Starting from 11:00 to 11:20

Clogging in PICP and the geotextile conundrum

• Speaker - Armitage

  Armitage

  ×

  Name

  Armitage

  Company

  University of Cape town

  Country

  South Africa

  
  Biographical Sketch
  
  Originally from Zimbabwe, Neil Armitage is a registered professional engineer with the Engineering Council of South Africa (ECSA) with over 40 years’ experience – both as a consultant and an academic – in a wide range of water-related work. Currently he is an Emeritus Professor in the Department of Civil Engineering and a member of the ‘Future Water’ research institute at the University of Cape Town (UCT) in South Africa where his research focuses on Sustainable Drainage Systems (SuDS). More details may be found at: https://futurewater.uct.ac.za/ https://ebe.uct.ac.za/department-civil-engineering/prof-neil-armitage https://orcid.org/0000-0003-3704-3648 https://scholar.google.co.za/citations?view_op=list_works&hl=en&user=EIYfbegAAAAJ.

  
  All Sessions of "Armitage"
  
  

  1. Clogging in PICP and the geotextile conundrum
  2. Modelling the potential stormwater quality improvement resulting from retrofitting SuDS in a large South African urban catchment

  Close

Starting from 11:20 to 11:40

Data-driven asset management: toward the deployement of risk-based rehabilitation planning in Lausanne

• Speaker - Guericke

  Guericke

  ×

  Name

  Guericke

  Company

  Kompetenzzentrum Wasser Berlin

  Country

  Germany

  
  Biographical Sketch
  
  Lukas Guericke (M.Sc. in Civil Engineering) is working as a researcher at Kompetenzzentrum Wasser Berlin (KWB) since 2022 specializing in data-driven modelling, risk assessement and rehabilitation processes in the field of sewer asset management. Lukas has started a PhD-program at INSA Lyon focussing on his specializations in addition to his work at KWB.

  
  All Sessions of "Guericke"
  
  

  1. Data-driven asset management: toward the deployement of risk-based rehabilitation planning in Lausanne

  Close

Starting from 11:40 to 12:00

Autonomous inspection robotics for urban drainage networks

• Speaker - Shepherd

  Shepherd

  ×

  Name

  Shepherd

  Company

  University of Sheffield

  Country

  United Kingdom

  
  Biographical Sketch
  
  Dr Will Shepherd is a Research Associate with over 20 years’ experience, he is based in the Department of Civil and Structural Engineering at the University of Sheffield in the UK. His main interests lie in the performance of urban drainage systems. Recent projects include CENTAUR, which developed a fuzzy logic controlled gate to manage sewer flows to minimise flooding and Combined Sewer Overflow (CSO) spills; and CSO Analytics which uses CSO level data and rainfall radar data in order to predict the likelihood of a downstream blockage. Will's current work is mainly focussed on the Pipebots project.

  
  All Sessions of "Shepherd"
  
  

  1. Autonomous inspection robotics for urban drainage networks

  Close

Starting from 12:00 to 12:20

Development of tools and methods for the evaluation of the physical integrity of sewage networks

• Speaker - Tesfamariam

  Tesfamariam

  ×

  Name

  Tesfamariam

  Company

  University of Oulu

  Country

  Finland

  
  Biographical Sketch
  
  Axumawit Tesfamariam is a doctoral candidate at the University of Oulu in Finland. Her study focuses on developing tools to estimate infiltration and inflow in wastewater infrastructures. She is interested in applying different tracers and indicators as a tool for assessing the physical integrity of sewage networks. She has published several papers on several wastewater related topics, such as: • Suitability of natural and chemically modified peat as a sorbent material for mining water purification in small-scale pilot systems [Journal: Environmental Technology, September 2020] • Organic polyelectrolytes as the sole precipitation agent in municipal wastewater treatment [Journal: Journal of Environmental Management, October 2020] • Solids management in freshwater-recirculating aquaculture systems: Effectivity of inorganic and organic coagulants and the impact of operating parameters [Journal: Science of the Total Environment, November 2020] • Stable water isotopes as a tool for assessing groundwater infiltration in sewage networks in cold climate conditions [Journal: Journal of Environmental Management, November 2021]

  
  All Sessions of "Tesfamariam"
  
  

  1. Development of tools and methods for the evaluation of the physical integrity of sewage networks

  Close

Starting from 12:20 to 12:25

Assessment of Inspection Techniques for Rising Mains

• Speaker - Beenen

  Beenen

  ×

  Name

  Beenen

  Company

  Stichting RIONED

  Country

  Netherlands

  
  Biographical Sketch
  
  Ton Beenen has a degree in Civil Engineering. He has spent his entire professional life at Stichting RIONED. His current function is Program Manager for Research and Development.

  
  All Sessions of "Beenen"
  
  

  1. 1.36 Assessment of Inspection Techniques for Rising Mains
  2. Assessment of Inspection Techniques for Rising Mains

  Close

Starting from 12:25 to 12:30

Sewer networks asset lifespan prediction: adapting machine learning techniques to fit the purpose

• Speaker - Masoumzadeh

  Masoumzadeh

  ×

  Name

  Masoumzadeh

  Company

  Aalto University

  Country

  Finland

  
  Biographical Sketch
  
  Sina Masoumzadeh Sayyar is a doctoral researcher at Aalto University, specializing in sewer networks asset management. He holds an MSc in Civil Engineering from the University of Tabriz, Iran. Previously, Sina worked as a Surface Water Resources Specialist at the Ardabil Regional Water Company and conducted research at the Hydroinformatics Lab in the University of Mohaghegh Ardabili. His research focuses on data-driven decision-making in the water sector, with a keen interest in water and wastewater network design and management.

  
  All Sessions of "Masoumzadeh"
  
  

  1. 1.37 Sewer networks asset lifespan prediction: adapting machine learning techniques to fit the purpose
  2. Sewer networks asset lifespan prediction: adapting machine learning techniques to fit the purpose

  Close