The primary objective is to demonstrate that, by adapting digitalization methodologies to process industries, significant optimization of the processes and reduction of their environmental impacts can be achieved. The innovations include optimization and control of industrial production processes using big data analytics, new online sensors and data-based models. In close cooperation with the project partners the innovation will lead to the development of an algorithm for self-adapting model, which the partners can potentially integrate into their existing data systems at the end of the projects. The research will involve combining existing process data with new data gathered from a grid of sensors assembled in an innovative manner. These new process data and the data-driven models will be used to define the optimum set points for the processes. The R&D challenges are many-fold, where the most critically are: i) harsh environment makes on-line measurement non-operational in practice, ii) identifying the most causal process variables from correlated data, iii) the majority of available data have poor quality – resulting in poor predictive models, iv) unexpected changes in critical process variables not integrated in the data-based models (e.g. future change in raw material quality) which reduce the autonomy potential of the SAM module. By involving several process industries, this project will give researchers the opportunity to develop generic algorithms suitable for a broad range of processes.
Project leader: Frode Brakstad
Institution: BILFINGER INDUSTRIAL SERVICES NORWAY AS