LAPSE:2026.0438
Published Article
LAPSE:2026.0438
GlycoPy: An Equation-Oriented and Object-Oriented Python Framework for Process Modeling, Optimization and Optimal Control
June 12, 2026
Abstract
Nonlinear model predictive control (NMPC) can substantially improve performance and constraint handling for (bio)chemical processes, but its adoption is still limited by the effort required to build maintainable first-principles models and to implement efficient dynamic optimization-based controllers. This paper presents GlycoPy, an open-source, equation-oriented and object-oriented Python framework that supports hierarchical model construction and integrated workflows for simulation, parameter estimation, dynamic optimization, and NMPC. The case study of the monoclonal antibody glycosylation process based on a multiscale model demonstrates the capability of GlycoPy.
Nonlinear model predictive control (NMPC) can substantially improve performance and constraint handling for (bio)chemical processes, but its adoption is still limited by the effort required to build maintainable first-principles models and to implement efficient dynamic optimization-based controllers. This paper presents GlycoPy, an open-source, equation-oriented and object-oriented Python framework that supports hierarchical model construction and integrated workflows for simulation, parameter estimation, dynamic optimization, and NMPC. The case study of the monoclonal antibody glycosylation process based on a multiscale model demonstrates the capability of GlycoPy.
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Suggested Citation
Ma Y, Guo J, Braatz RD. GlycoPy: An Equation-Oriented and Object-Oriented Python Framework for Process Modeling, Optimization and Optimal Control. Systems and Control Transactions 5:1884-1890 (2026) https://doi.org/10.69997/sct.197405
Author Affiliations
Ma Y: Nanjing University, School of Robotics and Automation, Suzhou, Jiangsu Province, China. Massachusetts Institute of Technology, Department of Chemical Engineering, Cambridge, MA, USA [ORCID]
Guo J: Massachusetts Institute of Technology, Department of Chemical Engineering, Cambridge, MA, USA. Polytechnique Montréal, Department of Chemical Engineering, Montréal, QC, Canada
Braatz RD: Nanjing University, School of Robotics and Automation, Suzhou, Jiangsu Province, China [ORCID]
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Guo J: Massachusetts Institute of Technology, Department of Chemical Engineering, Cambridge, MA, USA. Polytechnique Montréal, Department of Chemical Engineering, Montréal, QC, Canada
Braatz RD: Nanjing University, School of Robotics and Automation, Suzhou, Jiangsu Province, China [ORCID]
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Journal Name
Systems and Control Transactions
Volume
5
First Page
1884
Last Page
1890
Year
2026
Publication Date
2026-06-12
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Original Submission
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PII: 1884-1890-640-SCT-5-2026, Publication Type: Journal Article
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LAPSE:2026.0438
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https://doi.org/10.69997/sct.197405
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Jun 12, 2026
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References Cited
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