Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach
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 necessar...
| Main Authors: | , , , |
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| Format: | Journal Article |
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Springer London
2012
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| Online Access: | http://www.springerlink.com/content/382653l46t17n7kr/ http://hdl.handle.net/20.500.11937/49334 |
| _version_ | 1848758218625384448 |
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| author | Azadeh, A. Neshat, N. Kazemi, A. Saberi, Morteza |
| author_facet | Azadeh, A. Neshat, N. Kazemi, A. Saberi, Morteza |
| author_sort | Azadeh, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T09:40:30Z |
| format | Journal Article |
| id | curtin-20.500.11937-49334 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:40:30Z |
| publishDate | 2012 |
| publisher | Springer London |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-493342017-03-15T22:56:12Z Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach Azadeh, A. Neshat, N. Kazemi, A. Saberi, Morteza Spray-drying process Artificial neural networks Predictive control Neuro-fuzzy inference system Partial least squares 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. 2012 Journal Article http://hdl.handle.net/20.500.11937/49334 http://www.springerlink.com/content/382653l46t17n7kr/ Springer London restricted |
| spellingShingle | Spray-drying process Artificial neural networks Predictive control Neuro-fuzzy inference system Partial least squares Azadeh, A. Neshat, N. Kazemi, A. Saberi, Morteza Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach |
| title | Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach |
| title_full | Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach |
| title_fullStr | Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach |
| title_full_unstemmed | Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach |
| title_short | Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach |
| title_sort | predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach |
| topic | Spray-drying process Artificial neural networks Predictive control Neuro-fuzzy inference system Partial least squares |
| url | http://www.springerlink.com/content/382653l46t17n7kr/ http://hdl.handle.net/20.500.11937/49334 |