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A kinetic model describing acetone-butanol-ethanol (ABE) production applicable to both single substrate fermentations of glucose and xylose as well as co-fermentation of the substrates has been developed. The model accounts for carbon catabolite repression as well as the inhibition of kinetic rates at high substrate concentrations (∼90 g l −1 ). Model parameters were obtained through kinetic fitting to previously report experimental data. The model was used to study the design and operation of a continuous ABE fermentation process (with and without recycle of biomass). For continuous operation, it was shown that multiple steady-states exist at low dilution rates. For operation with recycle of biomass, the influence of recycle rate on both biomass concentration and ABE productivity were studied. The results indicate that for a range of recycle and dilution rates, ABE productivity can increase to 16 g l −1  h −1 (10 times higher than that without biomass recycling), consistent with experimental results. [ABSTRACT FROM AUTHOR]

Statistical Design of Experiments (DoE) is a widely adopted methodology in upstream bioprocess development (and generally across industries) to obtain experimental data from which the impact of independent variables (factors) on the process response can be inferred. In this work, a method is proposed that reduces the total number of experiments suggested by a traditional DoE. The method allows the evaluation of several DoE combinations to be compressed into a reduced number of experiments, which is referred to as intensified Design of Experiments (iDoE). In this paper, the iDoE is used to develop a dynamic hybrid model (consisting of differential equations and a feedforward artificial neural network) for data generated from a simulated Escherichia coli fermentation. For the case study presented, the results suggest that the total number of experiments could be reduced by about 40% when compared to traditional DoE. An additional benefit is the simultaneous development of an appropriate dynamic model which can be used in both, process optimization and control studies. [ABSTRACT FROM AUTHOR]

Ethyl acetate production by esterification and bioethanol recovery from dilute solutions (11 wt %) are energy-intensive processes. Although ethanol is a raw material for ethyl acetate production, both technologies are usually studied independently. Here, a cleaning coupled process was proposed. The dilute stream of ethanol was fed to a reactive distillation column, so reflux and columns for dehydration of ethanol were not required. This increases the steam flow at the bottoms of the reactive zone. Steam was found to displace the equilibrium allowing a total recovery of products in the reactive distillation column, ethyl acetate (top) and water (bottoms). So, it was found that, counterintuitively, steam favors the recovery of ethyl acetate, avoiding the over-recycle of raw materials such as acetic acid. An extractive distillation scheme using dimethyl sulfoxide (DMSO) was also proposed to minimize the energy requirements for the purification of a mixture of ethanol, water, and ethyl acetate. The decision tree model was used to estimate the importance of operating variables on total annualized costs. The operating conditions were selected considering steady-state multiplicities. The energy intensity was further decreased by heat integration with vapor compression from 2.4 to 2.8 to 0.9–1.4 MJ/kg-ethyl acetate. The scheme proposed in this work was found to be a novel intensified process as the energy intensity of the scheme proposed was between three and seven times lower than that reported previously in the literature. [Display omitted] • Ethyl acetate was produced from dilute ethanol in a novel intensified scheme. • The lowest energy intensity up to date for ethyl acetate production was reported. • Steady-state multiplicities for extractive distillation were found. • Vapor compression technology reduced the energy intensity by 42%. • The coefficient of performance of vapor compression was between 6.1 and 6.7 [ABSTRACT FROM AUTHOR]

This paper presents a novel method for the system identification of the fed-batch fermentation process defined in the problem statement of the Biotechnological Control Forum Modelling and Control Competition. The identification methodology involves a hybrid of mechanistic modelling and Genetic Programming tech niques. It provides an accurate model of the system which should be extremely useful in both the optimisation and control of this process. [ABSTRACT FROM PUBLISHER]