GlycoPy: An Equation-Oriented and Object-Oriented Python Framework for Process Modeling, Optimization and Optimal Control
Year: 2026
Type: conference-paper
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.
Source: Systems and Control Transactions
DOI: 10.69997/sct.197405
Authors: Yingjie Ma, Jing Guo, Richard D. Braatz
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7165404401