Sustainable energy-efficient production
Sustainable production in manufacturing can be defined as the creation of products through economically sound processes that minimize negative environmental impacts while minimizing the use of energy and natural resources. Research in this area is centered around: 1) Energy versatility and flexibility to reduce peak electricity demand 2) Predictive analytics to reduce energy consumption and waste 3) Smart systems that use data from the Industrial Internet of Things (IIoT), artificial intelligence (AI), and machine learning (ML) to quickly detect process variations. (Also important for sustainable manufacturing are renewable materials and recycling, which are handled in FA Sustainable Materials).
Key challenges:
- Implement the use of computer control of pulp and paper production.
- Use interaction modeling during production (of filters, heat pumps, paper, etc).
- Optimize the end use of products through smart control according to selected control parameters, enabling flexible and sustainable interaction with renewable energy sources.
- Digital twin-based process control optimization for the production line using Deep Reinforcement Learning (DRL) to reduce variation in the production process.
- Use advanced statistical and machine learning-based models to estimate how variations in raw material quality affect the quality of the final product.
- Cross-link data quality, data management, data reliability and decision-making algorithms in an agile manner (machine learning in operation – MLOPs).
- Use electricity to produce different chemicals/products, hydrogen, methanol, etc, a current research field called Power-to-X.