(345c) Data-Driven Modeling of Sequential Batch-Continuous Process
AIChE Annual Meeting
Tuesday, November 10, 2015 - 1:00pm to 1:15pm
Jungup Park, Michael Baldea,
Thomas F. Edgar Department
of Chemical Engineering
University of Texas at Austin, 1 University Station C0400, Austin, TX 78712
chemical processes can involve both batch and continuous stages. In this case,
raw materials and other ingredients are initially processed batch-wise, prior
to being fed to a processing line that operates continuously. The operating
conditions and operating performance of both the batch and the continuous
stages have an impact on the final product quality.
batch-to-continuous processes pose specific analysis and control challenges.
The batch side of the process operation is carried out periodically at
specified time intervals. After each operating instance, the batch product is
fed to the continuous production flux. Empirical evidence suggests that this
mode of operation leads to a deterioration of the causal relation between the
properties of the batch product and the quality of the product of the
continuous process. This is further complicated by the time delay that is
inherently introduced by the continuous stage of the process between the
completion of the batch stage and any quality measurements obtained from the
this contribution, we focus on using data-driven modeling tools to define a framework for establishing causality between batch properties and continuous process. Subsequently, we will focus on the batch
monitoring, correlating batch to continuous data, and modeling the end quality
of the product using different set of training data.
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