s-X-AIPI | self-X Artificial Intelligence for European Process Industry digital transformation

Summary

The overall objective of s-X-AIPI is to research, develop, test and experiment an innovative toolset of custom trustworthy self-X AI technologies (autonomous AI that minimizes human involvement in the loop an exhibit self-improving abilities). AI applications will help workers to deal with external and internal influences and enable agile and resilient reaction of European process industry processes and products' lifecycle for a true integration into the circular manufacturing economy ecosystem.

The aim is to provide existing process industries and its workers with agility of operation, improvement of performance across different indicators and state of the art AI-based sustainability tools for the design, development, engineering, operation and monitoring of their plants, products and value chains.

Demonstration at four representative industrial use cases (asphalt, steel, aluminium and pharmaceutics) will generate a showcase portfolio of trustworthy AI technologies (data sets, AI model and applications) integrated into an innovative open source toolset available for industry and research as an example of self-X AI technologies integrated in actual process industries? value chains.

s-X-AIPI toolset of AI technologies will include an innovative AI data pipeline with autonomic computing capabilities (self-X AI and autonomic manager), architecture, realistic datasets together with their respective algorithms derived from the demonstration in four realistic use cases of process industry. s-X-AIPI technologies will consider workesr' heterogeneous skill levels and self-adaptation capabilities to the actual profile of the worker respecting their human-in-the-loop role.

s-X-AIPI will be performed by an interdisciplinary consortium (AI integration and Big Data analytics, use case process understanding, modelling and digital platforms, research, industry, SME [4 companies, 1 industrial], communication, exploitation, standardisation).

More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/101058715
Start date: 01-05-2022
End date: 30-04-2025
Total budget - Public funding: 5 986 540,00 Euro - 5 351 440,00 Euro
Twitter: @s_X_AIPIProject
Cordis data

Original description

The overall objective of s-X-AIPI is to research, develop, test and experiment an innovative toolset of custom trustworthy self-X AI technologies (autonomous AI that minimizes human involvement in the loop an exhibit self-improving abilities). AI applications will help workers to deal with external and internal influences and enable agile and resilient reaction of European process industry processes and products? lifecycle for a true integration into the circular manufacturing economy ecosystem.
The aim is to provide existing process industries and its workers with agility of operation, improvement of performance across different indicators and state of the art AI-based sustainability tools for the design, development, engineering, operation and monitoring of their plants, products and value chains.
Demonstration at four representative industrial use cases (asphalt, steel, aluminium and pharmaceutics) will generate a showcase portfolio of trustworthy AI technologies (data sets, AI model and applications) integrated into an innovative open source toolset available for industry and research as an example of self-X AI technologies integrated in actual process industries? value chains.
s-X-AIPI toolset of AI technologies will include an innovative AI data pipeline with autonomic computing capabilities (self-X AI and autonomic manager), architecture, realistic datasets together with their respective algorithms derived from the demonstration in four realistic use cases of process industry. s-X-AIPI technologies will consider workers? heterogeneous skill levels and self-adaptation capabilities to the actual profile of the worker respecting their human-in-the-loop role.
s-X-AIPI will be performed by an interdisciplinary consortium (AI integration and Big Data analytics, use case process understanding, modelling and digital platforms, research, industry, SME [4 companies, 1 industrial], communication, exploitation, standardisation).

Status

SIGNED

Call topic

HORIZON-CL4-2021-TWIN-TRANSITION-01-07

Update Date

27-10-2022
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Result items:

The optimization of the use of resources during the selection of the aluminium recipes will be at the core of the IDSS with the aim of optimizing the overall process, reducing waste, and reducing polluting emissions.

To improve the efficiency of the whole value chain, AI apps will be used in predictive diagnosis of equipment efficiency to infer energy savings in production plant and maintenance optimization.