Exploitable Results
Propelling the digital transformation of the industry to new heights
See how s-X-AIPI empowers industry to push the boundaries, and to solve the toughest problems through AI-driven technologies.
Discover the key exploitable results that the project has delivered.
RES1
Toolset of self-X AI technologies
An open-source collection of datasets, components, and AI procedures that allow for the creation of AI applications with extraordinary self-X abilities inspired by the autonomic computing paradigm.
All Partners
Target markets
Steel, asphalt, pharma, and aluminium, with potential to extend into cement concrete, glass, copper, and fertilizers.
RES2
Application of AI Toolset to EAF Steelmaking Use Case
AI global ecosystem infrastructure which integrates self-X abilities for decision making in EAF melting of scrap. This new application reports to the human in the loop about deviations in scrap properties and suggests adaptation of material parameters for charge mix optimization and process control models.
IPR
50 %
IPR
20 %
IPR
30 %
Target markets
Steel with potential to extend into other sectors such as copper plants.
RES3
Application of toolset to Asphalt - Paving the Way for AI in Asphalt Value Chain (PAWAIA)
An AI application for integrating value chain information from quality laboratories, production and paving logistics and improve overall sustainable performance of the process by including human feedback for recipe adaptation, maintenance actions and quality monitoring.
IPR
33 %
IPR
33 %
IPR
33 %
Target markets
Asphalt plants - This technology, however, can also be replicated in cement and cement concrete plants, aggregates crushing plants and logistics sectors with similar requirements.
RES4
Application of toolset to Pharma
AI modeling of chemical flow synthesis process for dynamic adjustment to establish optimal process settings. Trustworthy AI fostering self-X autonomic computing with the human-in-the-loop as control instance.
IPR
100 %
Target markets
Pharma companies and chemical toll manufacturing companies. The prospective verticals include green hydrogen plants, ammonia fertilizer plants, petrochemicals plants doing liquid processing, chloralkali plants, and others.
RES5
Application of toolset to Aluminum
An AI application for integrating value chain information from quality laboratories, production and paving logistics and improve overall sustainable performance of the process by including human feedback for recipe adaptation, maintenance actions and quality monitoring.
AI algorithm
100 %
Product IPR
50 %
Product IPR
50 %
Target markets
Aluminum plants. Replicability can be achieved in scrap melting facilities like copper scrap plants, zinc plants, iron casting plants, electronic waste processing, ceramic materials, lead recycling plants, WEEE electric, and electronic equipment waste processing.
RES6
AI Continuous Integration Framework for Process Industry
This framework uses a flexible Reference Architecture to facilitate the implementation and integration of components in AI systems for the process industry, featuring autonomous management that continuously adapts and improves AI operations for various use cases.
Target markets
Process industry (PI) companies including pharma, steel, asphalt, and aluminum. Replicability can be achieved by AI service providers, systems integrators and consultants in PI, hardware manufacturers of sensors and components, academic and industrial R&D researchers, and EHS officers, among others.
RES7
Guidelines for trustworthy AI in process industry
Guidelines to complement and adapt trustworthy EC guidelines (taking into account their “Trustworthy AI assessment list”) to tailor them to process industry specifics, mainly based on practical experiences gained through the implementation of the use cases. A special focus on the human oversight roles is also considered taking as basis the ones defined in the Ethics Guidelines for Trustworthy AI3.
IPR
100 %
Target markets
Process planning providers, quality control system integrators, operation designers and developers of AI applications for manufacturing. Replicability extends to data scientists, AI scientists, IP lawyers, AI service providers, process engineers/developers, occupational hazard agency auditors, and operator trainers.
RES8
AI Maturity Model for Process Industry
An AI maturity model related to those technologies favouring the self-X capabilities of AI applications, enriched with consideration of human factors, especially user acceptance and user experience.
IPR
100 %
Target markets
SCADA and control providers, system integrators, and system engineers. Replicability extends to monitoring service providers, IoT end-to-end solution providers, system engineering academics, and control systems research groups.
RES9
Self-X AM Suite
An autonomous coordinator within the framework that manages and adapts AI data flows according to the specific needs of each use case, using self-X AI components and human-machine interaction principles to gradually enhance system performance and efficiency.
Target markets
Process Industry companies including pharma, steel, asphalt, and aluminum). Replicability extends to AI and data scientists, IT and operations teams, technology vendors and providers, regulatory bodies, and industry specific end