The glycerol and methanol concentrations in Pichia pastoris fermentations were measured on-line using Fourier transform infrared spectroscopy and an attenuated total reflection probe. Partial least squares regression was used to obtain calibration models. The models were regressed on synthetic multi-component spectra and semi-synthetic fermentation broth spectra. These were obtained by spectral addition. The accuracy for the on-line measurement of glycerol, given as standard error of prediction (SEP), was determined to 0.68 g/l, and the SEP of methanol was 0.13 g/l. We show how reliable calibration models are obtained relatively effortlessly by replacing extensive sampling from the reactor with simple mathematical manipulations of the model regression spectra.
In order to study and control fermentation processes, indirect on-tine measurements and mathematical models can be used. In this article we present a mathematical on-line model for fermentation processes. The model is based on atom and partial mass balances as well as on equations describing the acid-base system. The model is brought into an adaptive form by including transport equations for mass transfer and unstructured expressions for the fermentation kinetics. The state of the process, i.e., the concentrations of biomass, substrate, and products, can be estimated on-line using the balance part of the model completed with measurement equations for the input and output flows of the process. Adaptivity is realized by means of on-line estimation of parameters in the transport and kinetic expressions using recursive regression analysis. These expressions can thus be used in the model as valid equations enabling prediction of the process. This makes model-based automation of the process and testing of the validity of the measurement variables possible. The model and the on-line principles are applied to a 3.5-L laboratory tormentor in which Saccharomyces cerevisiae is cultivated. The experimental results show that the model-based estimation of the state and the predictions of the process correlate closely with high-performance liquid chromatography (HPLC) analyses. (c) 1995 John Wiley & Sons, Inc.
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