Project description
High-performance recycling of polypropylene and polyethylene waste
Management of plastic waste is one of the biggest environmental challenges worldwide. Most plastic waste goes either to landfills or directly into the environment because of ineffective recycling and inferior product performance. Supported by the Marie Skłodowska-Curie Actions programme, the OPTIRBLEND project seeks to design a framework based on data analytics for enhanced recycling and improved performance of mixed polypropylene (PP) and polyethylene (PE) plastic waste products. Using advanced manufacturing experimentation, machine learning algorithms, finite element modelling and optimisation methods, the study will find optimal parameters for processing mixed PP/PE plastic waste materials to make them stronger and more durable. The project’s goal is to offer tools for prediction and manufacturing of recyclable plastics that can be reused in high-performance industries like transport and construction.
Objective
Plastic waste is one of the most pressing global challenges, with more than 350 million metric tons generated annually and less than 10% effectively recycled. Polypropylene (PP) and Polyethylene (PE) account for nearly 50% of global plastic production and are often collected as mixed waste streams. Their immiscibility, however, leads to poor mechanical performance, preventing their use in high-value applications. Overcoming this limitation requires new methodologies to optimize manufacturing parameters and microstructures to maximize mechanical performance and reduce their intrinsic variability.
This project proposes a data-driven framework for the design and optimization of recycled PP/PE blends. The methodology integrates:
1. A manufacturing campaign with systematic variation of processing parameters (blend composition, compounding time, temperatures, screw speed, cooling rate, etc.), combined with morphological characterization.
2. Generative machine learning to model and reproduce microstructures conditioned on manufacturing parameters.
3. A multi-fidelity model based on finite element analyses to predict yield stress, ultimate strength, and toughness while quantifying epistemic and aleatoric uncertainties.
4. Bayesian active learning and multi-objective optimization to identify optimal manufacturing configurations.
The framework will be validated through an experimental testing campaign. Expected outcomes include a fundamental understanding of process–microstructure–property relationships in recycled PP/PE blends, efficient predictive tools, and manufacturing guidelines. The project will directly support the circular economy by enabling recycled polymers to be used in high-demand sectors (e.g. transportation, construction), strengthening Europe’s competitiveness while reducing environmental impacts.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- engineering and technology materials engineering composites
- natural sciences chemical sciences polymer sciences
- social sciences economics and business economics sustainable economy
- natural sciences computer and information sciences artificial intelligence machine learning
- social sciences educational sciences pedagogy active learning
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2025-PF
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
4099-002 Porto
Portugal
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.