Abstract 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.

High cell density cultivations of recombinant E. coli have been increasingly used for the production of heterologous proteins. However, it is a challenge to maintain these cultivations within the desired conditions, given that some variables such as dissolved oxygen concentration (DOC) and feed flow rate are difficult to control. This paper describes the software SUPERSYS_HCDC, a tool developed to supervise fed-batch cultures of rE. coli with biomass concentrations up to 150 gDCW/L and cell productivities up to 9 gDCW.L-1.h-1. The tool includes automatic control of the DOC by integrated action of the stirrer speed as well as of the air and oxygen flow rates; automatic start-up of the feed flow of fresh medium (system based on a neural network committee); and automatic slowdown of feeding when oxygen consumption exceeds the maximum capacity of the oxygen supply.

This work presents a kinetic study of the enzymatic hydrolysis of three cellulosic substrates: filter paper (FP), used as a low recalcitrance substrate model; steam exploded sugarcane bagasse (SB); and weak acid pretreated SB (1:20 dry bagasse:H2SO4 solution 1% w/w), the last two delignified with 4% NaOH (w/w). The influence of substrate concentration was assessed in hydrolysis experiments in a shaker, using Accellerase® 1500, at pH 4.8, in 50 mM sodium citrate buffer. Cellulose loads (weight substrate/weight total) were changed between 0.5%-13% (for FP) and 0.99%-9.09% (for SB). For FP and low loads of steam exploded SB, it was possible to fit pseudo-homogeneous Michaelis-Menten models (with inhibition). For FP and higher loads of steam exploded SB, modified Michaelis-Menten models were fitted. Besides, it was observed that, after retuning of the model parameters, it is possible to apply a model fitted for one situation to a different case. Chrastil models were also fitted and they were the only feasible approach for the highly recalcitrant acid-treated SB.

This work reviews recent trends in the modeling of cellulose hydrolysis, within the perspective of application of kinetic models in a bioreactor engineering framework, including scale-up, design and process optimization. From this point of view, despite the phenomenological insight that mechanistic models can provide, the expectation that more detailed approaches could be a basis for extrapolations to different substrates and/or enzymatic pools is still not fulfilled. The complexity of the lignocellulosic matrix, the different mechanisms of catalytic action, the role of mass transfer limitations and the deviations from ideal mixing are important difficulties for the modeler, which will continue to impose more conservative approaches for scale-up. Nevertheless, the search for more robust models is a very important task, provided that the engineer is aware of their limitations. Data-driven, non-mechanistic models such as artificial neural networks, perhaps in combination with other approaches in the so-called hybrid models, is also a promising alternative.

The production of penicillin G acylase (PGA, an important industrial enzyme) from a wild strain of Bacillus megaterium using a pool of free amino acids as substrate was studied in a bench-scale bioreactor. Experiments carried out in shakers showed that the substitution of casein for free amino acids in the presence of cheese whey was the culture medium that provided the highest productivity. Several cultivations were carried out in a bioreactor operated in either batch or fed-batch mode. Batch runs showed that enzyme production is associated with microorganism growth. The following set of amino acids was preferentially consumed: Ala, Arg, Asp, Gly, Lys, Ser, Thr and Trp. On the other hand, the rates of consumption of His, Ile, Leu, Met, Phe, Pro, Tyr and Val were lower.

Penicillin G acylase (PGA) is an important enzyme used as biocatalyst in the production of semisynthetic beta-lactam antibiotics. Many microorganisms produce this enzyme and recombinant Escherichia coli has been preferred use for industrial applications. Bacillus megaterium is one of the microorganisms that excretes this enzyme into the medium. As a consequence, separation and purification steps are simplified. On-line measurement of enzyme activity during cultivation using in-situ sensors is a difficult task in the industrial environment due to the lack of robust and inexpensive instrumentation. This work presents the results of a fuzzy logic algorithm used to determine the moment of maximum enzyme concentration during Bacillus megaterium cultivations in an aerated and stirred, automated lab-scale bioreactor. The fuzzy algorithm was written in Fortran, compiled as a dynamic link library and implemented on a platform developed in MS-Visual Basic. Data were exchanged in real time between the platform and the supervisory system, which was coupled to the bioreactor. It was possible to determine the moment at which maximum enzyme activity was reached in several bioreactor assays. At this point, the end of the process was indicated to the operator. The results illustrate the importance of using reliable computational intelligence-based algorithms in biochemical reactions.

An experimental design was applied to improve the reaction conditions for enzymatic synthesis of ampicillin from phenylglycine methyl ester (PGME) and 6-aminopenicillanic acid (6-APA), catalyzed by penicillin G acylase from E. coli immobilized on an agarose-glyoxyl derivative. The presence and magnitude of interactions between reaction variables were estimated using a 2(5) factorial design. A batch reactor was employed to assess the influence of the following variables: pH, temperature, initial 6-APA concentration, buffer concentration, and the presence of methanol. Response variables were productivity, selectivity, and yield (based on initial 6-APA concentration). The best synthesis yield (56.9%) was at T = 4ºC and pH 6.5. The highest productivity (49.3 ´ 10-3mM of antibiotic/min) was achieved at T = 25ºC and pH 6.5. Our results indicate that it is possible to achieve high productivity for this system while maintaining a high selectivity and yield.

A bench-scale, continuous vortex flow reactor (VFR), with a radius ratio, h, equal to 0.48 and an aspect ratio, G, equal to 11.19 was studied. This reactor may be used in the enzymatic hydrolysis of polypeptides obtained from sweet cheese whey with enzymes immobilized on agarose gel. Operational conditions were 2410 < Re q < 11793 and 30-min residence time for glycerol-water, 14% w/w, 27ºC (Re ax = 1.1) and for water, 38ºC (Re ax = 1.5). Residence time distributions (RTDs) were obtained after pulse injections of different tracers (including dyed solid particles). Mass transfer coefficients of a lumped-parameter model of the reactor were estimated from these data. Model fitting to experimental data was accurate. Working conditions were selected so that transport properties of the fluids would be similar to the ones in the actual process at the final stages of whey hydrolysis.

This paper reports on the use of the gas balance and dynamic methods to obtain an estimate of the volumetric oxygen transfer coefficient (kLa) in a conventional reactor during the growth phase of the microorganism Cephalosporium acremonium. A new way of calculating kLa by the dynamic method employing an electrode with a slow response, is proposed. The calculated values of kLa were used in the training of a feedforward neural network, for which the inputs were the parameter measurements of the related variables. The neural network technique proved effective, predicting values of kLa accurately from input data not used during the training phase. In contrast, the gas balance method was shown to be less useful. This could be attributed to the poor data obtained with the apparatus used to measure the oxygen in the exhaust gas, explained by the low rate of oxygen consumption by the microorganism.