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Eslami T, Jungbauer A. Control strategy for biopharmaceutical production by model predictive control. Biotechnol Prog 2024; 40:e3426. [PMID: 38199980 DOI: 10.1002/btpr.3426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 10/04/2023] [Accepted: 12/20/2023] [Indexed: 01/12/2024]
Abstract
The biopharmaceutical industry is rapidly advancing, driven by the need for cutting-edge technologies to meet the growing demand for life-saving treatments. In this context, Model Predictive Control (MPC) has emerged as a promising solution to address the complexity of modern biopharmaceutical production processes. Its ability to optimize operations and ensure consistent product yields has made it an attractive option for manufacturers in this sector. Furthermore, MPC's alignment with the Process Analytical Technology (PAT) initiative provides an additional layer of assurance, facilitating real-time monitoring and enabling swift adjustments to maintain process integrity. This comprehensive review delves into the various applications of MPC, ranging from robust control to stochastic model predictive control, thereby equipping biotechnologists and process engineers with a powerful toolset. By harnessing the capabilities of MPC, as elucidated in this review, manufacturers can confidently navigate the intricate bioprocessing landscape and unlock this approach's full potential in their production processes.
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Affiliation(s)
- Touraj Eslami
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
- Evon GmbH, St. Ruprecht an der Raab, Austria
| | - Alois Jungbauer
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
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2
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Scott D, Briggs NEB, Formosa A, Burnett A, Desai B, Hammersmith G, Rapp K, Capellades G, Myerson AS, Roper TD. Impurity Purging through Systematic Process Development of a Continuous Two-Stage Crystallization. Org Process Res Dev 2023. [DOI: 10.1021/acs.oprd.2c00317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Drew Scott
- Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, Virginia23284, United States
| | - Naomi E. B. Briggs
- On Demand Pharmaceuticals, 1550 E Gude Drive, Rockville, Maryland20850, United States
| | - Anna Formosa
- On Demand Pharmaceuticals, 1550 E Gude Drive, Rockville, Maryland20850, United States
| | - Annessa Burnett
- Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, Virginia23284, United States
| | - Bimbisar Desai
- TCG GreenChem, Inc., 701 Charles Ewing Boulevard, Ewing, New Jersey08628, United States
| | - Greg Hammersmith
- On Demand Pharmaceuticals, 1550 E Gude Drive, Rockville, Maryland20850, United States
| | - Kersten Rapp
- On Demand Pharmaceuticals, 1550 E Gude Drive, Rockville, Maryland20850, United States
| | - Gerard Capellades
- Henry M. Rowan College of Engineering, Rowan University, Glassboro, New Jersey08028, United States
| | - Allan S. Myerson
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Thomas D. Roper
- Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, Virginia23284, United States
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3
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Liu F, Bagi SD, Su Q, Chakrabarti R, Barral R, Gamekkanda JC, Hu C, Mascia S. Targeting Particle Size Specification in Pharmaceutical Crystallization: A Review on Recent Process Design and Development Strategies and Particle Size Measurements. Org Process Res Dev 2022. [DOI: 10.1021/acs.oprd.2c00277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Affiliation(s)
- Fan Liu
- Jiangsu Key Laboratory of Neuropsychiatric Diseases, College of Pharmaceutical Sciences, Soochow University, Suzhou 215123, China
- CONTINUUS Pharmaceuticals, 25R Olympia Avenue, Woburn, Massachusetts01801, United States
| | - Sujay D. Bagi
- CONTINUUS Pharmaceuticals, 25R Olympia Avenue, Woburn, Massachusetts01801, United States
| | - Qinglin Su
- CONTINUUS Pharmaceuticals, 25R Olympia Avenue, Woburn, Massachusetts01801, United States
| | - Rajshree Chakrabarti
- CONTINUUS Pharmaceuticals, 25R Olympia Avenue, Woburn, Massachusetts01801, United States
| | - Rita Barral
- CONTINUUS Pharmaceuticals, 25R Olympia Avenue, Woburn, Massachusetts01801, United States
| | - Janaka C. Gamekkanda
- CONTINUUS Pharmaceuticals, 25R Olympia Avenue, Woburn, Massachusetts01801, United States
| | - Chuntian Hu
- CONTINUUS Pharmaceuticals, 25R Olympia Avenue, Woburn, Massachusetts01801, United States
| | - Salvatore Mascia
- CONTINUUS Pharmaceuticals, 25R Olympia Avenue, Woburn, Massachusetts01801, United States
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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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5
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Feedback Control of Crystal Size Distribution for Cooling Batch Crystallization Using Deep Learning-Based Image Analysis. CRYSTALS 2022. [DOI: 10.3390/cryst12050570] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The shape of the crystal size distribution directly determines the quality of crystal products. It is often assumed that distributional properties of crystal size conform to the Gaussian distribution or the log normal distribution. The mean and variance or relative crystal number are widely adopted to describe the crystal size distribution and taken as the control objectives. Therefore, the resulting control methods have difficulties in controlling the crystal size distribution with a general shape. In this article, a novel feedback control system of crystal size distribution based on image analysis is designed for the effective control of crystal size distribution with a general shape. First, a deep learning network-based image analysis method is adopted and implemented to extract the crystal size distribution. Second, the crystal size distribution is approximated by a radial basis function neural network. Consequently, a feedback controller is designed and the tracking control of the target crystal size distribution is finally realized. The results of crystallization experiments demonstrate the effectiveness of the proposed method.
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Ashraf AB, Rao CS. Multiobjective Temperature Trajectory Optimization for Unseeded Batch Cooling Crystallization of Aspirin. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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8
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Aghajanian S, Ruuskanen V, Nieminen H, Laari A, Honkanen M, Koiranen T. Real-time monitoring and insights into process control of micron-sized calcium carbonate crystallization by an in-line digital microscope camera. Chem Eng Res Des 2022. [DOI: 10.1016/j.cherd.2021.12.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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9
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Pan HJ, Ward JD. Dimensionless Framework for Seed Recipe Design and Optimal Control of Batch Crystallization. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.0c06132] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Hao-Jen Pan
- Dept. of Chemical Engineering, National Taiwan University, Taipei 106-07, Taiwan
| | - Jeffrey D. Ward
- Dept. of Chemical Engineering, National Taiwan University, Taipei 106-07, Taiwan
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Orehek J, Teslić D, Likozar B. Continuous Crystallization Processes in Pharmaceutical Manufacturing: A Review. Org Process Res Dev 2020. [DOI: 10.1021/acs.oprd.0c00398] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Jaka Orehek
- National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia
- Lek d. d., Sandoz, a Novartis division, Verovškova 57, 1526 Ljubljana, Slovenia
| | - Dušan Teslić
- Lek d. d., Sandoz, a Novartis division, Verovškova 57, 1526 Ljubljana, Slovenia
| | - Blaž Likozar
- National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia
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11
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Toukhtarian R, Darabi M, Hatzikiriakos S, Atsbha H, Boulet B. Parameter identification of transport PDE/nonlinear ODE cascade model for polymer extrusion with varying die gap. CAN J CHEM ENG 2020. [DOI: 10.1002/cjce.23910] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Raffi Toukhtarian
- Department of Electrical and Computer Engineering McGill University Montreal Québec Canada
| | - Mostafa Darabi
- Department of Electrical and Computer Engineering McGill University Montreal Québec Canada
| | - Savvas Hatzikiriakos
- Department of Chemical and Biological Engineering University of British Columbia Vancouver British Columbia Canada
| | - Haile Atsbha
- Department of Computer Aided Engineering Kautex Textron Windsor Ontario Canada
| | - Benoit Boulet
- Department of Electrical and Computer Engineering McGill University Montreal Québec Canada
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Sheng L, Li J, He G, Xiao W, Yan X, Li X, Ruan X, Jiang X. Visual study and simulation of interfacial liquid layer mass transfer in membrane-assisted antisolvent crystallization. Chem Eng Sci 2020. [DOI: 10.1016/j.ces.2020.116003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Jia R, Zhang X, Cui F, Chen G, Li H, Peng H, Cao Z, Pei S. Machine-learning-based computationally efficient particle size distribution retrieval from bulk optical properties. APPLIED OPTICS 2020; 59:7284-7291. [PMID: 32902492 DOI: 10.1364/ao.398364] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 07/19/2020] [Indexed: 06/11/2023]
Abstract
Retrieval of particle size distribution from bulk optical properties based on evolutionary algorithms is usually computationally expensive. In this paper, we report an efficient numerical approach to solving the inverse scattering problem by accelerating the calculation of bulk optical properties based on machine learning. With the assumption of spherical particles, the forward scattering by particles is first solved by Mie scattering theory and then approximated by machine learning. The particle swarm optimization algorithm is finally employed to optimize the particle size distribution parameters by minimizing the deviation between the target and simulated bulk optical properties. The accuracies of machine learning and particle swarm optimization are separately investigated. Meanwhile, both monomodal and bimodal size distributions are tested, considering the influences of random noise. Results show that machine learning is capable of accurately predicting the scattering efficiency for a specific size distribution in approximately 0.5 µs on a standalone computer. Therefore, the proposed method has the potential to serve as a powerful tool in real-time particle size measurement due to its advantages of simplicity and high efficiency.
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Li J, Sheng L, Tuo L, Xiao W, Ruan X, Yan X, He G, Jiang X. Membrane-Assisted Antisolvent Crystallization: Interfacial Mass-Transfer Simulation and Multistage Process Control. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c01645] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jin Li
- State Key Laboratory of Fine Chemicals, School of Chemical Engineering, Engineering Laboratory for Petrochemical Energy-efficient Separation Technology of Liaoning Province, Dalian University of Technology, Dalian 116024, China
| | - Lei Sheng
- State Key Laboratory of Fine Chemicals, School of Chemical Engineering, Engineering Laboratory for Petrochemical Energy-efficient Separation Technology of Liaoning Province, Dalian University of Technology, Dalian 116024, China
| | - Linghan Tuo
- State Key Laboratory of Fine Chemicals, School of Chemical Engineering, Engineering Laboratory for Petrochemical Energy-efficient Separation Technology of Liaoning Province, Dalian University of Technology, Dalian 116024, China
| | - Wu Xiao
- State Key Laboratory of Fine Chemicals, School of Chemical Engineering, Engineering Laboratory for Petrochemical Energy-efficient Separation Technology of Liaoning Province, Dalian University of Technology, Dalian 116024, China
| | - Xuehua Ruan
- State Key Laboratory of Fine Chemicals, School of Chemical Engineering, Engineering Laboratory for Petrochemical Energy-efficient Separation Technology of Liaoning Province, Dalian University of Technology, Dalian 116024, China
| | - Xiaoming Yan
- State Key Laboratory of Fine Chemicals, School of Chemical Engineering, Engineering Laboratory for Petrochemical Energy-efficient Separation Technology of Liaoning Province, Dalian University of Technology, Dalian 116024, China
| | - Gaohong He
- State Key Laboratory of Fine Chemicals, School of Chemical Engineering, Engineering Laboratory for Petrochemical Energy-efficient Separation Technology of Liaoning Province, Dalian University of Technology, Dalian 116024, China
| | - Xiaobin Jiang
- State Key Laboratory of Fine Chemicals, School of Chemical Engineering, Engineering Laboratory for Petrochemical Energy-efficient Separation Technology of Liaoning Province, Dalian University of Technology, Dalian 116024, China
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Bötschi S, Rajagopalan AK, Rombaut I, Morari M, Mazzotti M. From needle-like toward equant particles: A controlled crystal shape engineering pathway. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.106581] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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