Model based online scheduling of concurrent and equal batch process units: Sugar End industrial case study. (August 2019) - Acebes, L.F.; Merino, A.; Rodriguez, A.; Mazaeda, R.; de Prada, C.
Highlights: The online dynamic scheduling implies an improvement in the process performance. Model based Predictive control provides a chance to update the dynamic scheduling. The use of knowledge-based constraints helps to the controller to take better solutions. The Sugar End stage is a good study case to test online dynamic scheduling strategies. The proposed online dynamic scheduling strategy is tested in the Sugar End stage. Abstract: An online scheduling algorithm for equal and concurrent batch process units embedded in a large process is shown in this paper. Its target is to calculate the waiting time intervals between consecutive cycles of each batch process unit to achieve a global stationary scheduling. This approach is based on nonlinear model predictive control with heuristic constraints for the particular process. The algorithm has been tested against an industrial problem simulation that consists of a set of batch sugar crystallizers which are part of a reduced sugar production process. Due to the large computational load of the optimization procedure, a reduced prediction model of the system was developed to obtain real time solutions.