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MARTÍ I FRANQUÈS MSCA-COFUND DP. CALL FOR 1 PHD CANDIDATE WITHIN THE PROJECT "OPTIMAL DESIGN AND OPTIMIZATION OF SUSTAINABLE CHEMICAL PROCESSES"

Descripción de la oferta de empleo

Overview The Martí i Franquès COFUND Doctoral Fellowships Programme (MFP) is a redesign of the existing MF programme, offering 100 doctoral contracts (in four editions.
and ) at the Universitat Rovira i Virgili (URV).
The programme is uniquely shaped to offer the best training stemming from the "triple i" principles of the Marie Sklodowska-Curie Actions.
international, interdisciplinary and intersectoral.
In order to achieve these goals, we combine leading research groups at URV with scientific partners from world-class institutions, such that the candidates are be exposed to interdisciplinary training as well as mentoring from the industrial sector.
Through MFP, URV is in a unique position to offer the best conditions for doctoral training, based on the principles of the European Charter for Researchers and the Code of Conduct for the Recruitment of Researchers (guaranteed by the HR award that URV has received in ), as well as the EU Principles for Innovative Doctorate Training.
Description of the research project (reference.
MFP-COFUND-21) 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.
Specifically, we will investigate the combined use of mathematical programming, process simulation and life cycle assessment principles to automatically generate the best process alternatives to achieve environmental improvements.
There are ongoing collaborations with leading chemical companies and international research centers that will be used to validate the modeling framework.
Background and state of the art.
This research project will focus on developing novel (global) optimization techniques for the solution of linear/nonlinear and mixed integer nonlinear programming problems (MILP or MINLP) arising in most real case applications.
From the algorithmic point of view, the objective is to investigate efficient solution methods (e.
., outer approximation) because standard methods often fail to identify the global optimum in the presence of multiple local optima.
This project will develop decision-making tools based on the combined use of multi-objective optimization, life cycle assessment and stochastic programming for the design and planning of sustainable processes under uncertainty.
To achieve these goals, we will use multi-objective stochastic programming tools combined with life cycle assessment and dimensionality reduction 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.
Finally, objective reduction techniques based on multivariate statistics will allow for the identification of redundant environmental objectives.
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.
Project contribution and methodology.
The main novelties of the project compared to other contributions presented so far in the literature are.
The explicit treatment of different sources of uncertainty that affect the environmental impact calculations.
The systematic analysis of the mathematical structure of multi-objective problems arising in environmental engineering, which will allow us to identify redundant environmental metrics that can be omitted.
The development of algorithms to expedite the search for optimal solutions in the environmental impact minimization.
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Detalles de la oferta

Empresa
  • Universitat Rovira i Virgili
Localidad
Dirección
  • Sin especificar - Sin especificar
Salario
  • Sin especificar
Fecha de publicación
  • 08/10/2020
Fecha de expiración
  • 06/01/2021
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