A Comparison of Adaptive Estimation with Neural based Techniques for Bioprocess Application

The last few years have seen the proliferation of inferential estimation methodologies. These are techniques which are capable of providing estimates of `difficult-to-measure' fermentation process variables from other easily available process measurements. In the literature, two avenues tend to predominate: on being based upon the use of physicochemical models, while the other makes use of an input-output or `black-box' model structure. Clearly, those based upon an accurate physical description will offer the best performance. In reality, however, the best possible situation will be the availability of a `reasonable' process model. Alternatively, at the other end of the spectrum of process knowledge, is the use of `adaptive' generically structured observers. Firstly, a generally structured linear estimator will be introduced. Successful applications of this estimator to an idustrial mycelial fermentation will be presented. Such success is due to the continuous nature of the process, which undergoes smaller changes in dynamics than, say, a batch fermentation. For such cases it is felt that more representative nonliner observers are required. A technique now receiving recognition is that of artificial neural networks. The neural network paradigm is discussed and a comparison with the performance of the adaptive linear estimator is presented.