Loading…

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

In this work, we develop a novel run-to-run-based model predictive controller (R2R-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 convent...

Full description

Saved in:
Bibliographic Details
Published in:Industrial & engineering chemistry research 2015-04, Vol.54 (16), p.4293-4302
Main Authors: Kwon, Joseph Sang-Il, Nayhouse, Michael, Orkoulas, Gerassimos, Ni, Dong, Christofides, Panagiotis D
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-a329t-c36007b7f0c3a94251a7516788dd31ab85ea489cac993dfaee392507eff88baf3
cites cdi_FETCH-LOGICAL-a329t-c36007b7f0c3a94251a7516788dd31ab85ea489cac993dfaee392507eff88baf3
container_end_page 4302
container_issue 16
container_start_page 4293
container_title Industrial & engineering chemistry research
container_volume 54
creator Kwon, Joseph Sang-Il
Nayhouse, Michael
Orkoulas, Gerassimos
Ni, Dong
Christofides, Panagiotis D
description In this work, we develop a novel run-to-run-based model predictive controller (R2R-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 parameters 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.
doi_str_mv 10.1021/ie502377a
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1709739047</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1709739047</sourcerecordid><originalsourceid>FETCH-LOGICAL-a329t-c36007b7f0c3a94251a7516788dd31ab85ea489cac993dfaee392507eff88baf3</originalsourceid><addsrcrecordid>eNptkM1KAzEYRYMoWKsL32A2gi5Gv0wmTbLUwT-oKP6sXAxfMwlNmU5qkhHq0zul6srVhcPhwr2EHFM4p1DQC2c4FEwI3CEjygvIOZR8l4xASplzKfk-OYhxAQCcl-WIvD_3XZ58vokrjKbJHnxj2uwpmMbp5D5NVvkuBd9m3g7UJ-O6rArrmLDNXua4MtkArjDp-S9u3Rcm57tDsmexjeboJ8fk7eb6tbrLp4-399XlNEdWqJRrNgEQM2FBM1RlwSkKTidCyqZhFGeSGyyl0qiVYo1FY5gqOAhjrZQztGxMTre9q-A_ehNTvXRRm7bFzvg-1lSAEkxBKQb1bKvq4GMMxtar4JYY1jWFenNg_Xfg4J5sXdSxXvg-dMOIf7xvWphvOA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1709739047</pqid></control><display><type>article</type><title>Run-to-Run-Based Model Predictive Control of Protein Crystal Shape in Batch Crystallization</title><source>American Chemical Society:Jisc Collections:American Chemical Society Read &amp; Publish Agreement 2022-2024 (Reading list)</source><creator>Kwon, Joseph Sang-Il ; Nayhouse, Michael ; Orkoulas, Gerassimos ; Ni, Dong ; Christofides, Panagiotis D</creator><creatorcontrib>Kwon, Joseph Sang-Il ; Nayhouse, Michael ; Orkoulas, Gerassimos ; Ni, Dong ; Christofides, Panagiotis D</creatorcontrib><description>In this work, we develop a novel run-to-run-based model predictive controller (R2R-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 parameters 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.</description><identifier>ISSN: 0888-5885</identifier><identifier>EISSN: 1520-5045</identifier><identifier>DOI: 10.1021/ie502377a</identifier><language>eng</language><publisher>American Chemical Society</publisher><subject>Computer simulation ; Crystal growth ; Crystallization ; Crystals ; Drift ; Dynamics ; Mathematical models ; Solubility</subject><ispartof>Industrial &amp; engineering chemistry research, 2015-04, Vol.54 (16), p.4293-4302</ispartof><rights>Copyright © 2014 American Chemical Society</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a329t-c36007b7f0c3a94251a7516788dd31ab85ea489cac993dfaee392507eff88baf3</citedby><cites>FETCH-LOGICAL-a329t-c36007b7f0c3a94251a7516788dd31ab85ea489cac993dfaee392507eff88baf3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Kwon, Joseph Sang-Il</creatorcontrib><creatorcontrib>Nayhouse, Michael</creatorcontrib><creatorcontrib>Orkoulas, Gerassimos</creatorcontrib><creatorcontrib>Ni, Dong</creatorcontrib><creatorcontrib>Christofides, Panagiotis D</creatorcontrib><title>Run-to-Run-Based Model Predictive Control of Protein Crystal Shape in Batch Crystallization</title><title>Industrial &amp; engineering chemistry research</title><addtitle>Ind. Eng. Chem. Res</addtitle><description>In this work, we develop a novel run-to-run-based model predictive controller (R2R-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 parameters 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.</description><subject>Computer simulation</subject><subject>Crystal growth</subject><subject>Crystallization</subject><subject>Crystals</subject><subject>Drift</subject><subject>Dynamics</subject><subject>Mathematical models</subject><subject>Solubility</subject><issn>0888-5885</issn><issn>1520-5045</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNptkM1KAzEYRYMoWKsL32A2gi5Gv0wmTbLUwT-oKP6sXAxfMwlNmU5qkhHq0zul6srVhcPhwr2EHFM4p1DQC2c4FEwI3CEjygvIOZR8l4xASplzKfk-OYhxAQCcl-WIvD_3XZ58vokrjKbJHnxj2uwpmMbp5D5NVvkuBd9m3g7UJ-O6rArrmLDNXua4MtkArjDp-S9u3Rcm57tDsmexjeboJ8fk7eb6tbrLp4-399XlNEdWqJRrNgEQM2FBM1RlwSkKTidCyqZhFGeSGyyl0qiVYo1FY5gqOAhjrZQztGxMTre9q-A_ehNTvXRRm7bFzvg-1lSAEkxBKQb1bKvq4GMMxtar4JYY1jWFenNg_Xfg4J5sXdSxXvg-dMOIf7xvWphvOA</recordid><startdate>20150429</startdate><enddate>20150429</enddate><creator>Kwon, Joseph Sang-Il</creator><creator>Nayhouse, Michael</creator><creator>Orkoulas, Gerassimos</creator><creator>Ni, Dong</creator><creator>Christofides, Panagiotis D</creator><general>American Chemical Society</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope></search><sort><creationdate>20150429</creationdate><title>Run-to-Run-Based Model Predictive Control of Protein Crystal Shape in Batch Crystallization</title><author>Kwon, Joseph Sang-Il ; Nayhouse, Michael ; Orkoulas, Gerassimos ; Ni, Dong ; Christofides, Panagiotis D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a329t-c36007b7f0c3a94251a7516788dd31ab85ea489cac993dfaee392507eff88baf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Computer simulation</topic><topic>Crystal growth</topic><topic>Crystallization</topic><topic>Crystals</topic><topic>Drift</topic><topic>Dynamics</topic><topic>Mathematical models</topic><topic>Solubility</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kwon, Joseph Sang-Il</creatorcontrib><creatorcontrib>Nayhouse, Michael</creatorcontrib><creatorcontrib>Orkoulas, Gerassimos</creatorcontrib><creatorcontrib>Ni, Dong</creatorcontrib><creatorcontrib>Christofides, Panagiotis D</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><jtitle>Industrial &amp; engineering chemistry research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kwon, Joseph Sang-Il</au><au>Nayhouse, Michael</au><au>Orkoulas, Gerassimos</au><au>Ni, Dong</au><au>Christofides, Panagiotis D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Run-to-Run-Based Model Predictive Control of Protein Crystal Shape in Batch Crystallization</atitle><jtitle>Industrial &amp; engineering chemistry research</jtitle><addtitle>Ind. Eng. Chem. Res</addtitle><date>2015-04-29</date><risdate>2015</risdate><volume>54</volume><issue>16</issue><spage>4293</spage><epage>4302</epage><pages>4293-4302</pages><issn>0888-5885</issn><eissn>1520-5045</eissn><abstract>In this work, we develop a novel run-to-run-based model predictive controller (R2R-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 parameters 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.</abstract><pub>American Chemical Society</pub><doi>10.1021/ie502377a</doi><tpages>10</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0888-5885
ispartof Industrial & engineering chemistry research, 2015-04, Vol.54 (16), p.4293-4302
issn 0888-5885
1520-5045
language eng
recordid cdi_proquest_miscellaneous_1709739047
source American Chemical Society:Jisc Collections:American Chemical Society Read & Publish Agreement 2022-2024 (Reading list)
subjects Computer simulation
Crystal growth
Crystallization
Crystals
Drift
Dynamics
Mathematical models
Solubility
title Run-to-Run-Based Model Predictive Control of Protein Crystal Shape in Batch Crystallization
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T07%3A15%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Run-to-Run-Based%20Model%20Predictive%20Control%20of%20Protein%20Crystal%20Shape%20in%20Batch%20Crystallization&rft.jtitle=Industrial%20&%20engineering%20chemistry%20research&rft.au=Kwon,%20Joseph%20Sang-Il&rft.date=2015-04-29&rft.volume=54&rft.issue=16&rft.spage=4293&rft.epage=4302&rft.pages=4293-4302&rft.issn=0888-5885&rft.eissn=1520-5045&rft_id=info:doi/10.1021/ie502377a&rft_dat=%3Cproquest_cross%3E1709739047%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-a329t-c36007b7f0c3a94251a7516788dd31ab85ea489cac993dfaee392507eff88baf3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1709739047&rft_id=info:pmid/&rfr_iscdi=true