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    Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach

    Access Status
    Fulltext not available
    Authors
    Azadeh, A.
    Neshat, N.
    Kazemi, A.
    Saberi, Morteza
    Date
    2012
    Type
    Journal Article
    
    Metadata
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    Citation
    Azadeh, A. and Neshat, N. and Kazemi, A. and Saberi, M. 2012. Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach. International Journal of Advanced Manufacturing Technology. 58: pp. 585-596.
    Source Title
    International Journal of Advanced Manufacturing Technology
    Additional URLs
    http://www.springerlink.com/content/382653l46t17n7kr/
    ISSN
    0268-3768
    School
    Digital Ecosystems and Business Intelligence Institute (DEBII)
    URI
    http://hdl.handle.net/20.500.11937/49334
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and partial least squares (PLS) approaches are applied to predictive control of a drying process. In the proposed approaches, the PLS analysis is used to pre-process actual data and to provide the necessary background to apply ANN and ANFIS approaches. A reasonable section of this study is assigned to the modeling with the aim at predicting the granule particle size and executing by ANFIS and ANN. ANN holds the promise of being capable of producing non-linear models, being able to work under noise conditions, and being fault tolerant to the loss of neurons or connections. Also, the ANFIS approach combines the advantages of fuzzy system and artificial neural network to design architecture and is capable of dealing with both limitation and complexity in the data set. The efficiencies of ANFIS and ANN approaches in prediction are compared and the superior approach is selected. Finally, by deploying the preferred approach, several scenarios are presented to be used in predictive control of spray drying as an accurate, fast running, and inexpensive tool. This is the first study that presents a flexible intelligent approach for predictive control of drying process by ANN, ANFIS, and PLS. The approach of this study may be easily applied to other production process.

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