Imagine a factory that can fix its own mistakes and tune its settings without a human needing to flip every switch. This project creates a smart brain for industrial plants that learns from data to keep things running smoothly. It acts like an autopilot for heavy industry, making sure production stays efficient and green.
Process industries struggle with high downtime, excessive scrap, and long production cycles. They lack AI tools that can autonomously adapt to changes without requiring constant human manual adjustment.
An open-source toolset featuring a modular AI pipeline and an Autonomic Manager based on the MAPE-K control loop. It includes an AI Maturity Model and Trustworthiness Guidelines for the process industry.
If you are a steel mill operator dealing with high scrap rates and downtime — this project developed a self-X AI pipeline that optimizes production cycles. This reduces material waste and improves operational agility.
If you are a drug manufacturer dealing with strict quality requirements and complex production cycles — this project developed an Autonomic Manager that enables smarter decision-making. This ensures safer human-AI collaboration and more consistent product quality.
If you are a smelting plant dealing with high CO2 emissions and energy costs — this project developed sustainability tools for plant monitoring. This helps lower emissions and integrates the plant into a circular manufacturing economy.
Based on available project data, the toolset has been released as open-source components, meaning the core software is available without a direct purchase price.
Yes, the solution was validated through four industrial pilots in the steel, asphalt, pharmaceutical, and aluminium sectors.
The project released the self-X AI pipeline and Autonomic Manager as open-source components and contributed to the CWA 18211:2025 standardization.
It uses a modular AI pipeline and a MAPE-K control loop to coordinate decision-making across diverse industrial scenarios.
The project ran from 2022-05-01 to 2025-04-30, with the second reporting period focusing on the final deployment and validation of the toolset.
The consortium is heavily weighted toward practical application, with 7 industry partners and 4 SMEs, resulting in a 47% industry ratio. This balance, combined with 6 research organizations and 1 university across 6 countries, ensures that the AI tools are grounded in real-world process industry needs rather than just theoretical research.
Contact FUNDACION CARTIF in Spain for technical specifications on the open-source toolset.
Contact us to find the open-source s-X-AIPI components for your plant.
