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.

翻译：尽管现实世界中的（生物）化学过程通常是非线性的，但过程工业中现有的大多数模型预测控制（MPC）应用仍采用线性模型。对于操作条件范围较宽的过程，线性模型的使用限制了MPC的性能和适用性。在复杂系统的MPC中采用非线性模型所面临的挑战在于：缺乏便于分层模型开发的工具，以及相应非线性模型预测控制（NMPC）算法的高效实现。为使NMPC在分层系统中更具实用性，我们推出了GlycoPy——一个基于Python的、面向方程和面向对象的过程建模、优化与NMPC软件框架。GlycoPy支持分层建模，使用户能够专注于建模方程的编写。该框架集成了参数估计、动态优化和NMPC算法，并允许用户自定义仿真、优化与控制算法。通过从简单的微分代数方程组到多尺度生物过程模型的三个案例研究，验证了GlycoPy在建模、优化和NMPC方面的能力。GlycoPy有望弥合先进NMPC算法与其在现实（生物）化学过程中实际应用之间的差距。