On the applicability of adaptive bioprocess state estimators.
This article presents an industrial case study, examining the application 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 to a rigorous theoretical development. The novel algorithm adopted is developed from Adaptive Inferential Estimation studies of Guilandoust and co-workers. The technique utilizes input-output process measurements obtained at different frequencies, thereby providing more frequent estimates of biomass concentration than are otherwise available from off-line laboratory analyses. The algorithm is particularly suited to the biotechnology industry, as it is capable of utilizing irregular assay measurements with varying delays.Although this article demonstrates the encouraging industrial implications of the adaptive algorithm, like all adaptive techniques currently developed, it is restricted by the inability to perform robust on-line system identification. The ultimate selection of a "suboptimal" "fixed parameter" algorithm for on-line implementation, is therefore directly attributable to these inadequacies. Aspects of data acquisition, data pretreatment, and data quality are critical for real process applications, and while some practical approaches are adopted here, many important implementation problems remain unresolved.
Lant
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Department of Chemical Engineering, University of Queensland, Queensland, Australia.
Tham
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Montague
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