Details of Research Outputs

An iterative dynamic optimization methodology is developed for on-line optimization of batch processes in the presence of plant-model mismatch and measurable error. In the proposed method, the plant-model mismatch is effectively eliminated by using information from previous batches to modify the trajectories that are applied to the subsequent ones. In addition, the effect of modelling error on the convergence of this algorithm is investigated. The utility of the proposed method is illustrated through the end-point optimization problem in the batch crystallization process, and comparisons to other optimization methods are made.An iterative dynamic optimization methodology is developed for on-line optimization of batch processes in the presence of plant-model mismatch and measurable error. In the proposed method, the plant-model mismatch is effectively eliminated by using information from previous batches to modify the trajectories that are applied to the subsequent ones. In addition, the effect of modelling error on the convergence of this algorithm is investigated. The utility of the proposed method is illustrated through the end-point optimization problem in the batch crystallization process, and comparisons to other optimization methods are made.