ON THE APPLICABILITY OF ADAPTIVE BIOPROCESS STATE ESTIMATORS

This article presents an industrial case study, examining the applicat ion of a novel adaptive biomass estimator to an industrial microfungi production process. It is our intention that this contribution should focus upon the implementation issues of the algorithm, in preference t o a rigorous theoretical development. The novel algorithm adopted is d eveloped from Adaptive Inferential Estimation studies of Guilandoust a nd co-workers. The technique utilizes input-output process measurement s obtained at different frequencies, thereby providing more frequent e stimates of biomass concentration than are otherwise available from of f-line laboratory analyses. The algorithm is particularly suited to th e biotechnology industry, as it is capable of utilizing irregular assa y measurements with varying delays. Although this article demonstrates the encouraging industrial implications of the adaptive algorithm, li ke all adaptive techniques currently developed, it is restricted by th e inability to perform robust on-line system identification. The ultim ate selection of a ''suboptimal'' ''fixed parameter'' algorithm for on -line implementation, is therefore directly attributable to these inad equacies. Aspects of data acquisition, data pretreatment, and data qua lity are critical for real process applications, and while some practi cal approaches are adopted here, many important implementation problem s remain unresolved. (C) 1993 John Wiley & Sons, Inc.