4 The optimal transition process to the future
Internship or Master thesis
Summer 2023
IPESE, EPFL, Sion, Switzerland.
4.1 Context
The industry today is required to transit to the future one in response to the climate change. During the transition process, different investment decisions are going to be made to phase out the outdated technologies, resize the existing technologies, and introduce the novel technologies. When and how to make those decisions will have a profound impact to the cost and environmental impacts. For instance, the prompt investment decision may lead to to a high total cost but low carbon emission over the transition period, vice versa. Therefore, a systematic way to provide guidance to the decision maker is essential to be addressed.
4.2 Problem statement
You will investigate the transition process of the refinery today to the future one. A multi-period model based on a mixed integer linear programming (MILP) method will be used to capture the key features of the transition process. Different technology options will be considered such as power-to-X, biorefinery and carbon capture and storage. When and how to integrate these new options into a conventional oil refinery will be studied based on the multi-objective optimization framework to balance the trade-off between carbon footprints and cost. The detailed tasks include:
Be familiar with the existing oil refinery model and understand the technologies to decarbonize the refining industry.
Establish database for different decarbonization technologies including their cost, carbon footprints and energy profiles.
Apply MILP method to generate the transition pathway to the future.
Build KPI matrix to compare different transition pathways systematically.
4.3 For candidates
The successful candidate is expected to have the following profiles:
High-level motivation and ability to work in an autonomous environment.
A concrete backgound in chemical engineering or process engineering is perferred but not a
Coding skills (R and Lua) are a plus.
System thinker and problem-solver oriented.
The candidate will develop different competencies, such as project management experience, technical knowledge with renewables and mathematical optimizations, and critical thinking skills, etc.
4.4 Supervising
This project will be supervised by Prof. François Maréchal from EPFL. Yi Zhao will serve as the assistant of the project.
If interested, please take contact with Yi Zhao (yi.zhao@epfl.ch) attaching your CV, Cover Letter and transcript of records (Bachelor’s and Master’s). Short-listed candidates will be interviewed. Early applications are encouraged.
4.5 Practical information
The IPESE group is located in Sion (EPFL Valais) at about 1h05 from Lausanne train station. The successful candidate will receive financial supports for this projects.
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