Run-to-Run-Based Model Predictive Control of Protein Crystal Shape in Batch Crystallization
Kwon, Joseph Sang-Il1; Nayhouse, Michael1; Orkoulas, Gerassimos1; Ni, Dong3; Christofides, Panagiotis D.1,2
Source PublicationINDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
2015-04-29
Volume54Issue:16Pages:4293-4302
SubtypeArticle
AbstractIn 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 HeadingsScience & Technology ; Technology
WOS KeywordGROWTH ; LYSOZYME ; SIMULATION
Indexed BySCI
Language英语
WOS Research AreaEngineering
WOS SubjectEngineering, Chemical
WOS IDWOS:000353931200031
Citation statistics
Cited Times:14[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8074
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Affiliation1.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
Recommended Citation
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.
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