This article presents an introduction to the use of neural network computational algorithms for the dynamic modeling of bioprocesses. The dynamic neural model is used for the prediction of key fermentation variables. This relatively hew method is compared with a more traditional prediction technique to judge its performance for prediction. Illustrative simulation results of a continuous stirred tank fermentor are used for this comparison. It is shown that neural network models are accurate with a certain degree of noise immunity. They offer the distinctive ability over more traditional methods to learn very naturally complex relationships without requiring the knowledge of the model structure.

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International audienceAdvanced monitoring, fault detection, automatic control and optimisation of the beer fermentation process require on-line prediction and off-line simulation of key variables. Three dynamic models for the beer fermentation process are proposed and validated in laboratory scale: a model based on biological knowledge of the fermentation process, an empirical model based on the shape of the experimental curves and a black-box model based on an artificial neural network. The models take into account the fermentation temperature, the top pressure and the initial yeast concentration, and predict the wort density, the residual sugar concentration, the ethanol concentration, and the released CO 2. The models were compared in terms of prediction accuracy, robustness and generalisation ability (interpolation and extrapolation), reliability of parameter identification and interpretation of the parameter values. Not surprisingly, in the case of a relatively limited experimental data (10 experiments in various operating conditions), models that include more process knowledge appear equally accurate but more reliable than the neural network. The achieved prediction accuracy was 5% for the released CO 2 volume, 10% for the density and the ethanol concentration and 20% for the residual sugar concentration

A simple structured mathematical model coupled with a methodology of state and parameter estimation is developed for lipase production by Candida rugosa in batch fermentation. The model describes the system according to the following qualitative observations and hypothesis: Lipase production is induced by extracellular oleic acid present in the medium. The acid is transported into the cell where it is consumed, transformed, and stored. Lipase is excreted to the medium where it is distributed between the available oil-water interphase and aqueous phase. Cell growth is modulated by the intracellular substrate concentration. Model parameters have been determined and the whole model validated against experiments not used in their determination. The estimation problem consists in the estimation of three state variables (biomass, intra- and extracellular substrate) and two kinetic parameters by using only the on-line measurement provided by exhaust gas analysis. The presented estimation strategy divides the complex problem into three subproblems that can be solved by stable algorithms. The estimation of biomass (X) and the specific growth rate (mu), is achieved by a recursive prediction error algorithm using the on-line measurement of the carbon dioxide evolution rate. mu is then used to perform an estimation of intracellular substrate and the other kinetic parameter related to substrate transport (A) by an adaptive observer. Extracellular substrate is then evaluated by means of the estimated values of intracellular substrate and biomass through the material balance of the reactor. Simulation and experimental tests showed good performance of the developed estimator, which appears suitable to be used for process control and monitoring. (c) 1995 John Wiley & Sons, Inc.

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