Process Optimization for Madhuca indica Seed Kernel Oil Extraction and Evaluation of its Potential for Biodiesel Production

The current research aims to optimize the solvent-based oil extraction process from Mahua (Madhuca indica) seed using response surface methodology and biodiesel production using heterogeneous catalysts. The oil extraction was varied through the levels of process parameters including extraction temperature (60 to 80°C), solvent-to-seed ratio (3 to 9 wt/wt), and time (2 to 4 h). The experiments were designed following the Central Composite model. The regression model provided optimal values for the selected process parameters based on the extraction yield percentage. To ensure the model’s reliability, it was experimentally validated. Maximum experimental oil yields of 50.9% were obtained at an optimized extraction scenario of 70 °C extraction temperature, solvent-to-seed ratio of 6 wt/wt, and time 4 h. The extracted oil’s physicochemical properties and fatty acid composition were tested. Also, using copper-coated dolomite as a catalyst, the extracted oil was transformed into biodiesel via transesterification. The FAME (94.31%) content of the prepared biodiesel was determined via gas chromatography. As a result, the findings of this study will be useful in further research into the use of Madhuca indica as a potential feedstock for biodiesel production.

Authors

Publication Details

Journal
Nature Environment and Pollution Technology
Published
2024-12-01
DOI
https://doi.org/10.46488/nept.2024.v23i04.037
Citations
1
Primary Topic
Natural Products and Biological Research
Type
article
Field-Weighted Citation Impact
0.57
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Process Optimization for Madhuca indica Seed Kernel Oil Extraction and Evaluation of its Potential for Biodiesel Production

M. G. Devanesan, A. Arumugam, S. Sudalai, S. Prabakaran
1 citations
Nature Environment and Pollution Technology
Natural Products and Biological Research
0.57
article

Process Optimization for Madhuca indica Seed Kernel Oil Extraction and Evaluation of its Potential for Biodiesel Production

M. G. Devanesan, A. Arumugam, S. Sudalai, S. Prabakaran
article en
1 citations

Abstract

The current research aims to optimize the solvent-based oil extraction process from Mahua (Madhuca indica) seed using response surface methodology and biodiesel production using heterogeneous catalysts. The oil extraction was varied through the levels of process parameters including extraction temperature (60 to 80°C), solvent-to-seed ratio (3 to 9 wt/wt), and time (2 to 4 h). The experiments were designed following the Central Composite model. The regression model provided optimal values for the selected process parameters based on the extraction yield percentage. To ensure the model’s reliability, it was experimentally validated. Maximum experimental oil yields of 50.9% were obtained at an optimized extraction scenario of 70 °C extraction temperature, solvent-to-seed ratio of 6 wt/wt, and time 4 h. The extracted oil’s physicochemical properties and fatty acid composition were tested. Also, using copper-coated dolomite as a catalyst, the extracted oil was transformed into biodiesel via transesterification. The FAME (94.31%) content of the prepared biodiesel was determined via gas chromatography. As a result, the findings of this study will be useful in further research into the use of Madhuca indica as a potential feedstock for biodiesel production.

Nature Environment and Pollution TechnologyVol. 23(4)
Openalex Percentile: Top 27%
Natural Products and Biological Research
0.57
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.