Sf. Deazevedo et al., HYBRID MODELING OF BIOCHEMICAL PROCESSES - A COMPARISON WITH THE CONVENTIONAL APPROACH, Computers & chemical engineering, 21, 1997, pp. 751-756
This paper addresses attitudes and forms of process modelling in bioch
emical engineering. Baker's yeast production in a fed-batch fermenter,
at laboratory scale, is employed as case-study. Three modelling appro
aches are described and compared, viz. - the conventional mechanistic
approach, formulations based on different artificial neural network (A
NN) topologies and a hybrid mechanistic-ANN structure. A standard 2-st
ep procedure of model development, estimation (training) and validatio
n with two independent sets of experiments, has been carried out. The
mechanistic model, using reaction kinetic schemes from the literature,
fine tuned by classical non-linear regression, gave smooth prediction
s for the validation data runs, but showed limited ability in predicti
ng the test data. The ANN were able to describe experiments at the tra
ining stage, but failed the validation (i.e. extrapolation) procedure,
giving oscillatory predictions of the process state. Additionally, th
is approach suffers from a strong influence of the net parameters, whi
ch must be chosen by trial and error. The hybrid model predictions are
good with the training and very satisfactory with the experimental te
st data. The indication is that the latter is a powerful tool for proc
ess modelling in biochemical engineering, particularly when limited th
eoretical knowledge of the process is available.