An assessment of methods of moments for the simulation of population dynamics in large-scale bioreactors. (2nd November 2017)

• Record Type: Journal Article
• Authors: Pigou, Maxime; Morchain, Jérôme; Fede, Pascal; Penet, Marie-Isabelle; Laronze, Geoffrey
• Is Part Of: Chemical engineering science. Volume 171(2017)
• Page Start: 218
• Page End: 232
• Publication Date: 2017-11-02
• Subjects: Biological dynamics -- Population balance model -- Method of classes -- Method of moments -- (E)QMOM -- Maximum Entropy; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2017.05.026
• Languages: English
• ISSNs: 0009-2509

Abstract

Highlights: A biological population balance model is solved using class and moment methods. Homogeneous chemostat and heterogeneous fedbatch cultures are simulated. Methods are compared through accuracy, stability and computation time. The Maximum Entropy method is found to be unstable in the present test-cases. QMOM and EQMOM are well suited and have major advantages against class method. Abstract: A predictive modelling for the simulation of bioreactors must account for both the biological and hydrodynamics complexities. Population balance models (PBM) are the best approach to conjointly describe these complexities, by accounting for the adaptation of inner metabolism for microorganisms that travel in a large-scale heterogeneous bioreactor. While being accurate for solving the PBM, the Class and Monte-Carlo methods are expensive in terms of calculation and memory use. Here, we apply Methods of Moments to solve a population balance equation describing the dynamic adaptation of a biological population to its environment. The use of quadrature methods (Maximum Entropy, QMOM or EQMOM) is required for a good integration of the metabolic behavior over the population. We then compare the accuracy provided by these methods against the class method which serves as a reference. We found that the use of 5 moments to describe a distribution of growth-rate over the population gives satisfactory accuracy against a simulation with a hundred classes. Thus, all methods of moments allow a significant decrease of memory usage in simulations. In terms of stability, QMOM and EQMOM performed far better than the Maximum Entropy method. The much lower memory impact of the methods of moments offers promising perspectives for the coupling of biological models with a fine hydrodynamics depiction.