PhD thesis - The contribution of artificial intelligence to improve asset management and the performance of drinking water supply networks

  • Strasbourg, Bas-Rhin
  • CDD
  • Temps-plein
  • Il y a 1 mois
Offer DescriptionThe literature review shows that ANNs in the drinking water sector are trained on a local scale using series of data collected by sensors scattered throughout the network. Neural networks are used to predict network parameters. The work reviewed does not allow links to be established with decision-support models for asset management or for predicting performance indicators. The problem, hypotheses and research questions: The thesis we are proposing aims to answer the following research question:
How can artificial intelligence be used to exploit existing data collections or information systems within water services in order to improve asset management and the performance of drinking water supply networks? Over and above the scientific aspect, this project will strengthen the research capabilities of Engees and Icube in the field of drinking water and offers operational advantages that could lead to negotiation and arbitration tools for water utility managers to support public decision-making objectively. For years, water utilities have been encouraged to instrument their networks, but they now find themselves with vast quantities of monitoring data that are not used effectively to support decision-making. AI can therefore be a tool for exploring and exploiting this data in order to objectify and support public decision-making. Several operational and scientific challenges will need to be addressed. The first concerns the contribution of artificial intelligence to the analysis of monitoring data and their relevance as explanatory variables through the identification of performance indicators that can be considered as variables to be explained. It is not enough to have data, beyond its completeness, it is necessary to establish causal links with the performance indicators to be predicted. A business dimension needs to be integrated into the understanding and analysis of the available data. The second scientific challenge concerns the establishment of links between monitoring data from sensors scattered across drinking water networks and asset management actions that may involve maintenance or investment. In other words, how can we establish the link between consumption, flow or pressure measurement data and water losses? How can causality be established between observed data and asset management actions such as pipe repairs? How can the effectiveness of actions on the network be predicted ex ante? The third issue concerns the choice of artificial intelligence approaches to be used and their level of complexity. This raises questions about the learning methods to be used and the data processing or prediction algorithms to be employed. One of the challenges is to develop approaches that are not too complex and precise, so as to encourage the involvement and participation of service managers. Experience shows that if the models developed are too complex, their appropriation and use will be limited within the services. The fourth issue concerns the identification of potential links between monitoring data and asset data. This involves studying possible causalities between data. In the case of our research, we want to cross-reference these data in order to build a decision-support model capable of exploiting the heritage data and the monitoring data. The aim is to be able to cross-reference operational data and asset data.RequirementsResearch Field Engineering » Computer engineering Education Level Master Degree or equivalentSkills/Qualifications
  • Data analysis
  • Data science : capacity to understand and program methods by python
  • Understand the operation of a drinking water service
  • Experience in mathemaical modelling
  • Skills in asset management of infrastructures
  • Basic backgroung in economics and management
Specific Requirements
  • Good knowledge of machine learning approches & methods
  • Background in the assessment of key performance indicators ( KPIs) of water services
  • Capacity of adaptation
Languages ENGLISH Level ExcellentResearch Field Engineering » Computer engineeringEngineering » Civil engineering Years of Research Experience 1 - 4Research Field Engineering » Civil engineeringEngineering » Computer engineering Years of Research Experience 1 - 4Additional InformationBenefitsWork in a good conditions with all needed ressources to carried out your missions, benefit of all advantages offered by the university facilities :restauration and sport. possibility of partial telework depending on the period.Eligibility criteriaMaster degree or equivalentSelection processThe offer is subject to obtaining funding. It is the candidate who will defend the subject in front of a committee. It is the candidate's understanding of the subject and his or her ability to convince others that will enable him or her to obtain the funding needed to complete the doctoral thesis.Work Location(s)Number of offers available 1 Company/Institute Engees / Icube Country France City Stars GeofieldWhere to apply E-mailamir.nafi@engees.unistra.frContact CityStrasbourg WebsiteStreet1 cour des Cigarières CS 61039 Postal Code67070STATUS: EXPIRED

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