Application of rigorous model-based predictive process control in the glass industry

The steady state and dynamic behaviour (heat transfer, temperatures, glass and gas flows) in glass furnaces and forehearths can be described accurately and reliably by Computational Fluid Dynamics (CFD) models [1] such as the TNO Glass Tank Model (GTM-X). Application of these detailed, but also slow models for direct on-line control or optimization of glass melting processes is not possible without strong model reduction. TNO, IPCOS and the Eindhoven University of Technology have developed a generic approach, the so-called Proper Orthogonal Decomposition (POD), which is able to reduce the complex CFD glass furnace simulation model to no more than approximately 50 equations, while maintaining the required accuracy and level of detail. Herewith, the computational speed of the reduced order model increases drastically even up to 10.000 times faster than real-time. By following this approach, the resulting reduced models have become so fast, that they can directly be applied in Model based Predictive Control (MPC). This paper describes the benefits of the so-called Rigorous Model based Predictive Control system (rMPC: an MPC based upon a fast, detailed, and accurate 3D CFD model). Also, the approach for setting-up such a controller is discussed and results are shown of an rMPC installed at a container glass furnace to control glass melt temperatures.