Run-to-Run-Based Model Predictive Control of Protein Crystal Shape in Batch Crystallization

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Run-to-Run-Based Model Predictive Control of Protein Crystal Shape in Batch Crystallization
	Kwon, Joseph Sang-Il 1; Nayhouse, Michael 1; Orkoulas, Gerassimos 1; Ni, Dong 3; Christofides, Panagiotis D.1,2
发表期刊	INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
	2015-04-29
卷号	54 期号:16 页码:4293-4302
文章类型	Article
摘要	In this work, we develop a novel run-to-run-based model predictive controller (BR-based MPC) for a batch crystallization process with process drift and inherent variation in solubility and crystal growth rates. In order to achieve the production of crystals with desired product qualities, a conventional MPC system with nominal process model paraineters is initially applied to a batch protein crystallization process. However, the mismatch between the process model and the actual process dynamic behavior because of the process drift and variability becomes severe as batch runs are repeated. To deal with this problem of batch-to-batch variability, after each batch is over, the post-batch crystal attribute measurements, including average crystal shape and size and the number of crystals, are used to estimate off-line the drift of the process model (used in the MPC) parameters from nominal values via a multivariable optimization problem. Along with the adapted controller model parameters, the exponentially weighted moving average (EWMA) scheme is used to deal with the remaining offset in the crystal shape values and thereby to compute a set of optimal jacket temperatures. Furthermore, the crystal growth in the batch crystallization process is modeled through kinetic Monte Carlo simulations, which are then used to demonstrate the capability of the proposed R2R-based MPC scheme in suppressing the inherent variation and process drift in solubility and crystal growth rates. It is demonstrated that the production of crystals with a desired shape distribution is successfully achieved after three batch runs through the use of the proposed R2R-based MPC, while it takes 24 batch runs for the system with the EWMA-type constant supersaturation control to achieve the same objective.
WOS标题词	Science & Technology ; Technology
关键词[WOS]	GROWTH ; LYSOZYME ; SIMULATION
收录类别	SCI
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Chemical
WOS记录号	WOS:000353931200031
引用统计	被引频次：29[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/8074
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
作者单位	1.Univ Calif Los Angeles, Dept Chem & Biomol Engn, Los Angeles, CA 90095 USA 2.Chinese Acad Sci, Dept Elect Engn, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Kwon, Joseph Sang-Il,Nayhouse, Michael,Orkoulas, Gerassimos,et al. Run-to-Run-Based Model Predictive Control of Protein Crystal Shape in Batch Crystallization[J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH,2015,54(16):4293-4302.
APA	Kwon, Joseph Sang-Il,Nayhouse, Michael,Orkoulas, Gerassimos,Ni, Dong,&Christofides, Panagiotis D..(2015).Run-to-Run-Based Model Predictive Control of Protein Crystal Shape in Batch Crystallization.INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH,54(16),4293-4302.
MLA	Kwon, Joseph Sang-Il,et al."Run-to-Run-Based Model Predictive Control of Protein Crystal Shape in Batch Crystallization".INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 54.16(2015):4293-4302.