Martí-Franquès COFUND Fellowship Programme


Details

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Reference:

2021MFP-COFUND-1

Area:

Engineering and Architecture

Supervisor name and surname:

Laureano Jiménez Esteller

Supervisor email:

laureano.jimenez@urv.cat

Supervisor short biography

PhD programme:

Nanoscience, Materials and Chemical Engineering

Title of the research project:

Techno-economic and environmental assessments of existing and emerging processes and technologies

Description of the research project:

OVERVIEW
This research project focuses on applying mathematical tools (mixed-integer linear and nonlinear programming) to sustainability problems arising in the water-energy-food nexus, with emphasis on energy systems modelling. The candidate will develop multi-objective mathematical models embedding life-cycle assessment principles to solve a variety of problems, with emphasis on the optimisation of energy mixes and the optimal allocation considering multiple (and often conflicting) objectives.


KEY TASKS
- Perform techno-economic and environmental assessments of existing and emerging processes and technologies.
- Develop mathematical programming models embedding economic, environmental and social criteria to address different problems, mostly focused on the best exploitation of biomass resources and the optimal design of sustainable energy systems.
PROJECT CONTRIBUTION AND METHODOLOGY
The goal of this research project is to promote green production by developing systematic tools for the optimal design, optimization and planning of more sustainable processes.
To achieve these goals, we will use multi-objective stochastic programming tools combined with life cycle assessment methods for multi-criteria problems. Stochastic programming will assist decision-makers in the face of uncertainty. Life cycle assessment tools will be employed to assess process alternatives from an environmental perspective.
We will apply these tools to different case studies that have attracted an increasing interest in the recent past. Particularly, we will focus on applications in different sectors, highlighting the advantages of the proposed methods as compared to traditional process design tools.

THE IDEAL CANDIDATE
- Hold a bachelor/master degree (or equivalent), in chemical engineeering, computer science, mathematics or similar.
- Language skills: English is the working language of the international research group (https://etseq.urv.cat/suscape/)
- Other skills: we wish to recruit a motivated and talented fellow to undertake original research in the area of sustainable decsion making in a very multidisciplinar and interdisciplinar topic.
- Knowledge of renewable energy and impact assessment methodologies is desirable, but not a requisite.
- Good programming skills are valuable (GAMS, Phyton, Matlab, or similar), but not a requisite.

BENEFITS AT THE END OF THE PhD
- Skills: multivariate statistical analysis, mathematical programming (LP, NLP, MILP, MINLP), multiobjective optimization, DEA, multicriteria decision making, machine learning, life-cycle assessment.
- State-of-the art modelling & optimization tools, including process simulation (e.g., gPROMS...); life cycle assessment and related software packages (e.g., SimaPro, Ecoinvent...); algebraic modelling systems (e.g., GAMS, Matlab...), optimization solvers (e.g., CPLEX, DICOPT, SBB...) and machine learning algorithms (e.g., artificial neural networks, support vector machine...). programming in several tools (GAMS, Matlab...).
- Personality traits: The PhD project is embedded into a larger, interdisciplinary research effort with international collaborations. There are ongoing collaborations with international research centres that will be used to validate the modelling framework.
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Ethics: This project does not involve ethical aspects.

Workplace location: Campus Sescelades, Tarragona

Gross anual salary:

27103.20 €

Dedication:

Full time

Working hours:

37.5 hours a week

Earliest expected start date:

04 May 2022

European union This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 945413