Abstract A very important step in the study of bioprocesses is the modeling and simulation of bioreactors. This paper presents the AnaBioPlus software package, which was developed for teaching and research purposes concerning the analysis of bioreactors. The package consists of two programs: OptimusFerm and SimulaFerm. OptimusFerm allows the analysis of experimental batch cultivation data sets in order to estimate parameters of kinetic models by means of a differential evolution method. SimulaFerm performs simulations obtaining values of cell growth, product formation, and limiting substrate consumption for batch, fed-batch and continuous cultivations. Thirty-two kinetic models of cell growth are available in OptimusFerm and SimulaFerm. Two case studies are described in order to illustrate the use of AnaBioPlus. The results show that AnaBioPlus is a very useful tool for the analysis of bioprocesses. Furthermore, it presents a user-friendly graphical interface and is freely available to users, in both English and Portuguese versions.

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.

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.