Bioprocess applications of model-based estimation techniques
| Autor: | Gary Montague, Aidan Saunders, A. Julian Morris, Paul Lant, Christine Di Massimo, M.T. Tham |
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| Rok vydání: | 2007 |
| Předmět: |
Structure (mathematical logic)
Measure (data warehouse) Engineering Artificial neural network Renewable Energy Sustainability and the Environment Process (engineering) business.industry General Chemical Engineering Organic Chemistry Estimator Pollution Industrial engineering System model Inorganic Chemistry Range (mathematics) Fuel Technology Bioprocess business Waste Management and Disposal Biotechnology |
| Zdroj: | ResearcherID |
| ISSN: | 1097-4660 0268-2575 |
| DOI: | 10.1002/jctb.280530307 |
| Popis: | The last decade has seen the development of a number of approaches for estimating those variables which are difficult to measure on‐line in industrial process situations. Whilst a range of techniques is available, a common element is the use of process knowledge in the form of a system model. In the case of bioprocess systems, although a large range of models has been presented in the literature, their use in estimation schemes on an industrial scale has been limited. A number of reasons can be identified for their low level of utilisation. Of particular significance is the uncertainty which exists in quantifying system performance and the process‐model mismatch which inevitably results. The level of ‘pre‐defined model’ uncertainty, together with the knowledge gained during the course of the fermentation, serves to dictate estimator structure. The paper considers a range of estimation strategies and contrasts, through industrial applications, their performance characteristics and utility. |
| Databáze: | OpenAIRE |
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