Comparative assessment of artificial neural network and response surface methodology for evaluation of the predictive capability on bio-oil yield of Tithonia diversifolia pyrolysis
Abstract The present study aims to explore the thermochemical conversion feasibility of an unexplored weed biomass, Tithonia diversifolia. In this investigation, the predictive ability of response surface model (RSM) and artificial neural network (ANN) models for bio-oil yield from pyrolysis of Tith...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bhuyan, Nilutpal [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Biomass Conversion and Biorefinery - Berlin : Springer, 2011, 12(2020), 6 vom: 18. Juni, Seite 2203-2218 |
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| Übergeordnetes Werk: |
volume:12 ; year:2020 ; number:6 ; day:18 ; month:06 ; pages:2203-2218 |
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| DOI / URN: |
10.1007/s13399-020-00806-x |
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| Katalog-ID: |
SPR04706191X |
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| 520 | |a Abstract The present study aims to explore the thermochemical conversion feasibility of an unexplored weed biomass, Tithonia diversifolia. In this investigation, the predictive ability of response surface model (RSM) and artificial neural network (ANN) models for bio-oil yield from pyrolysis of Tithonia Diversifolia was carried out in a fixed bed reactor. The influence of process parameters which included temperature (375–675 °C), heating rate (10–50 °C/min), nitrogen flow rate (50–250 mL/min), and particle size (< 0.25– > 1 mm) on bio-oil yield was investigated by central composite design (CCD) of RSM and feedforward backpropagation neural network with different topology. Based on statistical value for coefficient of determination (R2), mean average error (MAE), root mean square error (RMSE), standard error of prediction (SEP), and absolute average deviation (AAD), it was found that the ANN was more efficient than the RSM model for predicting bio-oil yield. The bio-oil obtained was analyzed for different physical and chemical properties which revealed that the bio-oil can be used as renewable fuel and chemicals. | ||
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10.1007/s13399-020-00806-x doi (DE-627)SPR04706191X (SPR)s13399-020-00806-x-e DE-627 ger DE-627 rakwb eng Bhuyan, Nilutpal verfasserin aut Comparative assessment of artificial neural network and response surface methodology for evaluation of the predictive capability on bio-oil yield of Tithonia diversifolia pyrolysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract The present study aims to explore the thermochemical conversion feasibility of an unexplored weed biomass, Tithonia diversifolia. In this investigation, the predictive ability of response surface model (RSM) and artificial neural network (ANN) models for bio-oil yield from pyrolysis of Tithonia Diversifolia was carried out in a fixed bed reactor. The influence of process parameters which included temperature (375–675 °C), heating rate (10–50 °C/min), nitrogen flow rate (50–250 mL/min), and particle size (< 0.25– > 1 mm) on bio-oil yield was investigated by central composite design (CCD) of RSM and feedforward backpropagation neural network with different topology. Based on statistical value for coefficient of determination (R2), mean average error (MAE), root mean square error (RMSE), standard error of prediction (SEP), and absolute average deviation (AAD), it was found that the ANN was more efficient than the RSM model for predicting bio-oil yield. The bio-oil obtained was analyzed for different physical and chemical properties which revealed that the bio-oil can be used as renewable fuel and chemicals. Pyrolysis (dpeaa)DE-He213 Fixed bed reactor (dpeaa)DE-He213 Response surface methodology (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Narzari, Rumi aut Bujar Baruah, Satyabrat Malla aut Kataki, Rupam (orcid)0000-0003-0114-3858 aut Enthalten in Biomass Conversion and Biorefinery Berlin : Springer, 2011 12(2020), 6 vom: 18. Juni, Seite 2203-2218 (DE-627)645092843 (DE-600)2592298-1 2190-6823 nnns volume:12 year:2020 number:6 day:18 month:06 pages:2203-2218 https://dx.doi.org/10.1007/s13399-020-00806-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2020 6 18 06 2203-2218 |
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10.1007/s13399-020-00806-x doi (DE-627)SPR04706191X (SPR)s13399-020-00806-x-e DE-627 ger DE-627 rakwb eng Bhuyan, Nilutpal verfasserin aut Comparative assessment of artificial neural network and response surface methodology for evaluation of the predictive capability on bio-oil yield of Tithonia diversifolia pyrolysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract The present study aims to explore the thermochemical conversion feasibility of an unexplored weed biomass, Tithonia diversifolia. In this investigation, the predictive ability of response surface model (RSM) and artificial neural network (ANN) models for bio-oil yield from pyrolysis of Tithonia Diversifolia was carried out in a fixed bed reactor. The influence of process parameters which included temperature (375–675 °C), heating rate (10–50 °C/min), nitrogen flow rate (50–250 mL/min), and particle size (< 0.25– > 1 mm) on bio-oil yield was investigated by central composite design (CCD) of RSM and feedforward backpropagation neural network with different topology. Based on statistical value for coefficient of determination (R2), mean average error (MAE), root mean square error (RMSE), standard error of prediction (SEP), and absolute average deviation (AAD), it was found that the ANN was more efficient than the RSM model for predicting bio-oil yield. The bio-oil obtained was analyzed for different physical and chemical properties which revealed that the bio-oil can be used as renewable fuel and chemicals. Pyrolysis (dpeaa)DE-He213 Fixed bed reactor (dpeaa)DE-He213 Response surface methodology (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Narzari, Rumi aut Bujar Baruah, Satyabrat Malla aut Kataki, Rupam (orcid)0000-0003-0114-3858 aut Enthalten in Biomass Conversion and Biorefinery Berlin : Springer, 2011 12(2020), 6 vom: 18. Juni, Seite 2203-2218 (DE-627)645092843 (DE-600)2592298-1 2190-6823 nnns volume:12 year:2020 number:6 day:18 month:06 pages:2203-2218 https://dx.doi.org/10.1007/s13399-020-00806-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2020 6 18 06 2203-2218 |
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10.1007/s13399-020-00806-x doi (DE-627)SPR04706191X (SPR)s13399-020-00806-x-e DE-627 ger DE-627 rakwb eng Bhuyan, Nilutpal verfasserin aut Comparative assessment of artificial neural network and response surface methodology for evaluation of the predictive capability on bio-oil yield of Tithonia diversifolia pyrolysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract The present study aims to explore the thermochemical conversion feasibility of an unexplored weed biomass, Tithonia diversifolia. In this investigation, the predictive ability of response surface model (RSM) and artificial neural network (ANN) models for bio-oil yield from pyrolysis of Tithonia Diversifolia was carried out in a fixed bed reactor. The influence of process parameters which included temperature (375–675 °C), heating rate (10–50 °C/min), nitrogen flow rate (50–250 mL/min), and particle size (< 0.25– > 1 mm) on bio-oil yield was investigated by central composite design (CCD) of RSM and feedforward backpropagation neural network with different topology. Based on statistical value for coefficient of determination (R2), mean average error (MAE), root mean square error (RMSE), standard error of prediction (SEP), and absolute average deviation (AAD), it was found that the ANN was more efficient than the RSM model for predicting bio-oil yield. The bio-oil obtained was analyzed for different physical and chemical properties which revealed that the bio-oil can be used as renewable fuel and chemicals. Pyrolysis (dpeaa)DE-He213 Fixed bed reactor (dpeaa)DE-He213 Response surface methodology (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Narzari, Rumi aut Bujar Baruah, Satyabrat Malla aut Kataki, Rupam (orcid)0000-0003-0114-3858 aut Enthalten in Biomass Conversion and Biorefinery Berlin : Springer, 2011 12(2020), 6 vom: 18. Juni, Seite 2203-2218 (DE-627)645092843 (DE-600)2592298-1 2190-6823 nnns volume:12 year:2020 number:6 day:18 month:06 pages:2203-2218 https://dx.doi.org/10.1007/s13399-020-00806-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2020 6 18 06 2203-2218 |
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10.1007/s13399-020-00806-x doi (DE-627)SPR04706191X (SPR)s13399-020-00806-x-e DE-627 ger DE-627 rakwb eng Bhuyan, Nilutpal verfasserin aut Comparative assessment of artificial neural network and response surface methodology for evaluation of the predictive capability on bio-oil yield of Tithonia diversifolia pyrolysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract The present study aims to explore the thermochemical conversion feasibility of an unexplored weed biomass, Tithonia diversifolia. In this investigation, the predictive ability of response surface model (RSM) and artificial neural network (ANN) models for bio-oil yield from pyrolysis of Tithonia Diversifolia was carried out in a fixed bed reactor. The influence of process parameters which included temperature (375–675 °C), heating rate (10–50 °C/min), nitrogen flow rate (50–250 mL/min), and particle size (< 0.25– > 1 mm) on bio-oil yield was investigated by central composite design (CCD) of RSM and feedforward backpropagation neural network with different topology. Based on statistical value for coefficient of determination (R2), mean average error (MAE), root mean square error (RMSE), standard error of prediction (SEP), and absolute average deviation (AAD), it was found that the ANN was more efficient than the RSM model for predicting bio-oil yield. The bio-oil obtained was analyzed for different physical and chemical properties which revealed that the bio-oil can be used as renewable fuel and chemicals. Pyrolysis (dpeaa)DE-He213 Fixed bed reactor (dpeaa)DE-He213 Response surface methodology (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Narzari, Rumi aut Bujar Baruah, Satyabrat Malla aut Kataki, Rupam (orcid)0000-0003-0114-3858 aut Enthalten in Biomass Conversion and Biorefinery Berlin : Springer, 2011 12(2020), 6 vom: 18. Juni, Seite 2203-2218 (DE-627)645092843 (DE-600)2592298-1 2190-6823 nnns volume:12 year:2020 number:6 day:18 month:06 pages:2203-2218 https://dx.doi.org/10.1007/s13399-020-00806-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2020 6 18 06 2203-2218 |
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10.1007/s13399-020-00806-x doi (DE-627)SPR04706191X (SPR)s13399-020-00806-x-e DE-627 ger DE-627 rakwb eng Bhuyan, Nilutpal verfasserin aut Comparative assessment of artificial neural network and response surface methodology for evaluation of the predictive capability on bio-oil yield of Tithonia diversifolia pyrolysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract The present study aims to explore the thermochemical conversion feasibility of an unexplored weed biomass, Tithonia diversifolia. In this investigation, the predictive ability of response surface model (RSM) and artificial neural network (ANN) models for bio-oil yield from pyrolysis of Tithonia Diversifolia was carried out in a fixed bed reactor. The influence of process parameters which included temperature (375–675 °C), heating rate (10–50 °C/min), nitrogen flow rate (50–250 mL/min), and particle size (< 0.25– > 1 mm) on bio-oil yield was investigated by central composite design (CCD) of RSM and feedforward backpropagation neural network with different topology. Based on statistical value for coefficient of determination (R2), mean average error (MAE), root mean square error (RMSE), standard error of prediction (SEP), and absolute average deviation (AAD), it was found that the ANN was more efficient than the RSM model for predicting bio-oil yield. The bio-oil obtained was analyzed for different physical and chemical properties which revealed that the bio-oil can be used as renewable fuel and chemicals. Pyrolysis (dpeaa)DE-He213 Fixed bed reactor (dpeaa)DE-He213 Response surface methodology (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Narzari, Rumi aut Bujar Baruah, Satyabrat Malla aut Kataki, Rupam (orcid)0000-0003-0114-3858 aut Enthalten in Biomass Conversion and Biorefinery Berlin : Springer, 2011 12(2020), 6 vom: 18. Juni, Seite 2203-2218 (DE-627)645092843 (DE-600)2592298-1 2190-6823 nnns volume:12 year:2020 number:6 day:18 month:06 pages:2203-2218 https://dx.doi.org/10.1007/s13399-020-00806-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2020 6 18 06 2203-2218 |
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Bhuyan, Nilutpal @@aut@@ Narzari, Rumi @@aut@@ Bujar Baruah, Satyabrat Malla @@aut@@ Kataki, Rupam @@aut@@ |
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In this investigation, the predictive ability of response surface model (RSM) and artificial neural network (ANN) models for bio-oil yield from pyrolysis of Tithonia Diversifolia was carried out in a fixed bed reactor. The influence of process parameters which included temperature (375–675 °C), heating rate (10–50 °C/min), nitrogen flow rate (50–250 mL/min), and particle size (< 0.25– > 1 mm) on bio-oil yield was investigated by central composite design (CCD) of RSM and feedforward backpropagation neural network with different topology. Based on statistical value for coefficient of determination (R2), mean average error (MAE), root mean square error (RMSE), standard error of prediction (SEP), and absolute average deviation (AAD), it was found that the ANN was more efficient than the RSM model for predicting bio-oil yield. 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Bhuyan, Nilutpal |
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Comparative assessment of artificial neural network and response surface methodology for evaluation of the predictive capability on bio-oil yield of Tithonia diversifolia pyrolysis Pyrolysis (dpeaa)DE-He213 Fixed bed reactor (dpeaa)DE-He213 Response surface methodology (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 |
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Comparative assessment of artificial neural network and response surface methodology for evaluation of the predictive capability on bio-oil yield of Tithonia diversifolia pyrolysis |
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Comparative assessment of artificial neural network and response surface methodology for evaluation of the predictive capability on bio-oil yield of Tithonia diversifolia pyrolysis |
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comparative assessment of artificial neural network and response surface methodology for evaluation of the predictive capability on bio-oil yield of tithonia diversifolia pyrolysis |
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Comparative assessment of artificial neural network and response surface methodology for evaluation of the predictive capability on bio-oil yield of Tithonia diversifolia pyrolysis |
| abstract |
Abstract The present study aims to explore the thermochemical conversion feasibility of an unexplored weed biomass, Tithonia diversifolia. In this investigation, the predictive ability of response surface model (RSM) and artificial neural network (ANN) models for bio-oil yield from pyrolysis of Tithonia Diversifolia was carried out in a fixed bed reactor. The influence of process parameters which included temperature (375–675 °C), heating rate (10–50 °C/min), nitrogen flow rate (50–250 mL/min), and particle size (< 0.25– > 1 mm) on bio-oil yield was investigated by central composite design (CCD) of RSM and feedforward backpropagation neural network with different topology. Based on statistical value for coefficient of determination (R2), mean average error (MAE), root mean square error (RMSE), standard error of prediction (SEP), and absolute average deviation (AAD), it was found that the ANN was more efficient than the RSM model for predicting bio-oil yield. The bio-oil obtained was analyzed for different physical and chemical properties which revealed that the bio-oil can be used as renewable fuel and chemicals. © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
| abstractGer |
Abstract The present study aims to explore the thermochemical conversion feasibility of an unexplored weed biomass, Tithonia diversifolia. In this investigation, the predictive ability of response surface model (RSM) and artificial neural network (ANN) models for bio-oil yield from pyrolysis of Tithonia Diversifolia was carried out in a fixed bed reactor. The influence of process parameters which included temperature (375–675 °C), heating rate (10–50 °C/min), nitrogen flow rate (50–250 mL/min), and particle size (< 0.25– > 1 mm) on bio-oil yield was investigated by central composite design (CCD) of RSM and feedforward backpropagation neural network with different topology. Based on statistical value for coefficient of determination (R2), mean average error (MAE), root mean square error (RMSE), standard error of prediction (SEP), and absolute average deviation (AAD), it was found that the ANN was more efficient than the RSM model for predicting bio-oil yield. The bio-oil obtained was analyzed for different physical and chemical properties which revealed that the bio-oil can be used as renewable fuel and chemicals. © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
| abstract_unstemmed |
Abstract The present study aims to explore the thermochemical conversion feasibility of an unexplored weed biomass, Tithonia diversifolia. In this investigation, the predictive ability of response surface model (RSM) and artificial neural network (ANN) models for bio-oil yield from pyrolysis of Tithonia Diversifolia was carried out in a fixed bed reactor. The influence of process parameters which included temperature (375–675 °C), heating rate (10–50 °C/min), nitrogen flow rate (50–250 mL/min), and particle size (< 0.25– > 1 mm) on bio-oil yield was investigated by central composite design (CCD) of RSM and feedforward backpropagation neural network with different topology. Based on statistical value for coefficient of determination (R2), mean average error (MAE), root mean square error (RMSE), standard error of prediction (SEP), and absolute average deviation (AAD), it was found that the ANN was more efficient than the RSM model for predicting bio-oil yield. The bio-oil obtained was analyzed for different physical and chemical properties which revealed that the bio-oil can be used as renewable fuel and chemicals. © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
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| title_short |
Comparative assessment of artificial neural network and response surface methodology for evaluation of the predictive capability on bio-oil yield of Tithonia diversifolia pyrolysis |
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https://dx.doi.org/10.1007/s13399-020-00806-x |
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Narzari, Rumi Bujar Baruah, Satyabrat Malla Kataki, Rupam |
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10.1007/s13399-020-00806-x |
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2024-07-04T01:42:00.176Z |
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| score |
7.401681 |