GlycoPy: An Equation-Oriented and Object-Oriented Software for Hierarchical Modeling, Optimization, and Control in Python

2026-01-04

arXiv.org

AI Summary

This work addresses the limitations of existing industrial model predictive control (MPC) approaches, which predominantly rely on linear models and struggle to effectively manage nonlinear (bio)chemical processes over wide operating ranges, while also lacking tools for hierarchical modeling and efficient nonlinear MPC implementation. To overcome these challenges, the authors propose GlycoPy—a novel hierarchical modeling framework in Python that uniquely integrates equation-oriented and object-oriented paradigms. GlycoPy enables users to declaratively specify model equations and seamlessly combines parameter estimation, dynamic optimization, and nonlinear MPC (NMPC), while supporting custom simulation and control algorithms. The framework’s advantages in modeling flexibility, optimization efficiency, and NMPC practicality are demonstrated through three case studies, ranging from simple differential-algebraic systems to multiscale biological processes, significantly lowering the barrier to deploying NMPC in complex (bio)chemical applications.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Machine Learning: Graph-based Machine Learning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• nonlinear model predictive control
• hierarchical modeling
• process industries
• bioprocess
• optimization

Innovation

• nonlinear model predictive control
• hierarchical modeling
• equation-oriented programming
• object-oriented framework
• dynamic optimization