Project description
Advancing sustainable catalysis with AI
Reducing the environmental footprint of the chemical industry is a major challenge in the transition to a more sustainable economy. Achieving this requires processes that maximise resource efficiency while improving transformation rates, selectivity, and energy use. Catalysis plays a crucial role in developing greener technologies, but accurately modelling catalytic reactions at multiple scales remains a barrier. Limited computational power has so far hindered the integration of detailed atomistic models into large-scale reactor simulations. The ERC-funded CATALYSE project addresses this challenge with MultiCAT, a cutting-edge machine learning framework that enables precise, computationally efficient catalytic process modelling. By reducing computational costs while improving predictive accuracy, CATALYSE paves the way for next-generation digital twins for process design and optimisation.
Objective
The reduction of the environmental footprint of the chemical and related industries is nowadays of utmost importance. The transition towards more sustainable processes that combine efficient use of raw material and energy with higher transformation rates, better selectivity and higher mass and energy efficiency will contribute to meet the objectives of the green deal. In this respect, catalysis engineering is pivotal to developing technologies able to meet these goals and to shape the sustainable economy of the future. The accurate description of this multiscale process has a substantial impact on the performances of the entire chemical process and, consequently, on many manufacturing sectors. The description of the catalytic process requires a detailed and accurate definition of the intrinsic reactivity, by means of first-principles kinetic schemes, coupled with rigorous models at the reactor scale. Currently, this approach is hindered by the limited available computational resources which prevent the adoption of detailed and atom-resolved kinetic models into reactor simulations with a reasonable computational burden. To overcome the limitations identified above, starting from the results obtained during the ERC Stg “SHAPE” (n. 677423), we propose MultiCAT, a highly accurate yet computationally lean multi-scale physics-guided machine learning-based surrogate modelling framework of the entire reactor from the atomistic to the process scales. This represents a leapfrog improvement in the detailed numerical modeling of catalytic processes, by achieving a drastic reduction in the computational cost with a concomitant boost in the prediction reliability, and paving the way for a new generation of catalytic process models, an evolution of hybrid digital twins, for online process design, optimization and control.
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.
- natural sciences computer and information sciences software
- natural sciences chemical sciences catalysis
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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.1.1 - European Research Council (ERC)
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-ERC-POC - HORIZON ERC Proof of Concept Grants
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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) ERC-2022-POC2
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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.
20133 Milano
Italy
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.