-The performance of an in-situ capacitance sensor for on-line monitoring of biomass concentration was evaluated for some of the most important microorganisms in the biotechnology industry: Escherichia coli, Saccharomyces cerevisiae, Pichia pastoris and Bacillus megaterium. A total of 33 batch and fed-batch cultures were carried out in a bench-scale bioreactor and biomass formation trends were followed by dielectric measurements during the growth phase as well as the induction phase, for 5 recombinant E. coli strains. Permittivity measurements and viable cellular concentrations presented a linear correlation for all the studied conditions. In addition, the permittivity signal was further used for inference of the cellular growth rate. The estimated specific growth rates mirrored the main trends of the metabolic states of the different cells and they can be further used for setting-up control strategies in fed-batch cultures.
Lignocellulosic biomass is mainly composed of cellulose, hemicellulose, and lignin. Fuzzy logic, in turn, is a branch of many-valued logic based on the paradigm of inference under vagueness. This paper presents a methodology, based on computational intelligence, for modeling the kinetics of a complex reactional system. The design of a fuzzy interpolator to model cellulose hydrolysis is reported, within the perspective of applying kinetic models in bioreactor engineering. Experimental data for various types of lignocellulosic materials were used to develop the interpolator. New experimental data from the enzymatic hydrolysis of a synthetic substrate, on the other hand, were used to validate the methodology. The accuracy of the results indicates that this is a promising approach to extend the application of models fitted for specific situations to different cases, thus enhancing their generality.
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