Advanced Autonomous Model-Based Operation of Industrial Process Systems

Project X.J.A. Bombois (Xavier), J.H.A. Ludlage (Jobert), A. Mesbah (Ali)
Keywords:Identification and estimation, Model predictive control, Process technology, Model-based control
Sponsored by:European Union via the Seventh Framework Programme for research and technological development (FP7).

This project is a joint endeavor of an international consortium of industrial and academic partners. Academic partners are Delft University of Technology (The Netherlands), Eindhoven University of Technology (The Netherlands), RWTH Aachen (Germany) and KTH Stockholm (Sweden). The industrial partners are ABB (Sweden), Boliden (Sweden) and SASOL (South Africa).

The cost related to the industrial implementation of current model-based operation support systems, like Model Predictive Control (MPC), Real-Time Optimization (RTO) and soft-sensors for large-scale complex dynamic processes are currently very high. Moreover it is widely recognized that the life-time performance of these systems is rather limited, particularly due to the fact that the underlying dynamic models need to be adapted/calibrated regularly, requiring dedicated measurement campaigns executed by highly specialized engineers. Given the importance of increasing demands on economic and sustainable process operation, there is a strong need to reduce the costs and increase the performance of model-based operation support systems. Therefore in this project a model-based operation support technology is developed that enables control and model calibration at a considerable higher level of autonomy than currently possible. The technology to be developed operates on the basis of the least costly principle. The influence of the invasive testing on process’ operation and economics will be minimised, to the extent possible given the necessary accuracy of the resulting identified model. Moreover all decisions are based on an economic trade-off between process operation costs and benefits. This operation support system should be able to optimise plant performance under varying operational conditions and adapting to changing circumstances.

© Copyright Delft Center for Systems and Control, Delft University of Technology, 2017.