Rolling learning-prediction of product formation in bioprocesses

A rolling learning-prediction. approach based on neural networks is propose d with the aim of on-line prediction of the product formation. Commercial-s cale penicillin cultivations were taken as an example to test the product p redictor. Raw data are pretreated in such a way that each input vector of t he neural network consists of a series of time-discretised values on a spec ified transient of process variables. The output vector is composed of the amount of product at the next one and two prediction steps. The process var iables involved in the predictor include carbon dioxide and product formati on as well as oxygen, precursor and substrate consumption. Accumulated rath er than instant values of these variables were used. A simple three-layer f eedforward backpropagation neural network with a tangent sigmoidal transfer function in the hidden nodes and a linear one in the output nodes was used as the main frame of the product predictor. The proposed prediction proced ure is called rolling learning-prediction because the training database is updated after each sampling interval and the learning-prediction is repeate d thereafter. The robustness of the predictor was illustrated by its adapti ve ability to widely scattered data sets and extra added noises. The testin g results indicated that a prediction accuracy of 2-5% could be generally e xpected in the later phase of cultivation and reliable prediction time span s may take more than 10% of the cultivation period for penicillin productio n. An intrinsic problem of using neural networks-occasional trap of the net work in bad local minima-is automatically detected and remedied. In additio n, it was illustrated by example that the prediction error signal may be po tentially used to detect extraordinary charges caused, for example, by cont amination. Problems associated with the industrial application of the predi ctor are discussed. (C) 1999 Elsevier Science B.V. All rights reserved.