Nonlinear model reduction for dynamic analysis of cell population models

Yongchun Zhang, Michael A. Henson, Yannis G. Kevrekidis

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Transient cell population balance models consist of nonlinear partial differential-integro equations. An accurate discretized approximation typically requires a large number of nonlinear ordinary differential equations that are not well suited for dynamic analysis and model based controller design. In this paper, proper orthogonal decomposition (also known as the method of empirical orthogonal eigenfunctions and Karhunen Loéve expansion) is used to construct nonlinear reduced-order models from spatiotemporal data sets obtained via simulations of an accurate discretized yeast cell population model. The short-term and long-term behavior of the reduced-order models are evaluated by comparison to the full-order model. Dynamic simulation and bifurcation analysis results demonstrate that reduced-order models with a comparatively small number of differential equations yield accurate predictions over a wide range of operating conditions.

Original languageEnglish (US)
Pages (from-to)429-445
Number of pages17
JournalChemical Engineering Science
Issue number2
StatePublished - Jan 2003

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • General Chemical Engineering
  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Nonlinear model reduction for dynamic analysis of cell population models'. Together they form a unique fingerprint.

Cite this