Leveraging Big Data to Improve Water System Operations

Thompson, Kate; Kadiyala, Raja

doi:10.1016/j.proeng.2014.11.235

Cited by 11 publications

(3 citation statements)

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“…Transformation into smart utilities will help utilities to leverage the vast amount of data they generate to optimize their service delivery [113][114][115]. Availability of comprehensive data on the performance of water utilities and energy use for water supply is not a unique situation to water utilities in Africa, as already discussed in reference [116].…”

Section: Discussionmentioning

confidence: 99%

Applying the Water-Energy Nexus for Water Supply—A Diagnostic Review on Energy Use for Water Provision in Africa

Macharia¹,

Kreuzinger²,

Kitaka

2020

Water

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This work explores the application of the Water-Energy Nexus concept for water supply in the African context, where its operationalization is quite limited compared to developed regions. Furthermore, water supply and demand drivers and their influence on energy use are examined. This study found that there is limited literature available on the operationalization of the concept, and energy use is not considered a key performance indicator by water regulators and utilities. Regionally, most of the studies were carried out in the northern and southern Africa, where energy demand for water supply through desalination is high. An analysis of water supply and demand drivers show diminishing quantities of available freshwater, and increased anthropogenic pollutant loads in some areas are projected. Consequently, utilities will likely consider alternative energy-intensive water supply options. Increased population growth with the highest global urban growth rate is projected, with about 60% of the total population in Africa as urban dwellers by 2050. This implies huge growth in water demand that calls for investment in technology, infrastructure, and improved understanding of energy use and optimization, as the largest controllable input within utilities boundaries. However, it requires a data-driven understanding of the operational drivers for water supply and incorporation of energy assessment metrics to inform water-energy policies and to exploit the nexus opportunities.

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Section: Discussionmentioning

confidence: 99%

Applying the Water-Energy Nexus for Water Supply—A Diagnostic Review on Energy Use for Water Provision in Africa

Macharia¹,

Kreuzinger²,

Kitaka

2020

Water

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“…Employing what we have knowledge to take valid decisions may be an automated procedure in several situations. It can be useful to split BD into these three parts (Thompson and Kadiyala, 2014a;2014b;Shaw, 2017;Zhang et al, 2016;Ahmad et al, 2017;Chen and Han, 2016).…”

Section: Getting Mad To Be Smartmentioning

confidence: 99%

Applying big data in water treatment industry: A new era of advance

Ghernaout

2018

Int. j. adv. appl. sci.

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“…There are also examples where it is not possible to mathematically describe these relationships. These hidden relations in data are mostly extracted through some machine learning systems [9,[18][19][20][21][22], which are used in gaining a better understanding of the process or to help in the decision making [9,15,23,24]. These models are known as non-parametric models, presented as "black boxes" ("black box models"), and they are classified in the "data-driven" models [2,8,9,[25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Diagnostics and Control of Pumping Stations in Water Supply Systems: Hybrid Model for Fault Operating Modes

et al. 2022

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This paper indicates the importance and advantages of the application of hybrid models in the control of water supply systems. A range of possibilities provided by this scientific approach is presented in the practical examples related to the fault diagnostics and fault-tolerant control in the pumping station (PS) control systems. It is presented that continuous monitoring and recording of the data of the pumping stations operation processes (electrical parameters such as electrical power, pressure or flow in the pipelines, water levels in the tanks, changes in various discrete states, etc.) could represent a significant resource that can be used to develop various hybrid models using the appropriate “data-driven” techniques. During this process, data are transformed into information, and thereafter, information into knowledge. Based on this knowledge, the control of PS operation can be significantly improved and a significant increase in the user’s satisfaction can be achieved while the maintenance and operation costs can be reduced.

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Leveraging Big Data to Improve Water System Operations

Cited by 11 publications

References 0 publications

Applying the Water-Energy Nexus for Water Supply—A Diagnostic Review on Energy Use for Water Provision in Africa

Applying the Water-Energy Nexus for Water Supply—A Diagnostic Review on Energy Use for Water Provision in Africa

Applying big data in water treatment industry: A new era of advance

Diagnostics and Control of Pumping Stations in Water Supply Systems: Hybrid Model for Fault Operating Modes

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