REEDEAM is a project where Luleå University of Technology, Mälardalen University and Örebro University, and industry will co-produce education for the business community’s climate transition. The project aims to strengthen cooperation between universities and industry by improving access to demand-driven competence development. REEDEAM also aims to establish long-term cooperation between the universities based on their scientific areas of expertise. A planned research school will provide the business community with greater access to doctoral competence and further strengthen the universities’ cooperation with the surrounding industry and society. Lessons learned, and experiences from the KK Foundation’s expert competence program are integrated to ensure efficiency and quality by creating a cohesive competence offering.
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This course explores the integration of artificial intelligence (AI) in decision support systems specifically tailored for the energy and production sectors. Students will learn how AI technologies, such as machine learning, optimization, and data analytics, are transforming traditional operational strategies, enhancing decision-making processes, and driving efficiency in energy and production operations. The curriculum will cover foundational concepts of AI and decision support systems, along with practical applications such as predictive maintenance, demand forecasting, process optimization, and real-time decision support. Through hands-on projects, case studies, and industry-relevant examples, participants will gain insights into designing and implementing AI-driven solutions that improve operational performance, reduce costs, and support sustainability goals. By the end of this course, students will be equipped with the skills to develop and apply AI-driven decision support systems to solve complex challenges in energy and production environments. This course is ideal for professionals and students interested in leveraging AI for operational excellence in the energy and production industries. You may join the course any time between November 18 and December 9, 2024. With the recommended study pace of 25%, the course would take approximate seven calendar weeks to complete. Higher or lower study pace is possible as long as the course is finished no later than Fevruary 22, 2025.
This course is designed for engineers, scientists, operators, and managers interested in utilizing AI-based methods for condition monitoring and prognostics in industrial systems and high-value assets. Participants will learn to identify common failure causes and predict Remaining Useful Life (RUL) using historical data, involving tasks such as data processing, feature selection, model development, and uncertainty quantification. Led by experienced professionals from industry and academia, the course covers the basics of prognostics and introduces various AI methods, including deep learning. It represents state-of-the-art AI-driven prognostic techniques, advanced signal processing, and feature engineering methods. Scheduled online meetings November 11th 2024 January 15th 2025