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Data-Driven Modeling and Dynamic Programming Applied to Batch Cooling Crystallization
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DOI：10.1021/acs.iecr.5b03635.png)
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Abstract
En 中文
In this article, we demonstrate a model-based approach for controlling the average size of crystals produced by batch cooling crystallization. The method is distinguished most notably in the modeling strategy. Rather than developing a crystallization model within the population-balance framework, as is usually done, we apply a machine-learning technique to identify an empirical model from measurement data. The model is low-dimensional and can therefore be discretized and used with dynamic programming to obtain optimal control policies for producing crystals of targeted average sizes in prespecified batch run times. Experimental results are reported that demonstrate the use of the identified policies to produce crystals of the desired average sizes in the specified run times.
Keywords:
CRYSTAL SIZE DISTRIBUTION
PROCESS ANALYTICAL TECHNOLOGY
DIRECT NUCLEATION CONTROL
ATR-FTIR SPECTROSCOPY
FEEDBACK-CONTROL
DIRECT DESIGN
ANTISOLVENT CRYSTALLIZATION
INDUSTRIAL CRYSTALLIZATION
SUPERSATURATION CONTROL
LASER BACKSCATTERING
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