COMPARISON OF RESPONSE SURFACE METHODOLOGY (RSM) AND ARTIFICIAL NEURAL NETWORKS (ANN) TOWARDS EFFICIENT OPTIMIZATION OF FLEXURAL PROPERTIES OF GYPSUM-BONDED FIBERBOARDS
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| Natura: | Artículo científico |
| Lingua: | en |
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Universidade Federal de Lavras
2018
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| _version_ | 1876427516344795136 |
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| author | Morteza Nazerian |
| author_facet | Morteza Nazerian |
| contents | COMPARISON OF RESPONSE SURFACE METHODOLOGY (RSM) AND ARTIFICIAL NEURAL NETWORKS (ANN) TOWARDS EFFICIENT OPTIMIZATION OF FLEXURAL PROPERTIES OF GYPSUM-BONDED FIBERBOARDS Morteza Nazerian Meysam Kamyabb Mohammad Shamsianb Mohammad Dahmardehb Mojtaba Kooshaa Agrociencias RSM ANN MOR Kenaf Gypsum In this study, the hydration behavior of gypsum paste mixed with bagasse and kenaf fibers as lignocellulosic material and fiberglass as inorganic material is evaluated. Moreover, the properties of gypsum-bonded fiberboard (GBFB) are examined using bagasse fibers (Saccharum officinarum.L), kenaf fibers (Hibiscus cannabinus.L) and industrial fiberglass. The weight ratios of fiberglass (at three levels 0, 3 and 6%), bagasse fiber (at three levels 0, 7.5 and 15%) and kenaf fiber (at three levels 0, 7.5 and 15%) to gypsum are used to make the gypsum-bonded fiberboard with the nominal density 1.10 g.cm-3 . After preparing the fiberboard, its flexural properties were examined. Response surface methodology (RSM) and artificial neural network (ANN) were used to model the bending strength of gypsum-bonded fiberboard. According to the hydration tests, it was determined that as the extractives in the lignocellulosic materials increased, the temperature of the mixture decreased and its setting time increased. According to the bending test results, it was determined that there is an ideal consistency between the predicted values and the observed data, so that as bagasse and kenaf fiber increased, the modulus of rupture (MOR) increased. Maximum MOR of panel was predicted to be 10.81 MPa and 11MPa by RSM and ANN at optimum condition. Based on the statistical analysis, the training and validation data sets of the studied models were compared by the coefficient of determination (R2), root mean squares error (RMSE) and mean absolute error (MAE). ANN model showed a much more accurate prediction than RSM in terms of the values R2, RMSE and MAE. 2018 artículo científico 0104-7760 https://www.redalyc.org/articulo.oa?id=74460165005 https://www.redalyc.org/journal/744/74460165005/ https://www.redalyc.org/journal/744/74460165005/html/ https://www.redalyc.org/journal/744/74460165005/74460165005.epub https://www.redalyc.org/journal/744/74460165005/movil 10.1590/01047760201824012484 en http://www.redalyc.org/revista.oa?id=744 CERNE application/pdf Universidade Federal de Lavras CERNE (Brasil) Num.1 Vol.24 |
| format | Artículo científico |
| id | redalyc_74460165005 |
| institution | Redalyc |
| language | en |
| publishDate | 2018 |
| publisher | Universidade Federal de Lavras |
| spellingShingle | COMPARISON OF RESPONSE SURFACE METHODOLOGY (RSM) AND ARTIFICIAL NEURAL NETWORKS (ANN) TOWARDS EFFICIENT OPTIMIZATION OF FLEXURAL PROPERTIES OF GYPSUM-BONDED FIBERBOARDS Morteza Nazerian Agrociencias RSM ANN MOR Kenaf Gypsum COMPARISON OF RESPONSE SURFACE METHODOLOGY (RSM) AND ARTIFICIAL NEURAL NETWORKS (ANN) TOWARDS EFFICIENT OPTIMIZATION OF FLEXURAL PROPERTIES OF GYPSUM-BONDED FIBERBOARDS Morteza Nazerian Meysam Kamyabb Mohammad Shamsianb Mohammad Dahmardehb Mojtaba Kooshaa Agrociencias RSM ANN MOR Kenaf Gypsum In this study, the hydration behavior of gypsum paste mixed with bagasse and kenaf fibers as lignocellulosic material and fiberglass as inorganic material is evaluated. Moreover, the properties of gypsum-bonded fiberboard (GBFB) are examined using bagasse fibers (Saccharum officinarum.L), kenaf fibers (Hibiscus cannabinus.L) and industrial fiberglass. The weight ratios of fiberglass (at three levels 0, 3 and 6%), bagasse fiber (at three levels 0, 7.5 and 15%) and kenaf fiber (at three levels 0, 7.5 and 15%) to gypsum are used to make the gypsum-bonded fiberboard with the nominal density 1.10 g.cm-3 . After preparing the fiberboard, its flexural properties were examined. Response surface methodology (RSM) and artificial neural network (ANN) were used to model the bending strength of gypsum-bonded fiberboard. According to the hydration tests, it was determined that as the extractives in the lignocellulosic materials increased, the temperature of the mixture decreased and its setting time increased. According to the bending test results, it was determined that there is an ideal consistency between the predicted values and the observed data, so that as bagasse and kenaf fiber increased, the modulus of rupture (MOR) increased. Maximum MOR of panel was predicted to be 10.81 MPa and 11MPa by RSM and ANN at optimum condition. Based on the statistical analysis, the training and validation data sets of the studied models were compared by the coefficient of determination (R2), root mean squares error (RMSE) and mean absolute error (MAE). ANN model showed a much more accurate prediction than RSM in terms of the values R2, RMSE and MAE. 2018 artículo científico 0104-7760 https://www.redalyc.org/articulo.oa?id=74460165005 https://www.redalyc.org/journal/744/74460165005/ https://www.redalyc.org/journal/744/74460165005/html/ https://www.redalyc.org/journal/744/74460165005/74460165005.epub https://www.redalyc.org/journal/744/74460165005/movil 10.1590/01047760201824012484 en http://www.redalyc.org/revista.oa?id=744 CERNE application/pdf Universidade Federal de Lavras CERNE (Brasil) Num.1 Vol.24 |
| title | COMPARISON OF RESPONSE SURFACE METHODOLOGY (RSM) AND ARTIFICIAL NEURAL NETWORKS (ANN) TOWARDS EFFICIENT OPTIMIZATION OF FLEXURAL PROPERTIES OF GYPSUM-BONDED FIBERBOARDS |
| topic | Agrociencias RSM ANN MOR Kenaf Gypsum |
| url | https://www.redalyc.org/articulo.oa?id=74460165005 https://www.redalyc.org/journal/744/74460165005/ https://www.redalyc.org/journal/744/74460165005/html/ https://www.redalyc.org/journal/744/74460165005/74460165005.epub https://www.redalyc.org/journal/744/74460165005/movil |