Abstract
The role of mathematical modelling is to give insight into the process being investigated by providing a concise summary of the observed behaviour. Generally speaking, creating a model using input-output data is characterised by two things: one is a mathematical tool to express a system model and the other is the method of identification. Process identification consists of two parts: structure identification and parameter identification. Stmcture identification means finding the input variables that affect the output variables, while parameter identification means finding the values of the parameters of the relationship function.
The green alga Haematococcus pluvialis Flotow was grown as a continuous culture in two 2–1 airlift chemostats. Fresh media was continuously fed at a range of defined flow rates into the chemostats and cell concentration and dry weight determined daily. The data collected was used to generate fuzzy logic and neuro-fuzzy process models, along with the classical ARX-type models as a comparison of model performance. The modelling methods have been applied to one stage in the complex life cycle of H. pluvialis, that of the accumulation of green (astaxanthin-free) biomass. The work presented highlights the applicability of intelligent techniques for modelling the growth of algae. The results illustrate that while the classical ARMAX method can produce satisfactory representations of the process data, much improved performances can be obtained by utilising intelligent techniques such as fuzzy logic and neuro-fuzzy methods.
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Clarkson, N., Jones, K.O., Young, A.J. (2001). Modelling of a Continuous Algal Production System Using Intelligent Methods. In: Chen, F., Jiang, Y. (eds) Algae and their Biotechnological Potential. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9835-4_6
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DOI: https://doi.org/10.1007/978-94-015-9835-4_6
Publisher Name: Springer, Dordrecht
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