GlycoPy: An Equation-Oriented and Object-Oriented Software for Hierarchical Modeling, Optimization, and Control in Python
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| Title: | GlycoPy: An Equation-Oriented and Object-Oriented Software for Hierarchical Modeling, Optimization, and Control in Python |
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| Authors: | Ma, Yingjie, Guo, Jing, Braatz, Richard D. |
| Publication Year: | 2026 |
| Collection: | ArXiv.org (Cornell University Library) |
| Subject Terms: | Software Engineering, Mathematical Software, Optimization and Control |
| Description: | Most existing model predictive control (MPC) applications in process industries employ lin-ear models, although real-world (bio)chemical processes are typically nonlinear. The use of linear models limits the performance and applicability of MPC for processes that span a wide range of operating conditions. A challenge in employing nonlinear models in MPC for com-plex systems is the lack of tools that facilitate hierarchical model development, as well as lack of efficient implementations of the corresponding nonlinear MPC (NMPC) algorithms. As a step towards making NMPC more practical for hierarchical systems, we introduce Gly-coPy, an equation-oriented, object-oriented software framework for process modeling, opti-mization, and NMPC in Python. GlycoPy enables users to focus on writing equations for modeling while supporting hierarchical modeling. GlycoPy includes algorithms for parame-ter estimation, dynamic optimization, and NMPC, and allows users to customize the simula-tion, optimization, and control algorithms. Three case studies, ranging from a simple differ-ential algebraic equation system to a multiscale bioprocess model, validate the modeling, optimization, and NMPC capabilities of GlycoPy. GlycoPy has the potential to bridge the gap between advanced NMPC algorithms and their practical application in real-world (bio)chemical processes. |
| Document Type: | text |
| Language: | unknown |
| Relation: | http://arxiv.org/abs/2601.01413 |
| Availability: | http://arxiv.org/abs/2601.01413 |
| Accession Number: | edsbas.3D8ED086 |
| Database: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | Most existing model predictive control (MPC) applications in process industries employ lin-ear models, although real-world (bio)chemical processes are typically nonlinear. The use of linear models limits the performance and applicability of MPC for processes that span a wide range of operating conditions. A challenge in employing nonlinear models in MPC for com-plex systems is the lack of tools that facilitate hierarchical model development, as well as lack of efficient implementations of the corresponding nonlinear MPC (NMPC) algorithms. As a step towards making NMPC more practical for hierarchical systems, we introduce Gly-coPy, an equation-oriented, object-oriented software framework for process modeling, opti-mization, and NMPC in Python. GlycoPy enables users to focus on writing equations for modeling while supporting hierarchical modeling. GlycoPy includes algorithms for parame-ter estimation, dynamic optimization, and NMPC, and allows users to customize the simula-tion, optimization, and control algorithms. Three case studies, ranging from a simple differ-ential algebraic equation system to a multiscale bioprocess model, validate the modeling, optimization, and NMPC capabilities of GlycoPy. GlycoPy has the potential to bridge the gap between advanced NMPC algorithms and their practical application in real-world (bio)chemical processes. |
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