One serious difficulty in modeling a fermentative process is the forecasting of the duration of the lag phase. The usual approach to model biochemical reactors relies on first-principles, unstructured mathematical models. These models are not able to take into account changes in the process response caused by different incubation times or by repeated fedbatches. To overcome this problem, we have proposed a hybrid neural network algorithm. Feedforward neural networks were used to estimate rates of cell growth, substrate consumption, and product formation from on-line measurements during cephalosporin C production. These rates were included in the mass balance equations to estimate key process variables: concentrations of cells, substrate, and product. Data from fed-batch fermentation runs in a stirred aerated bioreactor employing the microorganism Cephalosporium acremonium ATCC 48272 were used. On-line measurements strongly related to the mass and activity of the cells used. They include carbon dioxide and oxygen concentrations in the exhausted gas. Good results were obtained using this approach.
BACKGROUND: Production of microbial enzymes in bioreactors is a complex process including such phenomena as metabolic networks and mass transport resistances. The use of neural networks (NNs) to infer the state of bioreactors may be an interesting option that may handle the nonlinear dynamics of biomass growth and protein production.
This article reports studies concerning the production of penicillin G acylase (PGA) by Bacillus megaterium. This enzyme has industrial use in the hydrolysis of penicillin G to obtain 6-aminopenicillanic acid, an essential intermediate for the production of semisynthetic beta-lactam antibiotics. Although most microorganisms produce the enzyme intracellularly, B. megaterium provides extracellular PGA. The enzyme production by microorganisms involves several steps, resulting in a many operational variables to be studied. The study of the inoculum is an important step to be accomplished, before addressing other issues such as culture optimization and downstream processing. In this study, using a standard inoculum as reference, several runs were performed aiming at the definition of operational conditions in the PGA production. Cell concentration and PGA activity in the production medium were measured after 24, 48, and 72 h of the beginning of the production phase. This study encompasses the duration of the inoculum germination phase and the concentration of cells used to startup the germination. Based on these results, PGA productivity during the production phase was maximized. The selected values for these variables were 1.5 x 10(7) spores/mL of germination medium, germination during 24 h, and 72 h for the production phase.
Penicillin G acylase (PGA) is one of the most important enzymes for the pharmaceutical industry. Bacillus megaterium has the advantage of producing extra-cellular PGA. This work compares two neural networks (NNs) architectures for on-line inference of B. megaterium cell mass in an aerated stirred tank bioreactor, during the production of PGA. Nowadays, intelligent computing tools such as artificial NNs and fuzzy logic are commonly applied for state inference and modeling of bioreactors. Combining these two approaches in hybrid, neuro-fuzzy systems, may be advantageous. Our results indicate that a neuro-fuzzy inference system showed a better performance to infer cell concentrations, when compared to multilayer perceptrons networks.
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