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SAM Self Adapting Model-based system for Process Autonomy

SAM Self Adapting Model-based system for Process Autonomy

The primary objective of SAM is to optimize demanding industrial processes by developing advanced physical models and machine learning algorithms, and integrating new online sensors where real time data is currently limited or lacking.

This project is about an innovative and new digitalisation system for self-adapting models (SAM) - with a large potential for improving the competitiveness of Norwegian process industries.
This project is about an innovative and new digitalisation system for self-adapting models (SAM) - with a large potential for improving the competitiveness of Norwegian process industries.

There is an imbalance between the pace of change in the well-established process industries in Norway and the high-speed at which technology development and digitalisation are happening. There are massive opportunities for greener and more optimised processes. At the same time, only 17% of Norwegian industry claim to be advanced users in automation and digitalisation 

 

Bilfinger, the project owner, is a leading supplier of industrial services, and aim to advance their digital platform, BCAP for employment as a digitalisation toolbox in the process industry. In SAM, methods for optimization and control of industrial production processes will be developed, using big data analytics, new online sensors and data-based modelsThe innovation will lead to the development of algorithms for self-adapting models, which the end-users can potentially integrate into their existing data systems at the end of the project.   

The research requires cross-disciplinary expertise in data analysis, process chemistry, measurement systems and process control (cybernetics). The research partnersSINTEF and the University of South-Eastern Norway (USN), will investigate new sensors and models in close collaboration with end-users. The end-users providing case studies for the research are:

This project has received funding from the Research Council of Norway. Project No. 295945.

Published 18 December 2019
Chief Scientist
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Project duration

2019 - 2023