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Dataset on optimized biodiesel production from seeds of Vitis vinifera using ANN, RSM and ANFIS

This dataset disclose the investigational data on the extraction of bio-oil from seeds of Vitis vinifera through combination of mechanical pressing and soxhlet solvent extractor. Biodiesel is produced through single stage base catalysed transesterification process due to lower free fatty acid conten...

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Detalles Bibliográficos
Autores principales: Hariram, V., Bose, A., Seralathan, S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6685693/
https://www.ncbi.nlm.nih.gov/pubmed/31406908
http://dx.doi.org/10.1016/j.dib.2019.104298
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author Hariram, V.
Bose, A.
Seralathan, S.
author_facet Hariram, V.
Bose, A.
Seralathan, S.
author_sort Hariram, V.
collection PubMed
description This dataset disclose the investigational data on the extraction of bio-oil from seeds of Vitis vinifera through combination of mechanical pressing and soxhlet solvent extractor. Biodiesel is produced through single stage base catalysed transesterification process due to lower free fatty acid content in the Vitis vinifera bio-oil. Independent variable process parameters like molar ratio, reaction time and catalyst concentration are optimized using Artificial Neural Network, Response Surface Methodology and Adaptive Neuro-Fuzzy Interference System to predict the maximum biodiesel yield and the results are compared with the experimental data. Response Surface Methodology predicted a maximum Vitis vinifera biodiesel yield of 97.62% at methanol to oil molar ratio 0.2758 v/v, catalyst concentration 1.045 gm of NaOH and reaction duration of 1.11 hrs which is also confirmed with experimental results.
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spelling pubmed-66856932019-08-12 Dataset on optimized biodiesel production from seeds of Vitis vinifera using ANN, RSM and ANFIS Hariram, V. Bose, A. Seralathan, S. Data Brief Energy This dataset disclose the investigational data on the extraction of bio-oil from seeds of Vitis vinifera through combination of mechanical pressing and soxhlet solvent extractor. Biodiesel is produced through single stage base catalysed transesterification process due to lower free fatty acid content in the Vitis vinifera bio-oil. Independent variable process parameters like molar ratio, reaction time and catalyst concentration are optimized using Artificial Neural Network, Response Surface Methodology and Adaptive Neuro-Fuzzy Interference System to predict the maximum biodiesel yield and the results are compared with the experimental data. Response Surface Methodology predicted a maximum Vitis vinifera biodiesel yield of 97.62% at methanol to oil molar ratio 0.2758 v/v, catalyst concentration 1.045 gm of NaOH and reaction duration of 1.11 hrs which is also confirmed with experimental results. Elsevier 2019-07-23 /pmc/articles/PMC6685693/ /pubmed/31406908 http://dx.doi.org/10.1016/j.dib.2019.104298 Text en © 2019 Published by Elsevier Inc. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Energy
Hariram, V.
Bose, A.
Seralathan, S.
Dataset on optimized biodiesel production from seeds of Vitis vinifera using ANN, RSM and ANFIS
title Dataset on optimized biodiesel production from seeds of Vitis vinifera using ANN, RSM and ANFIS
title_full Dataset on optimized biodiesel production from seeds of Vitis vinifera using ANN, RSM and ANFIS
title_fullStr Dataset on optimized biodiesel production from seeds of Vitis vinifera using ANN, RSM and ANFIS
title_full_unstemmed Dataset on optimized biodiesel production from seeds of Vitis vinifera using ANN, RSM and ANFIS
title_short Dataset on optimized biodiesel production from seeds of Vitis vinifera using ANN, RSM and ANFIS
title_sort dataset on optimized biodiesel production from seeds of vitis vinifera using ann, rsm and anfis
topic Energy
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6685693/
https://www.ncbi.nlm.nih.gov/pubmed/31406908
http://dx.doi.org/10.1016/j.dib.2019.104298
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