Browsing by Author "Bayer, C"

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item: Conference-Abstract
Achieving value from process intensification through better process control
(2019) Udugama, IA; Mansouri, SS; Gernaey, KV; Bayer, C; Young, BR
The continual economic drive to achieve improved process efficiencies has made process integration and intensification a main stay in process industries ranging from petrochemicals to biotechnology. However, from a process control viewpoint these integrated and intensified processes are much harder to control due to complex process dynamics and/or reduced degrees of freedom. As such, in many process industries the realized efficiency gain through integration and intensification is diminished. The objective of this article is to highlight some of the lessons learnt by the authors during their involvement in controlling intensified processes in different process industries. To this end two industrial troubleshooting case studies of a side-draw distillation column and a divided wall column are presented together with actual problems the facilities faced and how the solutions developed enabled them to be remedied within industrial limitations. This is followed by an analysis of the current process integration and intensification drive of dairy and bioprocesses. Finally the lessons learnt in these diverse process industries are summarized and its implication for process control discussed.
item: Conference-Abstract
Adaptive model predictive control with successive linearization for distillate composition control in batch distillation
Mendis, P; Wickramasinghe, C; Narayana, M; Bayer, C
This paper investigates the application of adaptive model predictive control (MPC) with successive linearization for the control of top product purity of a batch distillation column. Adaptive MPC with successive linearization can overcome the prediction inaccuracies associated with linearization of highly non-linear and dynamic mathematical model of a batch distillation column, with a lower computational load than nonlinear MPC. A binary mixture of methanol and water was selected to demonstrate the controller development, and its performance was investigated by varying MPC tuning parameters in the MATLAB/Simulink simulation environment. Results indicated that the choice of tuning parameters had a considerable influence on the MPC’s ability to track a constant set-point for the output. With the correct choice of tuning parameters, however, it is possible for the controller to track a constant set-point. The present approach is compared with nonlinear MPC in order to gain a quantitative understanding on accuracy and computational effort.