Publisert: 18.05.21

PhD position – Data Driven stochastic optimization for natural gas

There is a temporary 3 year PhD Candidate position available at the Department of Industrial Economics and Technology Management – Section Managerial Economics, Finance and Operation Research. The position is resident at NTNUs campus in Trondheim. This is an educational position, which will provide promising research recruits the opportunity for professional development through studies towards a PhD-degree. The position is connected to the PhD program at the Faculty of Economics and Management and the faculty will be your employer.

The researcher will be a part of BRU21: NTNU Research and Innovation Program on Digital and Automation Solutions for the Oil and Gas Industry (www.ntnu.edu/bru21 ) BRU21 is a PhD driven program with about 30 PhD/PostDoc projects led by 25 Professors from different departments at NTNU, as well as a number of industrial co-supervisors.

The PhD Candidates are financed either by NTNU or by an Oil and Gas Company. This specific PhD position is financed by Gassco, and the PhD Candidate will work on a use case in collaboration with Gassco.


Information about the Department of Industrial Economics and Technology Management

The department is organized into six sections:

  • Managerial Economics, Finance and Operations Research
  • Health, Safety and Environment Management
  • Strategy and Business Development
  • Operations Management
  • Experts in Teamwork
  • Section of Economics and management (Campus Gjøvik)

 

About the position

This position is in optimization and operations research. The project will develop models and solution methods for short-term optimization of natural gas pipeline transport and compressor management focusing on trade-offs between energy efficiency, CO2 emissions and economic values of flexibility. In addition to new models, this requires advanced stochastic optimization techniques and data driven optimization. Machine learning and/or scenario generation techniques will play an important role in the project.

As an increasing amount of natural gas is sold in short-term markets, also the shippers of natural gas have incentives to change directions of gas flows toward the hubs with the highest price. With a higher renewable share in the European energy markets, the flexibility in the natural gas system and the ability to adjust production in swing fields may in the future have a high commercial value. This is a service that can be provided both to shippers and buyers. On the other hand, there is an increased interest in reducing the CO2 footprint of oil and gas production, including reducing the compression power. This can be done using stochastic programming in more advanced compressor management, considering the optimal scheduling for security of supply and for commercial use.

Another aspect of the project is a combination of data driven optimization and machine learning to dynamically manage faults detection and responses. This is related to the model-based approach described above but has a higher focus on machine learning for prediction and links to model-based linepack optimization. Methodological challenges are:

  • Better representation of compressors in the optimization models and solution methods to handle these
  • Better representation of the linepack in dynamic models, both in multiperiod steady- state models and transient-based optimization models
  • Optimization of the tradeoffs between commercial use and security supply aspects of linepack including also energy efficiency considerations to reduce the CO2 footprint.
  • Prediction models based on time series and/or machine learning for both events in the network (fault situations, events) and outside (nominations)

You will report to your supervisor.


Please read more here: https://www.jobbnorge.no/ledige-stillinger/stilling/206100




Om IØT

Department of Industrial Economics and Technology Management

IØT has challenging and inspiring educational programmes and research projects in the cross-disciplinary field of technology management. Through its programmes of study, IØT educates candidates with a solid technological basis in methodology and theory on how to develop and manage technologically based organisations.

Research

Participation in, and development of, research projects in close co-operation with Norwegian industry, the department is also developing models, tools and methods for how to manage Norwegian industry.




Stillingstype
Annet
Sted
Trondheim
Søknadsfrist
06.06.21
Årstrinn
1 2 3 4 5
Vedlegg
Utlysningstekst_206100.pdf

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