期刊
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
卷 55, 期 5, 页码 1361-1372出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.iecr.5b03635
关键词
-
资金
- Consortium for Risk Evaluation with Stakeholder Participation (CRESP)
- Nuclear Energy University Program (NEUP)
- Georgia Research Alliance
- Cecil J. Pete Silas Endowment
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据