Mohammad Moataz Tolba

• Since 11/2023:
  Ph.D. Student at the Pattern Recognition Lab, Diehl Metering GmbH
• 12/2020 – 10/2023:
  Researcher at the Chair of Fluid Mechanics (LSTM),
  Friedrich-Alexander University Erlangen-Nürnberg
• 09/2017 – 11/2020:
  M.Sc. In Computational Engineering
  Friedrich-Alexander University Erlangen-Nürnberg
• 09/2009 – 07/2010:
  M.Sc. In Fluid Mechanics
  Cairo University, Egypt
• 09/2003 – 07/2008:
  B.Sc. In Mechanical Power Engineering
  Cairo University, Egypt

2023

• AI-refined thermo-hydraulic model for the improvement of the efficiency and quality of water supply

  (Third Party Funds Single)

  Term: November 1, 2023 - October 31, 2026
  Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
  Abstract

  The United Nations' goals for sustainable development have made improving quality of life and access to clean drinking water a political priority. However, in recent decades, the water cycle in Bavaria has also been significantly affected by climate change. Two important aspects of daily drinking water supply and distribution are the assurance of water quality and the increase in usage efficiency. To enhance the resilience and capacity of the water supply in general, numerical simulation, data integration, and artificial intelligence (AI) are necessary. In this project, we aim to develop an AI-refined temperature-hydraulic model using heterogeneous data sources from a Bavarian water supply network. Hybrid AI methods are employed to model the complex relationship between water and soil temperature. The resulting model will serve as the basis for various real applications such as leak detection, anomaly recognition, and monitoring of drinking water quality, with the overarching goal of increasing the efficiency and quality of the water supply while simultaneously contributing to the containment of the impact of climate change on drinking water supply

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