Periodic Reporting for period 2 - s-X-AIPI (self-X Artificial Intelligence for European Process Industry digital transformation)
Berichtszeitraum: 2023-11-01 bis 2025-04-30
s-X-AIPI is a Horizon Europe project (2022–2025) involving 14 partners from 6 countries, aimed at enhancing trustworthy AI deployment in the European process industry. The project focused on developing self-X AI technologies to minimize human intervention, boost operational agility, and enable smarter, more autonomous decision-making through AI. These self-X capabilities, such as self-optimization, have been implemented via a modular AI pipeline equipped with an Autonomic Manager (AM) based on the MAPE-K control loop.
The project addressed pressing industrial needs: improving productivity, reducing scrap and downtime, shortening production cycles, and lowering CO2 emissions. It contributes to Europe’s digital transition and green goals by fostering circular manufacturing, responsible data use, and safe human-AI collaboration.
During its second reporting period, the project successfully demonstrated its solutions in four industrial pilots: Steel, Asphalt, Pharmaceutical and Aluminium.
The self-X AI pipeline and Autonomic Manager were validated and released as open-source components, with standardized architectures and guidelines (CWA 18211:2025) now available to promote adoption and interoperability. s-X-AIPI has shown replicability across sectors, offering a path to AI maturity in process industries, aligned with EU values of trustworthiness, safety, and human-centricity.
The project addressed pressing industrial needs: improving productivity, reducing scrap and downtime, shortening production cycles, and lowering CO2 emissions. It contributes to Europe’s digital transition and green goals by fostering circular manufacturing, responsible data use, and safe human-AI collaboration.
During its second reporting period, the project successfully demonstrated its solutions in four industrial pilots: Steel, Asphalt, Pharmaceutical and Aluminium.
The self-X AI pipeline and Autonomic Manager were validated and released as open-source components, with standardized architectures and guidelines (CWA 18211:2025) now available to promote adoption and interoperability. s-X-AIPI has shown replicability across sectors, offering a path to AI maturity in process industries, aligned with EU values of trustworthiness, safety, and human-centricity.
During the second reporting period (M19–M36), the s-X-AIPI project successfully completed the development, deployment, and validation of its core innovations. Key achievements include:
Completion and deployment of the s-X-AIPI Autonomic Manager (AM), integrating self-X abilities into AI pipelines and coordinating autonomous MAPE-K-based decision-making across diverse industrial scenarios (D4.4).
Execution of four industrial pilots, demonstrating AI-driven optimization for: Steel Asphalt Pharma Aluminium
Validation of the Self-X AI pipeline and data infrastructure under both Data-in-Motion and Data-at-Rest conditions (D3.3 D4.2) with human-in-the-loop capabilities enabling transparent and safe interaction.
Finalization and open-source release of the toolset in two major versions (D6.2 D6.3) providing reusable, modular AI pipeline components and autonomic decision layers.
Creation of the s-X-AIPI AI Maturity Model and Trustworthiness Guidelines (D2.3 D2.4) adapted to the process industry based on real feedback from pilots.
Standardization outcome: Publication of CWA 18211:2025, defining a reference architecture for self-X AI in the process industry, through active contribution to CEN/CENELEC workshops (D7.7).
Exploitation planning and market alignment: Completion of the final Exploitation & Business Plan, including KER strategies, stakeholder outreach, and market transferability assessment (D6.7 D6.5).
Training & dissemination: Extensive capacity building, training activities, and documentation of impact pathways, supported by D6.4 and the updated Communication and Dissemination Plans (D7.4 D7.5).
Ethics compliance: Continued monitoring by the Ethics Advisor and Ethics Manager, as documented in D8.1.
The project achieved its technical and scientific objectives, validated the robustness and flexibility of the proposed approach across different industrial domains, and demonstrated its potential for replicability in broader EU industrial contexts.
Completion and deployment of the s-X-AIPI Autonomic Manager (AM), integrating self-X abilities into AI pipelines and coordinating autonomous MAPE-K-based decision-making across diverse industrial scenarios (D4.4).
Execution of four industrial pilots, demonstrating AI-driven optimization for: Steel Asphalt Pharma Aluminium
Validation of the Self-X AI pipeline and data infrastructure under both Data-in-Motion and Data-at-Rest conditions (D3.3 D4.2) with human-in-the-loop capabilities enabling transparent and safe interaction.
Finalization and open-source release of the toolset in two major versions (D6.2 D6.3) providing reusable, modular AI pipeline components and autonomic decision layers.
Creation of the s-X-AIPI AI Maturity Model and Trustworthiness Guidelines (D2.3 D2.4) adapted to the process industry based on real feedback from pilots.
Standardization outcome: Publication of CWA 18211:2025, defining a reference architecture for self-X AI in the process industry, through active contribution to CEN/CENELEC workshops (D7.7).
Exploitation planning and market alignment: Completion of the final Exploitation & Business Plan, including KER strategies, stakeholder outreach, and market transferability assessment (D6.7 D6.5).
Training & dissemination: Extensive capacity building, training activities, and documentation of impact pathways, supported by D6.4 and the updated Communication and Dissemination Plans (D7.4 D7.5).
Ethics compliance: Continued monitoring by the Ethics Advisor and Ethics Manager, as documented in D8.1.
The project achieved its technical and scientific objectives, validated the robustness and flexibility of the proposed approach across different industrial domains, and demonstrated its potential for replicability in broader EU industrial contexts.
The s-X-AIPI project has successfully delivered several results that go significantly beyond the state of the art in industrial AI systems:
Deployment of the s-X-AIPI Autonomic Manager (AM) with validated self-X abilities, including self-optimization. These were implemented using a domain-agnostic reference architecture based on MAPE-K, applicable across multiple industrial settings.
Integration of self-X AI into real-world industrial pipelines, enabling the dynamic adaptation of AI components to unforeseen situations in four use cases (steel, asphalt, pharma, aluminium). The AM coordinates decision-making, monitors system behavior, and triggers corrective actions without manual intervention.
Execution of full pilot-scale validations, demonstrating how AI systems can autonomously optimize quality, efficiency, or circularity. For example:
Predictive adjustments to steel production processes (resilient high-end quality).
Asphalt formulation adapting to recycled material composition in real-time.
AI-assisted root-cause analysis and parameter prediction in pharma.
Automated mix design and sorting logic for recycled aluminium.
Formalization and release of the Reference Architecture in CWA 18211:2025, supporting replicability and transferability beyond the project. This standard sets a benchmark for the integration of AI pipelines with self-management capabilities in industrial environments.
Open-source release of key technological components via GitHub, promoting uptake by the broader innovation ecosystem. Components include orchestration pipelines, metadata layers, self-X function blocks, and modular connectors to legacy OT/IT systems.
Maturity Model and ALTAI-based Trustworthiness Assessment Tools, adapted to industrial users, guiding companies in evaluating their readiness and progress in deploying AI responsibly.
Exploitation and business readiness: Key Exploitable Results (KERs) were identified and assessed for market potential using the AIDA model and validated with the Horizon Results Booster. Early adopter interest was registered in sectors beyond the project pilots.
Scientific and standardisation impact: Contributions to CEN/CENELEC and clustering with sister Horizon Europe projects reinforced the project’s external visibility and influence.
These results set the foundation for more autonomous, adaptive, and human-aware AI systems in Europe’s industrial future, fostering innovation with safety, trust, and replicability at its core.
Deployment of the s-X-AIPI Autonomic Manager (AM) with validated self-X abilities, including self-optimization. These were implemented using a domain-agnostic reference architecture based on MAPE-K, applicable across multiple industrial settings.
Integration of self-X AI into real-world industrial pipelines, enabling the dynamic adaptation of AI components to unforeseen situations in four use cases (steel, asphalt, pharma, aluminium). The AM coordinates decision-making, monitors system behavior, and triggers corrective actions without manual intervention.
Execution of full pilot-scale validations, demonstrating how AI systems can autonomously optimize quality, efficiency, or circularity. For example:
Predictive adjustments to steel production processes (resilient high-end quality).
Asphalt formulation adapting to recycled material composition in real-time.
AI-assisted root-cause analysis and parameter prediction in pharma.
Automated mix design and sorting logic for recycled aluminium.
Formalization and release of the Reference Architecture in CWA 18211:2025, supporting replicability and transferability beyond the project. This standard sets a benchmark for the integration of AI pipelines with self-management capabilities in industrial environments.
Open-source release of key technological components via GitHub, promoting uptake by the broader innovation ecosystem. Components include orchestration pipelines, metadata layers, self-X function blocks, and modular connectors to legacy OT/IT systems.
Maturity Model and ALTAI-based Trustworthiness Assessment Tools, adapted to industrial users, guiding companies in evaluating their readiness and progress in deploying AI responsibly.
Exploitation and business readiness: Key Exploitable Results (KERs) were identified and assessed for market potential using the AIDA model and validated with the Horizon Results Booster. Early adopter interest was registered in sectors beyond the project pilots.
Scientific and standardisation impact: Contributions to CEN/CENELEC and clustering with sister Horizon Europe projects reinforced the project’s external visibility and influence.
These results set the foundation for more autonomous, adaptive, and human-aware AI systems in Europe’s industrial future, fostering innovation with safety, trust, and replicability at its core.