Modeling tool using neural networks for l(+)-lactic acid production by pellet-form Rhizopus oryzae NRRL 395 on biodiesel crude glycerol

Abstract Most chemical reactions produce unwanted by-products. In an effort to reduce environmental problems these by-products could be used to produce valuable organic chemicals. In biodiesel industry a huge amount of glycerol is generated, approximately 10% of the final product. The research group...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Dulf, Eva-H. [verfasserIn] Vodnar, Dan Cristian Dulf, Francisc-V.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Software application Neural network Biodiesel Predictive model

Anmerkung:	© The Author(s) 2018

Übergeordnetes Werk:	Enthalten in: Chemistry central journal - London : BioMed Central, 2007, 12(2018), 1 vom: 29. Nov.
Übergeordnetes Werk:	volume:12 ; year:2018 ; number:1 ; day:29 ; month:11

Links:	Volltext

DOI / URN:	10.1186/s13065-018-0491-5

Katalog-ID:	SPR030136822

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allfields	10.1186/s13065-018-0491-5 doi (DE-627)SPR030136822 (SPR)s13065-018-0491-5-e DE-627 ger DE-627 rakwb eng Dulf, Eva-H. verfasserin (orcid)0000-0002-6540-6525 aut Modeling tool using neural networks for l(+)-lactic acid production by pellet-form Rhizopus oryzae NRRL 395 on biodiesel crude glycerol 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract Most chemical reactions produce unwanted by-products. In an effort to reduce environmental problems these by-products could be used to produce valuable organic chemicals. In biodiesel industry a huge amount of glycerol is generated, approximately 10% of the final product. The research group from University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca developed opportunities to produce l(+) lactic acid from the glycerol. The team is using the Rhizopus oryzae NRRL 395 bacteria for the fermentation of the glycerol. The purpose of the research is to improve the production of l(+) lactic acid in order to optimize the process. A predictive model obtained by neural networks is useful in this case. The main objective of the present work is to present the developed user-friendly application useful in modeling this fermentation process, in order to be used by people who are inexperienced with neural networks or specific software. Besides the interface for training of a new neural network in order to develop the model in some characteristic condition, the software also provides an interface for visualization of the results, useful in interpretation and as a tool for prediction. Software application (dpeaa)DE-He213 Neural network (dpeaa)DE-He213 Biodiesel (dpeaa)DE-He213 Predictive model (dpeaa)DE-He213 Vodnar, Dan Cristian aut Dulf, Francisc-V. aut Enthalten in Chemistry central journal London : BioMed Central, 2007 12(2018), 1 vom: 29. Nov. (DE-627)525475176 (DE-600)2272440-0 1752-153X nnns volume:12 year:2018 number:1 day:29 month:11 https://dx.doi.org/10.1186/s13065-018-0491-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2018 1 29 11
spelling	10.1186/s13065-018-0491-5 doi (DE-627)SPR030136822 (SPR)s13065-018-0491-5-e DE-627 ger DE-627 rakwb eng Dulf, Eva-H. verfasserin (orcid)0000-0002-6540-6525 aut Modeling tool using neural networks for l(+)-lactic acid production by pellet-form Rhizopus oryzae NRRL 395 on biodiesel crude glycerol 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract Most chemical reactions produce unwanted by-products. In an effort to reduce environmental problems these by-products could be used to produce valuable organic chemicals. In biodiesel industry a huge amount of glycerol is generated, approximately 10% of the final product. The research group from University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca developed opportunities to produce l(+) lactic acid from the glycerol. The team is using the Rhizopus oryzae NRRL 395 bacteria for the fermentation of the glycerol. The purpose of the research is to improve the production of l(+) lactic acid in order to optimize the process. A predictive model obtained by neural networks is useful in this case. The main objective of the present work is to present the developed user-friendly application useful in modeling this fermentation process, in order to be used by people who are inexperienced with neural networks or specific software. Besides the interface for training of a new neural network in order to develop the model in some characteristic condition, the software also provides an interface for visualization of the results, useful in interpretation and as a tool for prediction. Software application (dpeaa)DE-He213 Neural network (dpeaa)DE-He213 Biodiesel (dpeaa)DE-He213 Predictive model (dpeaa)DE-He213 Vodnar, Dan Cristian aut Dulf, Francisc-V. aut Enthalten in Chemistry central journal London : BioMed Central, 2007 12(2018), 1 vom: 29. Nov. (DE-627)525475176 (DE-600)2272440-0 1752-153X nnns volume:12 year:2018 number:1 day:29 month:11 https://dx.doi.org/10.1186/s13065-018-0491-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2018 1 29 11
allfields_unstemmed	10.1186/s13065-018-0491-5 doi (DE-627)SPR030136822 (SPR)s13065-018-0491-5-e DE-627 ger DE-627 rakwb eng Dulf, Eva-H. verfasserin (orcid)0000-0002-6540-6525 aut Modeling tool using neural networks for l(+)-lactic acid production by pellet-form Rhizopus oryzae NRRL 395 on biodiesel crude glycerol 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract Most chemical reactions produce unwanted by-products. In an effort to reduce environmental problems these by-products could be used to produce valuable organic chemicals. In biodiesel industry a huge amount of glycerol is generated, approximately 10% of the final product. The research group from University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca developed opportunities to produce l(+) lactic acid from the glycerol. The team is using the Rhizopus oryzae NRRL 395 bacteria for the fermentation of the glycerol. The purpose of the research is to improve the production of l(+) lactic acid in order to optimize the process. A predictive model obtained by neural networks is useful in this case. The main objective of the present work is to present the developed user-friendly application useful in modeling this fermentation process, in order to be used by people who are inexperienced with neural networks or specific software. Besides the interface for training of a new neural network in order to develop the model in some characteristic condition, the software also provides an interface for visualization of the results, useful in interpretation and as a tool for prediction. Software application (dpeaa)DE-He213 Neural network (dpeaa)DE-He213 Biodiesel (dpeaa)DE-He213 Predictive model (dpeaa)DE-He213 Vodnar, Dan Cristian aut Dulf, Francisc-V. aut Enthalten in Chemistry central journal London : BioMed Central, 2007 12(2018), 1 vom: 29. Nov. (DE-627)525475176 (DE-600)2272440-0 1752-153X nnns volume:12 year:2018 number:1 day:29 month:11 https://dx.doi.org/10.1186/s13065-018-0491-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2018 1 29 11
allfieldsGer	10.1186/s13065-018-0491-5 doi (DE-627)SPR030136822 (SPR)s13065-018-0491-5-e DE-627 ger DE-627 rakwb eng Dulf, Eva-H. verfasserin (orcid)0000-0002-6540-6525 aut Modeling tool using neural networks for l(+)-lactic acid production by pellet-form Rhizopus oryzae NRRL 395 on biodiesel crude glycerol 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract Most chemical reactions produce unwanted by-products. In an effort to reduce environmental problems these by-products could be used to produce valuable organic chemicals. In biodiesel industry a huge amount of glycerol is generated, approximately 10% of the final product. The research group from University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca developed opportunities to produce l(+) lactic acid from the glycerol. The team is using the Rhizopus oryzae NRRL 395 bacteria for the fermentation of the glycerol. The purpose of the research is to improve the production of l(+) lactic acid in order to optimize the process. A predictive model obtained by neural networks is useful in this case. The main objective of the present work is to present the developed user-friendly application useful in modeling this fermentation process, in order to be used by people who are inexperienced with neural networks or specific software. Besides the interface for training of a new neural network in order to develop the model in some characteristic condition, the software also provides an interface for visualization of the results, useful in interpretation and as a tool for prediction. Software application (dpeaa)DE-He213 Neural network (dpeaa)DE-He213 Biodiesel (dpeaa)DE-He213 Predictive model (dpeaa)DE-He213 Vodnar, Dan Cristian aut Dulf, Francisc-V. aut Enthalten in Chemistry central journal London : BioMed Central, 2007 12(2018), 1 vom: 29. Nov. (DE-627)525475176 (DE-600)2272440-0 1752-153X nnns volume:12 year:2018 number:1 day:29 month:11 https://dx.doi.org/10.1186/s13065-018-0491-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2018 1 29 11
allfieldsSound	10.1186/s13065-018-0491-5 doi (DE-627)SPR030136822 (SPR)s13065-018-0491-5-e DE-627 ger DE-627 rakwb eng Dulf, Eva-H. verfasserin (orcid)0000-0002-6540-6525 aut Modeling tool using neural networks for l(+)-lactic acid production by pellet-form Rhizopus oryzae NRRL 395 on biodiesel crude glycerol 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract Most chemical reactions produce unwanted by-products. In an effort to reduce environmental problems these by-products could be used to produce valuable organic chemicals. In biodiesel industry a huge amount of glycerol is generated, approximately 10% of the final product. The research group from University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca developed opportunities to produce l(+) lactic acid from the glycerol. The team is using the Rhizopus oryzae NRRL 395 bacteria for the fermentation of the glycerol. The purpose of the research is to improve the production of l(+) lactic acid in order to optimize the process. A predictive model obtained by neural networks is useful in this case. The main objective of the present work is to present the developed user-friendly application useful in modeling this fermentation process, in order to be used by people who are inexperienced with neural networks or specific software. Besides the interface for training of a new neural network in order to develop the model in some characteristic condition, the software also provides an interface for visualization of the results, useful in interpretation and as a tool for prediction. Software application (dpeaa)DE-He213 Neural network (dpeaa)DE-He213 Biodiesel (dpeaa)DE-He213 Predictive model (dpeaa)DE-He213 Vodnar, Dan Cristian aut Dulf, Francisc-V. aut Enthalten in Chemistry central journal London : BioMed Central, 2007 12(2018), 1 vom: 29. Nov. (DE-627)525475176 (DE-600)2272440-0 1752-153X nnns volume:12 year:2018 number:1 day:29 month:11 https://dx.doi.org/10.1186/s13065-018-0491-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2018 1 29 11
language	English
source	Enthalten in Chemistry central journal 12(2018), 1 vom: 29. Nov. volume:12 year:2018 number:1 day:29 month:11
sourceStr	Enthalten in Chemistry central journal 12(2018), 1 vom: 29. Nov. volume:12 year:2018 number:1 day:29 month:11
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topic_facet	Software application Neural network Biodiesel Predictive model
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authorswithroles_txt_mv	Dulf, Eva-H. @@aut@@ Vodnar, Dan Cristian @@aut@@ Dulf, Francisc-V. @@aut@@
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author	Dulf, Eva-H.
spellingShingle	Dulf, Eva-H. misc Software application misc Neural network misc Biodiesel misc Predictive model Modeling tool using neural networks for l(+)-lactic acid production by pellet-form Rhizopus oryzae NRRL 395 on biodiesel crude glycerol
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topic_title	Modeling tool using neural networks for l(+)-lactic acid production by pellet-form Rhizopus oryzae NRRL 395 on biodiesel crude glycerol Software application (dpeaa)DE-He213 Neural network (dpeaa)DE-He213 Biodiesel (dpeaa)DE-He213 Predictive model (dpeaa)DE-He213
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title	Modeling tool using neural networks for l(+)-lactic acid production by pellet-form Rhizopus oryzae NRRL 395 on biodiesel crude glycerol
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title_full	Modeling tool using neural networks for l(+)-lactic acid production by pellet-form Rhizopus oryzae NRRL 395 on biodiesel crude glycerol
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title_sort	modeling tool using neural networks for l(+)-lactic acid production by pellet-form rhizopus oryzae nrrl 395 on biodiesel crude glycerol
title_auth	Modeling tool using neural networks for l(+)-lactic acid production by pellet-form Rhizopus oryzae NRRL 395 on biodiesel crude glycerol
abstract	Abstract Most chemical reactions produce unwanted by-products. In an effort to reduce environmental problems these by-products could be used to produce valuable organic chemicals. In biodiesel industry a huge amount of glycerol is generated, approximately 10% of the final product. The research group from University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca developed opportunities to produce l(+) lactic acid from the glycerol. The team is using the Rhizopus oryzae NRRL 395 bacteria for the fermentation of the glycerol. The purpose of the research is to improve the production of l(+) lactic acid in order to optimize the process. A predictive model obtained by neural networks is useful in this case. The main objective of the present work is to present the developed user-friendly application useful in modeling this fermentation process, in order to be used by people who are inexperienced with neural networks or specific software. Besides the interface for training of a new neural network in order to develop the model in some characteristic condition, the software also provides an interface for visualization of the results, useful in interpretation and as a tool for prediction. © The Author(s) 2018
abstractGer	Abstract Most chemical reactions produce unwanted by-products. In an effort to reduce environmental problems these by-products could be used to produce valuable organic chemicals. In biodiesel industry a huge amount of glycerol is generated, approximately 10% of the final product. The research group from University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca developed opportunities to produce l(+) lactic acid from the glycerol. The team is using the Rhizopus oryzae NRRL 395 bacteria for the fermentation of the glycerol. The purpose of the research is to improve the production of l(+) lactic acid in order to optimize the process. A predictive model obtained by neural networks is useful in this case. The main objective of the present work is to present the developed user-friendly application useful in modeling this fermentation process, in order to be used by people who are inexperienced with neural networks or specific software. Besides the interface for training of a new neural network in order to develop the model in some characteristic condition, the software also provides an interface for visualization of the results, useful in interpretation and as a tool for prediction. © The Author(s) 2018
abstract_unstemmed	Abstract Most chemical reactions produce unwanted by-products. In an effort to reduce environmental problems these by-products could be used to produce valuable organic chemicals. In biodiesel industry a huge amount of glycerol is generated, approximately 10% of the final product. The research group from University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca developed opportunities to produce l(+) lactic acid from the glycerol. The team is using the Rhizopus oryzae NRRL 395 bacteria for the fermentation of the glycerol. The purpose of the research is to improve the production of l(+) lactic acid in order to optimize the process. A predictive model obtained by neural networks is useful in this case. The main objective of the present work is to present the developed user-friendly application useful in modeling this fermentation process, in order to be used by people who are inexperienced with neural networks or specific software. Besides the interface for training of a new neural network in order to develop the model in some characteristic condition, the software also provides an interface for visualization of the results, useful in interpretation and as a tool for prediction. © The Author(s) 2018
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title_short	Modeling tool using neural networks for l(+)-lactic acid production by pellet-form Rhizopus oryzae NRRL 395 on biodiesel crude glycerol
url	https://dx.doi.org/10.1186/s13065-018-0491-5
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Modeling tool using neural networks for l(+)-lactic acid production by pellet-form Rhizopus oryzae NRRL 395 on biodiesel crude glycerol

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