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Iterative learning control for final batch product quality using partial least squares models
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DOI：10.1021/ie048811p.png)
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摘要
En 中文
A terminal iterative learning control (ILC) strategy for batch-to-batch and within-batch control of final product properties, based on empirical partial least squares (PLS) models, is presented. The strategy rejects persistent process disturbances and achieves new final product quality targets using an iterative procedure that works in the reduced space of a latent variable model rather than in the high dimensional space of the manipulated variable trajectories. Complete manipulated variable trajectory reconstruction is then achieved by exploiting the PLS model of the process. The approach is illustrated with a condensation polymerization example for the production of nylon.
Keyword:
MOLECULAR-WEIGHT DISTRIBUTION
OPTIMAL TEMPERATURE
POLYMERIZATION PROCESSES
DYNAMIC OPTIMIZATION
QUADRATIC CRITERION
CORRECTION POLICIES
SEMIBATCH REACTORS
PREDICTIVE CONTROL
TENDENCY MODELS
SYSTEMS
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期刊
I
IF:
3.9
论文数:
4.0W
被引数:
9.6W
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