1
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Ahmad N, Hao S, Liu T, Gong Y, Wang QG. Data-driven set-point learning control with ESO and RBFNN for nonlinear batch processes subject to nonrepetitive uncertainties. ISA TRANSACTIONS 2024; 146:308-318. [PMID: 38199841 DOI: 10.1016/j.isatra.2023.12.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/29/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024]
Abstract
This paper proposes an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme for a class of nonlinear batch processes with a priori P-type feedback control structure subject to nonrepetitive uncertainties, by only using the process input and output data available in practice. Firstly, the unknown process dynamics is equivalently transformed into an iterative dynamic linearization data model (IDLDM) with a residual term. A radial basis function neural network is adopted to estimate the pseudo partial derivative information related to IDLDM, and meanwhile, a data-driven iterative ESO is constructed to estimate the unknown residual term along the batch direction. Then, an adaptive set-point learning control law is designed to merely regulate the set-point command of the closed-loop control structure for realizing batch optimization. Robust convergence of the output tracking error along the batch direction is rigorously analyzed by using the contraction mapping approach and mathematical induction. Finally, two illustrative examples from the literature are used to validate the effectiveness and advantage of the proposed design.
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Affiliation(s)
- Naseem Ahmad
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China; School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
| | - Shoulin Hao
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China; School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.
| | - Tao Liu
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China; School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.
| | - Yihui Gong
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China; School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
| | - Qing-Guo Wang
- Institute of Artificial Intelligence and Future Networks, Beijing Normal University at Zhuhai, Zhuhai, China; BNU-HKBU United International College, Tangjiawan, Rd. JinTong 2000#, Zhuhai, China
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2
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Wang L, Zhu Y, Gan C. Predictive Control of Particle Size Distribution of Crystallization Process Using Deep Learning based Image Analysis. AIChE J 2022. [DOI: 10.1002/aic.17817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Liangyong Wang
- State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China
| | - Yaolong Zhu
- State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China
| | - Chenyang Gan
- State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China
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3
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Rosenbaum T, Mbachu V, Mitchell NA, Gamble JF, Cho P, Engstrom JD. Comparison of One-Dimensional and Two-Dimensional Population Balance Models for Optimization of a Crystallization Process for a Needle-Shaped Active Pharmaceutical Ingredient. Org Process Res Dev 2022. [DOI: 10.1021/acs.oprd.1c00344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Tamar Rosenbaum
- Drug Product Science and Technology, Bristol-Myers Squibb, 1 Squibb Drive, New Brunswick, New Jersey 08903, United States
| | - Victoria Mbachu
- Drug Product Science and Technology, Bristol-Myers Squibb, 1 Squibb Drive, New Brunswick, New Jersey 08903, United States
| | - Niall Anthony Mitchell
- Process Systems Enterprise (PSE) − A Siemens Business, 6th Floor East, 26-28 Hammersmith Grove, London W6 7HA, United Kingdom
| | - John Francis Gamble
- Drug Product Science and Technology, Bristol-Myers Squibb, Moreton CH46 1QW, United Kingdom
| | - Patricia Cho
- Chemical and Synthetic Development, Bristol-Myers Squibb, 1 Squibb Drive, New Brunswick, New Jersey 08903, United States
| | - Joshua D. Engstrom
- Drug Product Science and Technology, Bristol-Myers Squibb, 1 Squibb Drive, New Brunswick, New Jersey 08903, United States
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4
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Modeling and analysis of MSMPR cascades involving nucleation, growth and agglomeration mechanisms with slurry recycling. Chem Eng Res Des 2021. [DOI: 10.1016/j.cherd.2021.07.029] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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5
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Model-Based Evaluation of a Data-Driven Control Strategy: Application to Ibuprofen Crystallization. Processes (Basel) 2021. [DOI: 10.3390/pr9040653] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This work presents a methodology that relies on the application of the radial basis functions network (RBF)-based feedback control algorithms to a pharmaceutical crystallization process. Within the scope of the model-based evaluation of the proposed strategy, firstly strategies for the data treatment, data structure and the training methods reflecting the possible scenarios in the industry (Moving Window, Growing Window and Golden Batch strategies) were introduced. This was followed by the incorporation of such RBF strategies within a soft sensor application and a nonlinear predictive data-driven control application. The performance of the RBF control strategies was tested for the undisturbed cases as well as in the presence of disturbances in the process. The promising results from both RBF soft sensor control and the RBF predictive control demonstrated great potential of these techniques for the control of the crystallization process. In particular, both Moving Window and Golden Batch strategies performed the best results for an RBF soft sensor, and the Growing Window outperformed the remaining methodologies for predictive control.
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6
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Application of PAT-Based Feedback Control Approaches in Pharmaceutical Crystallization. CRYSTALS 2021. [DOI: 10.3390/cryst11030221] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Crystallization is one of the important unit operations for the separation and purification of solid products in the chemical, pharmaceutical, and pesticide industries, especially for realizing high-end, high-value solid products. The precise control of the solution crystallization process determines the polymorph, crystal shape, size, and size distribution of the crystal product, which is of great significance to improve product quality and production efficiency. In order to develop the crystallization process in a scientific method that is based on process parameters and data, process analysis technology (PAT) has become an important enabling platform. In this paper, we review the development of PAT in the field of crystallization in recent years. Based on the current research status of drug crystallization process control, the monitoring methods and control strategies of feedback control in the crystallization process were systematically summarized. The focus is on the application of model-free feedback control strategies based on the solution and solid information collected by various online monitoring equipment in product engineering, including improving particle size distribution, achieving polymorphic control, and improving purity. In this paper, the challenges of feedback control strategy in the crystallization process are also discussed, and the development trend of the feedback control strategy has been prospected.
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7
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Öner M, Montes FC, Ståhlberg T, Stocks SM, Bajtner JE, Sin G. Comprehensive evaluation of a data driven control strategy: Experimental application to a pharmaceutical crystallization process. Chem Eng Res Des 2020. [DOI: 10.1016/j.cherd.2020.08.032] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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8
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Ma Y, Wu S, Macaringue EGJ, Zhang T, Gong J, Wang J. Recent Progress in Continuous Crystallization of Pharmaceutical Products: Precise Preparation and Control. Org Process Res Dev 2020. [DOI: 10.1021/acs.oprd.9b00362] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Yiming Ma
- School of Chemical Engineering and Technology, State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300072, People’s Republic of China
- Co-innovation Center of Chemistry and Chemical Engineering of Tianjin, Tianjin 300072, People’s Republic of China
| | - Songgu Wu
- School of Chemical Engineering and Technology, State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300072, People’s Republic of China
- Co-innovation Center of Chemistry and Chemical Engineering of Tianjin, Tianjin 300072, People’s Republic of China
| | - Estevao Genito Joao Macaringue
- School of Chemical Engineering and Technology, State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300072, People’s Republic of China
- Co-innovation Center of Chemistry and Chemical Engineering of Tianjin, Tianjin 300072, People’s Republic of China
| | - Teng Zhang
- School of Chemical Engineering and Technology, State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300072, People’s Republic of China
- Co-innovation Center of Chemistry and Chemical Engineering of Tianjin, Tianjin 300072, People’s Republic of China
| | - Junbo Gong
- School of Chemical Engineering and Technology, State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300072, People’s Republic of China
- Co-innovation Center of Chemistry and Chemical Engineering of Tianjin, Tianjin 300072, People’s Republic of China
| | - Jingkang Wang
- School of Chemical Engineering and Technology, State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300072, People’s Republic of China
- Co-innovation Center of Chemistry and Chemical Engineering of Tianjin, Tianjin 300072, People’s Republic of China
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9
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Continuous Generation of Millimeter-Sized Glycine Crystals in Non-Seeded Millifluidic Slug Flow. CRYSTALS 2019. [DOI: 10.3390/cryst9080412] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Millimeter-sized α-glycine crystals were generated from continuous non-seeded cooling crystallization in slug flow. The crystallization process is composed of three steps in sequence: slug formation, crash-cooling nucleation, and growth. Stable uniform slugs of three different aspect ratios (slug length/tubing inner diameter) were formed, by adjusting the flow rates of both the solution and air streams. Besides supersaturation, the slug aspect ratio can also affect primary nucleation outcome. Stable slug flow can accommodate a relative supersaturation (C/C*) of up to 1.5 without secondary nucleation. Large glycine crystals can grow to millimeter size within 10 min, inside millimeter-sized slugs without reducing the slug quality.
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10
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Robust Process Design in Pharmaceutical Manufacturing under Batch-to-Batch Variation. Processes (Basel) 2019. [DOI: 10.3390/pr7080509] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Model-based concepts have been proven to be beneficial in pharmaceutical manufacturing, thus contributing to low costs and high quality standards. However, model parameters are derived from imperfect, noisy measurement data, which result in uncertain parameter estimates and sub-optimal process design concepts. In the last two decades, various methods have been proposed for dealing with parameter uncertainties in model-based process design. Most concepts for robustification, however, ignore the batch-to-batch variations that are common in pharmaceutical manufacturing processes. In this work, a probability-box robust process design concept is proposed. Batch-to-batch variations were considered to be imprecise parameter uncertainties, and modeled as probability-boxes accordingly. The point estimate method was combined with the back-off approach for efficient uncertainty propagation and robust process design. The novel robustification concept was applied to a freeze-drying process. Optimal shelf temperature and chamber pressure profiles are presented for the robust process design under batch-to-batch variation.
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11
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Su Q, Ganesh S, Moreno M, Bommireddy Y, Gonzalez M, Reklaitis GV, Nagy ZK. A perspective on Quality-by-Control (QbC) in pharmaceutical continuous manufacturing. Comput Chem Eng 2019; 125:216-231. [PMID: 36845965 PMCID: PMC9948678 DOI: 10.1016/j.compchemeng.2019.03.001] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
The Quality-by-Design (QbD) guidance issued by the US Food and Drug Administration (FDA) has catalyzed the modernization of pharmaceutical manufacturing practices including the adoption of continuous manufacturing. Active process control was highlighted recently as a means to improve the QbD implementation. This advance has since been evolving into the concept of Quality-by-Control (QbC). In this study, the concept of QbC is discussed, including a definition of QbC, a review of the recent developments towards the QbC, and a perspective on the challenges of QbC implementation in continuous manufacturing. The QbC concept is demonstrated using a rotary tablet press, integrated into a pilot scale continuous direct compaction process. The results conclusively showed that active process control, based on product and process knowledge and advanced model-based techniques, including data reconciliation, model predictive control (MPC), and risk analysis, is indispensable to comprehensive QbC implementation, and ensures robustness and efficiency.
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Affiliation(s)
- Qinglin Su
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Sudarshan Ganesh
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Mariana Moreno
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Yasasvi Bommireddy
- School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Marcial Gonzalez
- School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.,Ray W. Herrick Laboratories, Purdue University, West Lafayette, IN 47907, USA
| | - Gintaras V Reklaitis
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Zoltan K Nagy
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA
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12
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13
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Ghadipasha N, Romagnoli J, Tronci S, Baratti R. A model-based approach for controlling particle size distribution in combined cooling-antisolvent crystallization processes. Chem Eng Sci 2018. [DOI: 10.1016/j.ces.2018.06.032] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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14
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Multiobjective optimization and experimental validation for batch cooling crystallization of citric acid anhydrate. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2018.02.019] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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15
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Bhoi S, Sarkar D. Constructing regions of attainable sizes and achieving target size distribution in a batch cooling sonocrystallization process. ULTRASONICS SONOCHEMISTRY 2018; 42:162-170. [PMID: 29429657 DOI: 10.1016/j.ultsonch.2017.11.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 09/24/2017] [Accepted: 11/13/2017] [Indexed: 06/08/2023]
Abstract
The application of ultrasound to a crystallization process has several interesting benefits. The temperature of the crystallizer increases during ultrasonication and this makes it difficult for the temperature controller of the crystallizer to track a set temperature trajectory precisely. It is thus necessary to model this temperature rise and the temperature-trajectory tracking ability of the crystallizer controller to perform model-based dynamic optimization for a given cooling sonocrystallization set-up. In our previous study, we reported a mathematical model based on population balance framework for a batch cooling sonocrystallization of l-asparagine monohydrate (LAM). Here we extend the previous model by including energy balance equations and a Generic Model Control algorithm to simulate the temperature controller of the crystallizer that tracks a cooling profile during crystallization. The improved model yields very good closed-loop prediction and is conveniently used for studies related to particle engineering by optimization. First, the model is used to determine the regions of attainable particle sizes for LAM batch cooling sonocrystallization process by solving appropriate dynamic optimization problems. Then the model is used to determine optimal operating conditions for achieving a target crystal size distribution. The experimental evidence clearly demonstrates the efficiency of the particle engineering approach by optimization.
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Affiliation(s)
- Stutee Bhoi
- Department of Chemical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Debasis Sarkar
- Department of Chemical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India.
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16
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Wu S, Jin Q, Zhang R, Zhang J, Gao F. Improved design of constrained model predictive tracking control for batch processes against unknown uncertainties. ISA TRANSACTIONS 2017; 69:273-280. [PMID: 28411952 DOI: 10.1016/j.isatra.2017.04.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2016] [Revised: 02/07/2017] [Accepted: 04/07/2017] [Indexed: 06/07/2023]
Abstract
In this paper, an improved constrained tracking control design is proposed for batch processes under uncertainties. A new process model that facilitates process state and tracking error augmentation with further additional tuning is first proposed. Then a subsequent controller design is formulated using robust stable constrained MPC optimization. Unlike conventional robust model predictive control (MPC), the proposed method enables the controller design to bear more degrees of tuning so that improved tracking control can be acquired, which is very important since uncertainties exist inevitably in practice and cause model/plant mismatches. An injection molding process is introduced to illustrate the effectiveness of the proposed MPC approach in comparison with conventional robust MPC.
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Affiliation(s)
- Sheng Wu
- Key Lab for IOT and Information Fusion Technology of Zhejiang, Information and Control Institute, Hangzhou Dianzi University, Hangzhou 310018, PR China
| | - Qibing Jin
- Institute of Automation, Beijing University of Chemical Technology, Beijing 100029, PR China
| | - Ridong Zhang
- Key Lab for IOT and Information Fusion Technology of Zhejiang, Information and Control Institute, Hangzhou Dianzi University, Hangzhou 310018, PR China; Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Hong Kong
| | - Junfeng Zhang
- Key Lab for IOT and Information Fusion Technology of Zhejiang, Information and Control Institute, Hangzhou Dianzi University, Hangzhou 310018, PR China
| | - Furong Gao
- Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Hong Kong
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17
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Borsos Á, Szilágyi B, Agachi PŞ, Nagy ZK. Real-Time Image Processing Based Online Feedback Control System for Cooling Batch Crystallization. Org Process Res Dev 2017. [DOI: 10.1021/acs.oprd.6b00242] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Ákos Borsos
- Department
of Chemical Engineering, Loughborough University, Loughborough, LE11 3TU, United Kingdom
| | - Botond Szilágyi
- Department
of Chemical Engineering, Babeş-Bolyai University, Arany János Street 11, Cluj Napoca, 400084 Romania
| | - Paul Şerban Agachi
- Department
of Chemical Engineering, Babeş-Bolyai University, Arany János Street 11, Cluj Napoca, 400084 Romania
- College
of Engineering and Technology, Botswana International University of Science and Technology (BIUST), P. Bag 16, Palapye, Botswana
| | - Zoltán K. Nagy
- Department
of Chemical Engineering, Loughborough University, Loughborough, LE11 3TU, United Kingdom
- School
of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907, United States
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18
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Bhoi S, Lenka M, Sarkar D. Particle engineering by optimization for the unseeded batch cooling crystallization of l-asparagine monohydrate. CrystEngComm 2017. [DOI: 10.1039/c7ce01291h] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A model-based optimization approach is proposed to obtain temperature profiles to achieve the target CSD in a batch cooling crystallization process.
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Affiliation(s)
- Stutee Bhoi
- Department of Chemical Engineering
- Indian Institute of Technology Kharagpur
- Kharagpur 721302
- India
| | - Maheswata Lenka
- Department of Chemical Engineering
- Indian Institute of Technology Kharagpur
- Kharagpur 721302
- India
| | - Debasis Sarkar
- Department of Chemical Engineering
- Indian Institute of Technology Kharagpur
- Kharagpur 721302
- India
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19
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Griffin DJ, Grover MA, Kawajiri Y, Rousseau RW. Data-Driven Modeling and Dynamic Programming Applied to Batch Cooling Crystallization. Ind Eng Chem Res 2016. [DOI: 10.1021/acs.iecr.5b03635] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Daniel J. Griffin
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0100, United States
| | - Martha A. Grover
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0100, United States
| | - Yoshiaki Kawajiri
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0100, United States
| | - Ronald W. Rousseau
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0100, United States
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20
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Rasoulian S, Ricardez-Sandoval LA. Stochastic nonlinear model predictive control applied to a thin film deposition process under uncertainty. Chem Eng Sci 2016. [DOI: 10.1016/j.ces.2015.10.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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21
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Wu Z, Yang S, Wu W. Application of temperature cycling for crystal quality control during crystallization. CrystEngComm 2016. [DOI: 10.1039/c5ce02522b] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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22
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Liu Y, Zhang Z, Chen J. Ensemble local kernel learning for online prediction of distributed product outputs in chemical processes. Chem Eng Sci 2015. [DOI: 10.1016/j.ces.2015.06.005] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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23
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Vetter T, Burcham CL, Doherty MF. Designing Robust Crystallization Processes in the Presence of Parameter Uncertainty Using Attainable Regions. Ind Eng Chem Res 2015. [DOI: 10.1021/acs.iecr.5b00693] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Thomas Vetter
- School of Chemical Engineering
and Analytical Science, University of Manchester, Manchester M13 9PL, United Kingdom
| | - Christopher L. Burcham
- Small Molecule Design and Development, Eli Lilly & Company, Indianapolis, Indiana 46285, United States
| | - Michael F. Doherty
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, United States
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24
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Huettmann H, Zich S, Berkemeyer M, Buchinger W, Jungbauer A. Design of industrial crystallization of interferon gamma: Phase diagrams and solubility curves. Chem Eng Sci 2015. [DOI: 10.1016/j.ces.2014.12.018] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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25
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Simon LL, Pataki H, Marosi G, Meemken F, Hungerbühler K, Baiker A, Tummala S, Glennon B, Kuentz M, Steele G, Kramer HJM, Rydzak JW, Chen Z, Morris J, Kjell F, Singh R, Gani R, Gernaey KV, Louhi-Kultanen M, O’Reilly J, Sandler N, Antikainen O, Yliruusi J, Frohberg P, Ulrich J, Braatz RD, Leyssens T, von Stosch M, Oliveira R, Tan RBH, Wu H, Khan M, O’Grady D, Pandey A, Westra R, Delle-Case E, Pape D, Angelosante D, Maret Y, Steiger O, Lenner M, Abbou-Oucherif K, Nagy ZK, Litster JD, Kamaraju VK, Chiu MS. Assessment of Recent Process Analytical Technology (PAT) Trends: A Multiauthor Review. Org Process Res Dev 2015. [DOI: 10.1021/op500261y] [Citation(s) in RCA: 269] [Impact Index Per Article: 29.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
| | - Hajnalka Pataki
- Department
of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3, H-1111 Budapest, Hungary
| | - György Marosi
- Department
of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3, H-1111 Budapest, Hungary
| | - Fabian Meemken
- Department
of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg
1, 8093 Zürich, Switzerland
| | - Konrad Hungerbühler
- Department
of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg
1, 8093 Zürich, Switzerland
| | - Alfons Baiker
- Department
of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg
1, 8093 Zürich, Switzerland
| | - Srinivas Tummala
- Chemical
Development, Bristol-Myers Squibb Company, One Squibb Dr, New Brunswick, New Jersey 08903, United States
| | - Brian Glennon
- Synthesis
and Solid State Pharmaceutical Centre, School of Chemical and Bioprocess
Engineering, University College Dublin, Belfield, Dublin 4, Ireland
- APC Ltd, Belfield Innovation
Park, Dublin 4, Ireland
| | - Martin Kuentz
- School of Life
Sciences, Institute of Pharma Technology, University of Applied Sciences and Arts Northwestern Switzerland, Gründenstrasse 40, 4132 Muttenz, Switzerland
| | - Gerry Steele
- PharmaCryst Consulting
Ltd., Loughborough, Leicestershire LE11 3HN, U.K
| | - Herman J. M. Kramer
- Intensified Reaction & Separation Systems, Delft University of Technology, Leeghwaterstraat 39, 2628 CB Delft, The Netherlands
| | - James W. Rydzak
- GlaxoSmithKline Pharmaceuticals, 709 Swedeland Rd, King of
Prussia, Pennsylvania 19406, United States
| | - Zengping Chen
- State Key
Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry
and Chemical Engineering, Hunan University, Changsha, Hunan 410082, PR China
| | - Julian Morris
- Centre for Process Analytics & Control Technology, School of Chemical Engineering & Advanced Materials, Newcastle University, Newcastle upon Tyne, Tyne and Wear NE17RU, U.K
| | - Francois Kjell
- Siemens nv/sa,
Industry
Automation − SIPAT Industry Software, Marie Curie Square 30, 1070 Brussels, Belgium
| | - Ravendra Singh
- CAPEC-PROCESS,
Department of Chemical and Biochemical Engineering, Technical University of Denmark (DTU), Building 229, DK-2800 Lyngby, Denmark
| | - Rafiqul Gani
- CAPEC-PROCESS,
Department of Chemical and Biochemical Engineering, Technical University of Denmark (DTU), Building 229, DK-2800 Lyngby, Denmark
| | - Krist V. Gernaey
- CAPEC-PROCESS,
Department of Chemical and Biochemical Engineering, Technical University of Denmark (DTU), Building 229, DK-2800 Lyngby, Denmark
| | - Marjatta Louhi-Kultanen
- Department
of Chemical Technology, Lappeenranta University of Technology, P.O. Box 20, FI-53851 Lappeenranta, Finland
| | - John O’Reilly
- Roche Ireland
Limited, Clarecastle, Co. Clare, Ireland
| | - Niklas Sandler
- Pharmaceutical
Sciences Laboratory, Department of Biosciences, Abo Akademi University, Artillerigatan 6, 20520 Turku, Finland
| | - Osmo Antikainen
- Division
of Pharmaceutical Technology, Faculty of Pharmacy, University of Helsinki, Yliopistonkatu 4, 00100 Helsinki, Finland
| | - Jouko Yliruusi
- Division
of Pharmaceutical Technology, Faculty of Pharmacy, University of Helsinki, Yliopistonkatu 4, 00100 Helsinki, Finland
| | - Patrick Frohberg
- Center of
Engineering Science, Thermal Process Engineering, Martin Luther University Halle-Wittenberg, D-06099 Halle (Saale), Germany
| | - Joachim Ulrich
- Center of
Engineering Science, Thermal Process Engineering, Martin Luther University Halle-Wittenberg, D-06099 Halle (Saale), Germany
| | - Richard D. Braatz
- Massachusetts Institute
of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Tom Leyssens
- Institute
of Condensed Matter and Nanosciences, Université Catholique de Louvain, Place Louis Pasteur 1, 1348 Louvain-la-Neuve, Belgium
| | - Moritz von Stosch
- REQUIMTE
- Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 1099-085 Caparica, Portugal
- HybPAT, Caparica, Portugal
| | - Rui Oliveira
- REQUIMTE
- Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 1099-085 Caparica, Portugal
- HybPAT, Caparica, Portugal
| | - Reginald B. H. Tan
- Institute
of Chemical and Engineering Sciences, A*Star, 1 Pesek Road, Singapore 627833
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576
| | - Huiquan Wu
- Division
of Product Quality Research, Office of Testing and Research, Office
of Pharmaceutical Science, Center for Drug Evaluation and Research, US Food and Drug Administration (FDA), Silver Spring, Maryland 20993, United States
| | - Mansoor Khan
- Division
of Product Quality Research, Office of Testing and Research, Office
of Pharmaceutical Science, Center for Drug Evaluation and Research, US Food and Drug Administration (FDA), Silver Spring, Maryland 20993, United States
| | - Des O’Grady
- Mettler Toledo
AutoChem, 7075 Samuel Morse Drive, Columbia, Maryland 20146, United States
| | - Anjan Pandey
- Mettler Toledo
AutoChem, 7075 Samuel Morse Drive, Columbia, Maryland 20146, United States
| | - Remko Westra
- FMC Technologies B.V., Delta 101, 6825 MN Arnhem, The Netherlands
| | - Emmanuel Delle-Case
- University of Tulsa, 800 South Tucker
Drive, Tulsa, Oklahoma 74104, United States
| | - Detlef Pape
- ABB Corporate Research Center, Segelhofstrasse
1K, 5405, Dättwil, Baden, Switzerland
| | - Daniele Angelosante
- ABB Corporate Research Center, Segelhofstrasse
1K, 5405, Dättwil, Baden, Switzerland
| | - Yannick Maret
- ABB Corporate Research Center, Segelhofstrasse
1K, 5405, Dättwil, Baden, Switzerland
| | - Olivier Steiger
- ABB Corporate Research Center, Segelhofstrasse
1K, 5405, Dättwil, Baden, Switzerland
| | - Miklós Lenner
- ABB Corporate Research Center, Segelhofstrasse
1K, 5405, Dättwil, Baden, Switzerland
| | - Kaoutar Abbou-Oucherif
- School of
Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Zoltan K. Nagy
- School of
Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
- Chemical
Engineering Department, Loughborough University, Loughborough, LE11 3TU, U.K
| | - James D. Litster
- School of
Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Vamsi Krishna Kamaraju
- Synthesis
and Solid State Pharmaceutical Centre, School of Chemical and Bioprocess
Engineering, University College Dublin, Belfield, Dublin 4, Ireland
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576
| | - Min-Sen Chiu
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576
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26
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Bolaños-Reynoso E, Sánchez-Sánchez KB, Urrea-García GR, Ricardez-Sandoval L. Dynamic Modeling and Optimization of Batch Crystallization of Sugar Cane under Uncertainty. Ind Eng Chem Res 2014. [DOI: 10.1021/ie501800j] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Eusebio Bolaños-Reynoso
- División
de Estudios de Posgrado e Investigación, Instituto Tecnológico de Orizaba, Avenida Oriente 9 Núm. 852 Colonia Emiliano
Zapata, Orizaba, Veracruz 94320, México
| | - Kelvyn B. Sánchez-Sánchez
- División
de Estudios de Posgrado e Investigación, Instituto Tecnológico de Orizaba, Avenida Oriente 9 Núm. 852 Colonia Emiliano
Zapata, Orizaba, Veracruz 94320, México
| | - Galo R. Urrea-García
- División
de Estudios de Posgrado e Investigación, Instituto Tecnológico de Orizaba, Avenida Oriente 9 Núm. 852 Colonia Emiliano
Zapata, Orizaba, Veracruz 94320, México
| | - Luis Ricardez-Sandoval
- Department
of Chemical Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
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27
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Kwon JSI, Nayhouse M, Orkoulas G, Ni D, Christofides PD. Run-to-Run-Based Model Predictive Control of Protein Crystal Shape in Batch Crystallization. Ind Eng Chem Res 2014. [DOI: 10.1021/ie502377a] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | | | | | - Dong Ni
- Institute
of Automation, Chinese Academy of Sciences, Beijing 100190, China
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28
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Borsos Á, Lakatos BG. Investigation and simulation of crystallization of high aspect ratio crystals with fragmentation. Chem Eng Res Des 2014. [DOI: 10.1016/j.cherd.2013.08.020] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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29
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Orkomi AA, Shahrokhi M. SIMULATION AND CONTROL OF MULTIDIMENSIONAL CRYSTALLIZATION PROCESSES. CHEM ENG COMMUN 2014. [DOI: 10.1080/00986445.2013.785947] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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30
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García Muñoz S, Padovani V, Mercado J. A computer aided optimal inventory selection system for continuous quality improvement in drug product manufacture. Comput Chem Eng 2014. [DOI: 10.1016/j.compchemeng.2013.09.020] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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31
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Ochsenbein DR, Schorsch S, Vetter T, Mazzotti M, Morari M. Growth Rate Estimation of β l-Glutamic Acid from Online Measurements of Multidimensional Particle Size Distributions and Concentration. Ind Eng Chem Res 2013. [DOI: 10.1021/ie4031852] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- David R. Ochsenbein
- Automatic
Control Laboratory, ETH Zurich, Physikstrasse 3, CH-8092 Zurich, Zurich, Switzerland
| | - Stefan Schorsch
- Institute
of Process Engineering, ETH Zurich, Sonneggstrasse 3, CH-8092 Zurich, Zurich, Switzerland
| | - Thomas Vetter
- Institute
of Process Engineering, ETH Zurich, Sonneggstrasse 3, CH-8092 Zurich, Zurich, Switzerland
| | - Marco Mazzotti
- Institute
of Process Engineering, ETH Zurich, Sonneggstrasse 3, CH-8092 Zurich, Zurich, Switzerland
| | - Manfred Morari
- Automatic
Control Laboratory, ETH Zurich, Physikstrasse 3, CH-8092 Zurich, Zurich, Switzerland
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32
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Sanzida N, Nagy ZK. Iterative learning control for the systematic design of supersaturation controlled batch cooling crystallisation processes. Comput Chem Eng 2013. [DOI: 10.1016/j.compchemeng.2013.05.027] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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33
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Nagy ZK, Fevotte G, Kramer H, Simon LL. Recent advances in the monitoring, modelling and control of crystallization systems. Chem Eng Res Des 2013. [DOI: 10.1016/j.cherd.2013.07.018] [Citation(s) in RCA: 211] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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34
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Majumder A, Nagy ZK. Prediction and control of crystal shape distribution in the presence of crystal growth modifiers. Chem Eng Sci 2013. [DOI: 10.1016/j.ces.2013.07.017] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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35
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Majumder A, Nagy ZK. Fines removal in a continuous plug flow crystallizer by optimal spatial temperature profiles with controlled dissolution. AIChE J 2013. [DOI: 10.1002/aic.14196] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Aniruddha Majumder
- Dept. of Chemical Engineering; Loughborough University; Loughborough LE11 3TU U.K
| | - Zoltan K. Nagy
- School of Chemical Engineering, Purdue University; West Lafayette IN 47907
- Dept. of Chemical Engineering; Loughborough University; Loughborough LE11 3TU U.K
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36
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A systematic framework for design of process monitoring and control (PAT) systems for crystallization processes. Comput Chem Eng 2013. [DOI: 10.1016/j.compchemeng.2013.03.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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37
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Samad NAFA, Sin G, Gernaey KV, Gani R. Introducing uncertainty analysis of nucleation and crystal growth models in Process Analytical Technology (PAT) system design of crystallization processes. Eur J Pharm Biopharm 2013; 85:911-29. [PMID: 23770430 DOI: 10.1016/j.ejpb.2013.05.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2012] [Revised: 05/28/2013] [Accepted: 05/31/2013] [Indexed: 10/26/2022]
Abstract
This paper presents the application of uncertainty and sensitivity analysis as part of a systematic model-based process monitoring and control (PAT) system design framework for crystallization processes. For the uncertainty analysis, the Monte Carlo procedure is used to propagate input uncertainty, while for sensitivity analysis, global methods including the standardized regression coefficients (SRC) and Morris screening are used to identify the most significant parameters. The potassium dihydrogen phosphate (KDP) crystallization process is used as a case study, both in open-loop and closed-loop operation. In the uncertainty analysis, the impact on the predicted output of uncertain parameters related to the nucleation and the crystal growth model has been investigated for both a one- and two-dimensional crystal size distribution (CSD). The open-loop results show that the input uncertainties lead to significant uncertainties on the CSD, with appearance of a secondary peak due to secondary nucleation for both cases. The sensitivity analysis indicated that the most important parameters affecting the CSDs are nucleation order and growth order constants. In the proposed PAT system design (closed-loop), the target CSD variability was successfully reduced compared to the open-loop case, also when considering uncertainty in nucleation and crystal growth model parameters. The latter forms a strong indication of the robustness of the proposed PAT system design in achieving the target CSD and encourages its transfer to full-scale implementation.
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Affiliation(s)
- Noor Asma Fazli Abdul Samad
- Computer Aided Process-Product Engineering Center (CAPEC), Department of Chemical and Biochemical Engineering, Technical University of Denmark, DK-2800, Kgs. Lyngby, Denmark
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38
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Wijaya Hermanto M, Phua A, Shan Chow P, Tan RB. Improved C-control of crystallization with reduced calibration effort via conductometry. Chem Eng Sci 2013. [DOI: 10.1016/j.ces.2013.04.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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39
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Aumi S, Mhaskar P. An adaptive data-based modeling approach for predictive control of batch systems. Chem Eng Sci 2013. [DOI: 10.1016/j.ces.2012.12.005] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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40
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41
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Hofmann S, Raisch J. Solutions to inversion problems in preferential crystallization of enantiomers—Part II: Batch crystallization in two coupled vessels. Chem Eng Sci 2013. [DOI: 10.1016/j.ces.2012.10.030] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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42
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Aamir E, Rielly CD, Nagy ZK. Experimental Evaluation of the Targeted Direct Design of Temperature Trajectories for Growth-Dominated Crystallization Processes Using an Analytical Crystal Size Distribution Estimator. Ind Eng Chem Res 2012. [DOI: 10.1021/ie301610z] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- E. Aamir
- Loughborough University, Loughborough, Leicestershire, LE11 3TU,
United Kingdom
| | - C. D. Rielly
- Loughborough University, Loughborough, Leicestershire, LE11 3TU,
United Kingdom
| | - Z. K. Nagy
- Loughborough University, Loughborough, Leicestershire, LE11 3TU,
United Kingdom
- School of
Chemical Engineering, Purdue University, West Lafayette, Indiana 47907-2100,
United States
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43
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Nagy Z, Aamir E. Systematic design of supersaturation controlled crystallization processes for shaping the crystal size distribution using an analytical estimator. Chem Eng Sci 2012. [DOI: 10.1016/j.ces.2012.08.048] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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44
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Simon L, Merz T, Dubuis S, Lieb A, Hungerbuhler K. In-situ monitoring of pharmaceutical and specialty chemicals crystallization processes using endoscopy–stroboscopy and multivariate image analysis. Chem Eng Res Des 2012. [DOI: 10.1016/j.cherd.2012.03.023] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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45
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Hofmann S, Raisch J. Solutions to inversion problems in preferential crystallization of enantiomers—part I: Batch crystallization in a single vessel. Chem Eng Sci 2012. [DOI: 10.1016/j.ces.2012.06.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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46
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Pataki H, Csontos I, Nagy ZK, Vajna B, Molnar M, Katona L, Marosi G. Implementation of Raman Signal Feedback to Perform Controlled Crystallization of Carvedilol. Org Process Res Dev 2012. [DOI: 10.1021/op300062t] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Hajnalka Pataki
- Department
of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest,
Hungary
- Department
of Control Engineering and Information Technology, Budapest University of Technology and Economics, H-1111
Budapest, Hungary
| | - Istvan Csontos
- Department
of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest,
Hungary
- Department
of Control Engineering and Information Technology, Budapest University of Technology and Economics, H-1111
Budapest, Hungary
| | - Zsombor K. Nagy
- Department
of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest,
Hungary
- Department
of Control Engineering and Information Technology, Budapest University of Technology and Economics, H-1111
Budapest, Hungary
| | - Balazs Vajna
- Department
of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest,
Hungary
- Department
of Control Engineering and Information Technology, Budapest University of Technology and Economics, H-1111
Budapest, Hungary
| | - Milan Molnar
- Department
of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest,
Hungary
- Department
of Control Engineering and Information Technology, Budapest University of Technology and Economics, H-1111
Budapest, Hungary
| | - Laszlo Katona
- Department
of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest,
Hungary
- Department
of Control Engineering and Information Technology, Budapest University of Technology and Economics, H-1111
Budapest, Hungary
| | - Gyorgy Marosi
- Department
of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest,
Hungary
- Department
of Control Engineering and Information Technology, Budapest University of Technology and Economics, H-1111
Budapest, Hungary
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47
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Abstract
The academic literature on and industrial practice of control of solution crystallization processes have seen major advances in the past 15 years that have been enabled by progress in in-situ real-time sensor technologies and driven primarily by needs in the pharmaceutical industry for improved and more consistent quality of drug crystals. These advances include the accurate measurement of solution concentrations and crystal characteristics as well as the first-principles modeling and robust model-based and model-free feedback control of crystal size and polymorphic identity. Research opportunities are described in model-free controller design, new crystallizer designs with enhanced control of crystal size distribution, strategies for the robust control of crystal shape, and interconnected crystallization systems for multicomponent crystallization.
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Affiliation(s)
- Zoltan K. Nagy
- Department of Chemical Engineering, Loughborough University, Loughborough, LE11 3TU, United Kingdom
| | - Richard D. Braatz
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
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48
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In situ monitoring, control and optimization of a liquid–liquid phase separation crystallization. Chem Eng Sci 2012. [DOI: 10.1016/j.ces.2012.01.047] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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49
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Saleemi AN, Steele G, Pedge NI, Freeman A, Nagy ZK. Enhancing crystalline properties of a cardiovascular active pharmaceutical ingredient using a process analytical technology based crystallization feedback control strategy. Int J Pharm 2012; 430:56-64. [DOI: 10.1016/j.ijpharm.2012.03.029] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2012] [Revised: 03/16/2012] [Accepted: 03/17/2012] [Indexed: 11/25/2022]
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50
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Saleemi A, Rielly C, Nagy Z. Monitoring of the combined cooling and antisolvent crystallisation of mixtures of aminobenzoic acid isomers using ATR-UV/vis spectroscopy and FBRM. Chem Eng Sci 2012. [DOI: 10.1016/j.ces.2012.02.005] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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