| Περίληψη: | For the sake of sustainability and environmental considerations, the chemical
industry has turned to biomass-based production of fuels and chemical commodities.
In view of that, significant research studies have been conducted on microorganisms
producing high yields of the desirable end or intermediate products.
Succinic acid has been identified by the U.S. Department of Energy as a top 12
target molecule due to the reactivity of the two carboxylic groups leading to versatile
end-products and the cost-competitiveness of biotechnological over petrochemical
production. Over the past years, there is an increasingly number of studies concerning
microorganisms producing succinic acid, some of which are Basfia succiniciproducens,
Actinobacillus succinogenes and Mannheimia succiniciproducens. The aforementioned
bacteria produce high yields of succinic acid since it is a major metabolic
intermediate, as reflected by their names. In recent years, optimization has been
employed to predict the distribution of carbon flux in the metabolism of a bacterium.
In this thesis, the specifications of each microorganism, the set of reactions implied
and the results regarding the polyhedra of the feasible regions provided by metabolic
flux analysis are studied. The developed model is employed to support experimental
data and its validity is assessed.
A mathematical model for designing and optimizing the biotransformation
(upstream) section of biotechnological processes is presented. The model has been
augmented by equations for the estimation of the equipment cost derived from a
recent publication by the US National Renewable Energy Laboratory. The processes of
the downstream section for the recovery of succinic acid are then presented, along
with a financial analysis of the case study for the biobased production of succinic acid.
A key step in the decision making in the design of a fermentation process is to
choose an appropriate strain and/or improve it, since the product of interest is
primarily determined by the properties of the microorganism. Consequently, a
quantitative approach in which basic principles of the biosciences is combined with
core disciplines from the engineering fields will indicate the best design of any
bioprocess. The novelty of this study is that it incorporates significant details of the
bioprocess design and also use is made of a relatively accurate model that simulates
the metabolic pathways of microorganisms. Therefore, the model provides a linkage
between the metabolism of bacteria and the operation and design of white
biotechnology processes.
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