…In the same direction, the authors from [36] analyzed the groundwater resources used for irrigation and identified a non-linear multi-year optimal distribution model of groundwater, which is capitalized for obtaining a sustainable utilization of groundwater in irrigation. The water demand pattern is analyzed in terms of impact over the calibration process in [37]. The authors of [38] presented the optimization of water treatment regarding the water turbidity Processes 2020, 8, 282 3 of 18…”

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confidence: 99%

Industrial Internet of Things and Fog Computing to Reduce Energy Consumption in Drinking Water Facilities

et al. 2020

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The industry is generally preoccupied with the evolution towards Industry 4.0 principles and the associated advantages as cost reduction, respectively safety, availability, and productivity increase. So far, it is not completely clear how to reach these advantages and what their exact representation or impact is. It is necessary for industrial systems, even legacy ones, to assure interoperability in the context of chronologically dispersed and currently functional solutions, respectively; the Open Platform Communications Unified Architecture (OPC UA) protocol is an essential requirement. Then, following data accumulation, the resulting process-aware strategies have to present learning capabilities, pattern identification, and conclusions to increase efficiency or safety. Finally, model-based analysis and decision and control procedures applied in a non-invasive manner over functioning systems close the optimizing loop. Drinking water facilities, as generally the entire water sector, are confronted with several issues in their functioning, with a high variety of implemented technologies. The solution to these problems is expected to create a more extensive connection between the physical and the digital worlds. Following previous research focused on data accumulation and data dependency analysis, the current paper aims to provide the next step in obtaining a proactive historian application and proposes a non-invasive decision and control solution in the context of the Industrial Internet of Things, meant to reduce energy consumption in a water treatment and distribution process. The solution is conceived for the fog computing concept to be close to local automation, and it is automatically adaptable to changes in the process’s main characteristics caused by various factors. The developments were applied to a water facility model realized for this purpose and on a real system. The results prove the efficiency of the concept.

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Industrial Internet of Things and Fog Computing to Reduce Energy Consumption in Drinking Water Facilities

et al. 2020

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Calibration of the continuous water model based on the NRW patterns

Torkaman

Ahmadi

Aminnejad

2021

Journal of Applied Water Engineering and Research

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Off-Line Data Validation for Water Network Modeling Studies

Quiñones-Grueiro

Torres

Verde

2019

The 4th International Electronic Conference on Water Sciences

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The success of the analysis and design of a Water Network (WN) is strongly dependent on 1 the veracity of the data and a priori knowledge used in the model calibration of the network. This fact 2 motivates this paper in which an off-line approach to verify data-sets acquired from WN is proposed. 3This approach allows the data separation of abnormal and normal events without requiring high 4 expertise for a large raw database. The core of the approach is an unsupervised classification tool 5 that does not requires the features of the different events to be identified. The proposal is applied to 6 data-sets acquired from a Mexican water management utility located in the center part of Mexico. The 7 data-sets were pre-processed to be synchronized since they were recorded and sent with different and 8 irregular sampling times to a web platform. The pressures and flow-rate conforming the data-sets (DMA) is formed by 90 nodes and 78 pipes and it provides service to approximately 2000 consumers. 11The raw data identified as generated by abnormal events were validated with the reports of the 12 DMA managers. The abnormal events identified were communication problems, sensor failures, and 13 draining of the network reservoir.

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Impact of Water Demand Pattern on Calibration Process

Cited by 3 publications

References 2 publications

Industrial Internet of Things and Fog Computing to Reduce Energy Consumption in Drinking Water Facilities

Industrial Internet of Things and Fog Computing to Reduce Energy Consumption in Drinking Water Facilities

Calibration of the continuous water model based on the NRW patterns

Off-Line Data Validation for Water Network Modeling Studies

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