Project description
AI-driven process flow diagrams’ optimisation design
Improving the efficiency and resilience of the chemical and pharmaceutical industries is vital for the EU. AI-driven process flow diagrams (PFDs) designs have shown great potential for generating new process configurations. However, existing methods often require retraining for each design and fail to use relevant engineering information. Supported by the Marie Skłodowska-Curie Actions programme, the CoAUTHOR project aims to develop an AI agent that combines reinforcement learning (RL) with chemical process optimisation and property predictions. This agent will be able to design PFDs and retrofit existing ones without retraining, while considering design objectives and engineering context. The project will identify techniques for integrating chemical components and reaction properties into RL training for PFD design.
Objective
Improving the efficiency and resilience of the chemical and pharmaceutical industry is pivotal for the EU; in this frame, process flow diagram (PFD) design and retrofitting play a crucial role. Currently, it is executed by engineers leveraging their knowledge and engineering context, using a time-consuming iterative approach. On the other hand, AI-driven PFD design has proven outstanding capabilities in generating new process configurations. Despite being a promising technology, current approaches rely on training a new model when designing a process without leveraging process engineering information. Inserting such information would give the agent the optimisation context, improving the efficiency of the generated PFD and removing the need for retraining on each new design. However, such an integration requires investigating the best approach to inform the agent in the engineering context. This action wants to fill this gap by using a multidisciplinary approach combining reinforcement learning (RL) with chemical process optimisation and chemical properties predictions. The main project outcome is an AI agent able to design PFDs and retrofit existing ones without retraining while informed of the design aim and engineering context. The project will investigate the most suitable techniques for integrating chemical components and reaction properties into RL training for PFD design and retrofitting. The results of CoAUTHOR will advance our understanding of context-aware RL for chemical applications and generate an extensive toolkit for AI-driven PFD generation, paving the way toward a more sustainable and resilient chemical industry. In this action, I will combine my background in AI application in the chemical industrial domain with the expertise of the host research group in AI-driven PFD generation at TU Delft while acquiring skills in representing physicochemical properties via AI techniques during my secondment at ETH.
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.
This project has not yet been classified with EuroSciVoc.
Be the first one to suggest relevant scientific fields and help us improve our classification service
You need to log in or register to use this function
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.
-
HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
See all projects funded under this programme
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-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
See all projects funded under this funding scheme
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-2024-PF-01
See all projects funded under this callCoordinator
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.
2628 CN DELFT
Netherlands
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.