Double Substrate Limitation Model for the Experimental Scale-up of Succinic Acid Production from Biorefinery Glycerol

A combination of experimental and computational work has been performed in order to assess and optimise the experimental conditions for the bio-production of succinic acid - one of the top-value added chemicals (Werpy et al., 2004) from crude glycerol, which is the major by-product of the bio-diesel...

Full description

Bibliographic Details
Main Authors: A. Rigaki, C. Webb, C. Theodoropoulos
Format: Article
Language:English
Published: AIDIC Servizi S.r.l. 2013-09-01
Series:Chemical Engineering Transactions
Online Access:https://www.cetjournal.it/index.php/cet/article/view/6121
Description
Summary:A combination of experimental and computational work has been performed in order to assess and optimise the experimental conditions for the bio-production of succinic acid - one of the top-value added chemicals (Werpy et al., 2004) from crude glycerol, which is the major by-product of the bio-diesel production process. The kinetics of a single substrate (crude glycerol) model have been fully analysed and further optimisation based on both yield and productivity of succinic acid defined the decision parameters of a batch process. At the same time, the introduction of a double substrate limiting model has been suggested as it has been proved that both the uptake rate of glycerol and of dissolved CO2 have a significant effect on succinic acid production using Actinobacillus Succinogenes (Der Werf et al., 1997; Binns et al., 2011). Process parameters that influence the transfer rate of gaseous CO2 in the broth have been incorporated in the model. The developed model can be utilized to successfully predict the concentration profiles of six state variables (biomass, glycerol, succinic acid, formic acid, acetic acid and dissolved CO2) for a range of initial glycerol concentrations and working volumes. Kinetic parameters of the model were estimated by minimizing the difference between experimental and predicted values (Vlysidis et al., 2011a) for a range of batch experiments.
ISSN:2283-9216