کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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689393 | 889607 | 2010 | 13 صفحه PDF | دانلود رایگان |
Latent Variable Model Predictive Control (LV-MPC) algorithms are developed for trajectory tracking and disturbance rejection in batch processes. The algorithms are based on multi-phase PCA models developed using batch-wise unfolding of batch data arrays. Two LV-MPC formulations are presented, one based on optimization in the latent variable space and the other on direct optimization over a finite vector of future manipulated variables. In both cases prediction of the future trajectories is accomplished using statistical latent variable missing data imputation methods. The proposed LV-MPCs can handle constraints. Furthermore, due to the batch-wise unfolding approach selected in the modeling section, the nonlinear time-varying behavior of batch processes is captured by the linear LV models thereby yielding very simple and computationally fast nonlinear batch MPC. The methods are tested and compared on a simulated batch reactor case study.
Journal: Journal of Process Control - Volume 20, Issue 4, April 2010, Pages 538–550