EXPLAIN Project AI Transparency

ABB’s EXPLAIN Project Wins AI Innovation Award for Transparent Industrial AI

International research project recognized for making artificial intelligence transparent and reliable for process industry operations

Prestigious Recognition for AI Innovation

ABB’s Corporate Research Centers in Germany and Sweden received the ITEA Award of Excellence 2025. The ceremony took place in Portugal on September 16, 2025. The award recognizes the EU-funded EXPLAIN research project. ABB collaborated with international partners from three countries. The project developed methods to make AI transparent and reliable for process industries.

Bridging Research and Industrial Applications

ABB’s Research Centers demonstrated capabilities in managing complex AI technologies. The project highlights potential for translating advanced research into industrial solutions. By combining AI research with industrial collaboration, the project shows how explainable AI can empower operators. This approach ensures AI becomes a reliable part of daily operations. It transforms AI from technical innovation to practical decision-making tool.

Mining Industry Application

ABB worked closely with Boliden to design AI solutions for flotation processes. The collaboration targeted higher harmonization and efficiency. “EXPLAIN has delivered concrete results,” said Rasmus Tammia of Boliden. “The project demonstrated in a new way how AI can be applied in industry. We look forward to taking the insights further.”

Pulp & Paper Industry Implementation

ABB and academic partners collaborated with Södra to improve pulp quality stability. The focus remained on explainability and user-centered design. “Through EXPLAIN, Södra gained additional opportunities to apply industrial AI,” said Andreas Darnell of Södra Cell Technology. “The project highlighted the need to put people at the center for trust and robustness.”

Energy Sector Innovation

ABB developed an AI-based anomaly detection system with LEAG. The solution not only raises alarms but explains what is happening and why. This makes AI predictions more transparent for power plant operators. “The collaboration created a dynamic and highly productive environment,” said Dr. Jan Koltermann of LEAG. “We gained valuable insights into explainable AI technologies and their practical applications.”

Project Scope and Achievements

The EXPLAIN project (EXPLanatory interactive Artificial intelligence for INdustry) ran from 2022-2025. It involved 15 partners from Germany, Sweden, and the Netherlands. Results include new XAI methods, industry-tested prototypes, and over 70 scientific contributions. The project also produced a public guidebook for explainable AI in process industries.

Funding and International Collaboration

The project received funding from national agencies in all three participating countries. German support came from the Federal Ministry of Research. Sweden provided funding through Vinnova, while the Netherlands Enterprise Agency supported Dutch participation. This international collaboration was enabled through the ITEA framework.

Leadership Perspective

“We are extremely proud that our Research Centers received this recognition,” said Dr. Jan-Henning Fabian, head of ABB Research Center Germany. “This award shows the difference we can make combining world-leading technology with industry collaboration. We succeeded in showing how AI can be both understandable and useful for our customers.”

Author’s Insight: The Critical Importance of Explainable AI

The EXPLAIN project addresses one of the most significant barriers to industrial AI adoption: the “black box” problem. For control systems engineers and plant operators, understanding why an AI system makes a particular recommendation is often as important as the recommendation itself. In safety-critical process industries, unexplained AI decisions can’t be trusted. ABB’s work demonstrates that the future of industrial AI isn’t just about accuracy—it’s about transparency and collaboration between human expertise and machine intelligence. This approach could finally bridge the gap between AI’s theoretical potential and its practical implementation in environments where decisions have immediate operational and safety implications.

Application Scenario: AI-Powered Process Optimization

Challenge: A chemical plant needs to optimize reactor performance but operators distrust AI recommendations they cannot understand.
Solution: Implementation of EXPLAIN’s transparent AI system that provides reasoning behind optimization suggestions.
Outcome: Operators gain confidence in AI recommendations, leading to 15% efficiency improvement and 25% reduction in process variability through trusted human-AI collaboration.

About the Organizations

ITEA is a European cluster program for software innovation and digitalization. It strengthens Europe’s competitiveness through collaborative projects between companies, research institutes and universities.

ABB is a global technology leader in electrification and automation. The company enables sustainable and resource-efficient futures for industries worldwide. ABB has over 140 years of history and approximately 110,000 employees.

Frequently Asked Questions (FAQs)

What is the EXPLAIN project?
EXPLAIN is an international research project developing explainable AI methods for process industries, making AI systems transparent and understandable for operators.

Which industries benefited from the EXPLAIN project?
The project delivered practical applications in mining, pulp & paper, and energy sectors, with potential for broader industrial adoption.

What makes explainable AI different from conventional AI?
Explainable AI not only provides recommendations but also explains the reasoning behind decisions, building trust and enabling human-AI collaboration.

How long did the EXPLAIN project run?
The project duration was from 2022 to 2025, involving 15 partners across Germany, Sweden, and the Netherlands.

What practical outcomes resulted from the project?
The project produced new XAI methods, industry-tested prototypes, over 70 scientific contributions, and a public guidebook for explainable AI implementation.