Digitalization in the Water Sector

10 credits

Syllabus, Master's level, 1HY215

A revised version of the syllabus is available.
Code
1HY215
Education cycle
Second cycle
Main field(s) of study and in-depth level
Water Engineering A1F
Grading system
Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
Finalised by
The Faculty Board of Science and Technology, 2 March 2021
Responsible department
Department of Earth Sciences

Entry requirements

120 credits. Measurement methods in the water sector, 5 credits or Meteorology, Hydrology and Environmental Measurement Techniques, 15 credits. Computer Programming I. Distribution and treatment of drinking water, 5 credits or Municipal and industrial waste water treatment, 5 credits should have been attended. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

Upon completion of the course, the student should be able to:

  • critically reflect on the historical development and current application of digitalization in the water sector, including the strengths, weaknesses, opportunities and threats with digitalization,
  • design and critically evaluate digitalization schemes in the water sector,
  • Identify the security risks and ethical implications associated with different data transfer and storage systems,
  • analyze time series for trends, periodic signals, and outliers using statistical methods,
  • Implement and interpret the results of process-based and data-driven models for relevant water sector applications,
  • plan and conduct projects related to digitalization in the water sector.

Content

The course covers driving forces and adaptation measures for digitalization in the water sector, which encompasses drinking water and wastewater utilities, stormwater management, and the interconnection of the water, food and energy sectors. Digitalization is considered within the contexts of water quality monitoring, treatment process control, and pipe network and reservoir operations through the application of real-time monitoring, automatic control, sensors, remote sensing, and Internet of Things. The digitalization tools included in the course are machine learning, artificial intelligence, and statistical and process-based mechanistic modelling. Special attention is given to data transfer and IT security, as well as ethics related to digitalization.

The course includes project work with digitalization case studies. Project work may be integrated with other educational programs.

Instruction

Lectures, seminars, computer exercises, project work in groups. Project work with other educational programs may be included.

Assessment

Seminars and exercises (5 credits), written and oral presentation of project work (5 credits).

No reading list found.

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