The s-X-AIPI project has been dedicated to advancing Artificial Intelligence solutions to enhance automation, efficiency, and sustainability in the European process industry. By integrating self-X AI capabilities, the project has delivered AI-driven tools that support human operators in decision-making, reduce waste, and improve production processes. With successful industry use cases in steel, asphalt, pharmaceuticals, and aluminum, s-X-AIPI has demonstrated how AI can lead to smarter, more adaptive, and more sustainable manufacturing operations.

The Autonomic Manager in the s-X-AIPI project ensures AI system stability through self-X abilities like self-configuration, self-optimization, and self-healing, enabling dynamic adaptation across industrial contexts. A key innovation is its capability of implementing outer control loops, which optimize AI behavior based on business objectives. For instance, its self-healing features detect failures and trigger AI model retraining to minimize downtime and prevent production losses. Designed for multiple industries, including asphalt, steel, pharma, and aluminum, the Autonomic Manager aims at maintaining consistent performance and stability across diverse AI solutions and use cases, ensuring alignment between architecture, AI maturity, and business objectives.

In s-X-AIPI, the steel use case aims to increase the efficiency of electric steelmaking, targeting at greener production through optimized scrap mixing and circulation. The developed scheme is not only efficient but also emphasizes the importance of human skills, involving the operators and a data scientist. The whole is supported by an external web service that exploits Artificial Intelligence to notify the plant about sub-optimal operation. The optimization system is resilient thanks to its Self-X capabilities enabling self-detection, self-evaluation, and self-repair. The responsible project partners are the plant operator Sidenor Aceros Especiales from Spain, the information system integrator MSI (Mondragon Sistemas de Información) from Spain as well, and the research institute BFI (VDEh-Betriebsforschungsinstitut) from Germany. Complementing this, CORE from Greece has developed an AI-driven anomaly detection software that incorporates active learning with a Human-in-the-Loop (HITL) component, enabling continuous improvement through user feedback.

The asphalt use case in s-X-AIPI focuses on optimizing the entire asphalt value chain, from raw material selection to road paving, using AI-driven capabilities. The system improves quality control, predictive maintenance, and sustainability by integrating real-time sensor data, AI-based decision-making, and human-in-the-loop interactions. The developed solution enables self-detection of anomalies, self-optimization of asphalt mix designs, and self-healing capabilities for predictive maintenance. Key partners include EIFFAGE, a leader in infrastructure and road construction, DEUSER, an engineering AI solutions provider, and CARTIF, responsible for the AI-driven framework and integration. CORE has implemented advanced data augmentation techniques to enhance model performance.

As part of the s-X-AIPI Project, the Aluminium use case focuses on optimizing resource utilization through the development of an Intelligent Decision Support System (IDSS) that drives sustainability within the industry. This system enhances operators' decision-making and process understanding through a suite of digital services integrated into a user-friendly web application. Featuring interactive tables, advanced visualization charts, KPIs dashboards, and anomaly detection mechanisms, the IDSS provides operators with valuable operational insights. Additionally, it leverages Machine Learning (ML) Regressors for precise composition prediction and Generative models to design optimal material formulations based on user requirements, balancing resource efficiency, cost, and performance. Driven by self-optimizing capabilities, the framework also ensures continuous improvement of its intelligent solutions over time. The responsible project partners are IDALSA (Ibérica de Aleaciones Ligeras), a leading producer of secondary casting aluminium alloys, and AIMEN Technology Center, responsible for the development of the IDSS, digital services, and AI-driven solutions.

In this session, MSI will present the Key Exploitable Results (KERs) of the s-X-AIPI project, highlighting the main achievements and practical applications developed. The discussion will focus on how these results can be leveraged by the industry, their impact on digitalization and process optimization, and opportunities for future exploitation. Additionally, we will explore the potential for replicating these solutions across different industrial sectors to maximize their impact and scalability.