Computational evaluation of microalgae biomass conversion to biodiesel

Momir Milić; Biljana Petković; Abdellatif Selmi; Dalibor Petković; Kittisak Jermsittiparsert; Aleksandar Radivojević; Milos Milovancevic; Afrasyab Khan; Slađana T. Vidosavljević; Nebojša Denić; Boris Kuzman

doi:10.1007/s13399-021-01314-2

Computational evaluation of microalgae biomass conversion to biodiesel

Momir Milić
, Biljana Petković
, Abdellatif Selmi
, Dalibor Petković
, Kittisak Jermsittiparsert
, Aleksandar Radivojević
, Milos Milovancevic
, Afrasyab Khan
, Slađana T. Vidosavljević
, Nebojša Denić
, Boris Kuzman

Civil Engineering

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

Since there are large requirements for green energy sustainability, in this study, optimization of in situ transesterification of microalgae slurry conversion into biodiesel was performed. The main aim was to perform a selection procedure of the optimal predictors for fatty acid methyl ester yield and exergy efficiency in situ transesterification process. The adaptive neuro fuzzy inference system (ANFIS) as a soft computing approach was used for the optimization of the predictors for the fatty acid methyl ester yield and exergy efficiency. Based on the obtained results, the optimal combination for fatty acid methyl ester yield was ultrasonic power and reaction time while the optimal combination for the exergy efficiency was concentrations of methanol and chloroform in oil. These selected predictors could be used effectively in order to maximize the fatty acid methyl ester yield and exergy efficiency.

Original language	English
Pages (from-to)	3179-3186
Number of pages	8
Journal	Biomass Conversion and Biorefinery
Volume	13
Issue number	4
DOIs	https://doi.org/10.1007/s13399-021-01314-2
State	Published - Feb 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

ANFIS
Biodiesel
Microalgae
Optimization
Soft computing

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10.1007/s13399-021-01314-2

Cite this

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title = "Computational evaluation of microalgae biomass conversion to biodiesel",

abstract = "Since there are large requirements for green energy sustainability, in this study, optimization of in situ transesterification of microalgae slurry conversion into biodiesel was performed. The main aim was to perform a selection procedure of the optimal predictors for fatty acid methyl ester yield and exergy efficiency in situ transesterification process. The adaptive neuro fuzzy inference system (ANFIS) as a soft computing approach was used for the optimization of the predictors for the fatty acid methyl ester yield and exergy efficiency. Based on the obtained results, the optimal combination for fatty acid methyl ester yield was ultrasonic power and reaction time while the optimal combination for the exergy efficiency was concentrations of methanol and chloroform in oil. These selected predictors could be used effectively in order to maximize the fatty acid methyl ester yield and exergy efficiency.",

keywords = "ANFIS, Biodiesel, Microalgae, Optimization, Soft computing",

author = "Momir Mili{\'c} and Biljana Petkovi{\'c} and Abdellatif Selmi and Dalibor Petkovi{\'c} and Kittisak Jermsittiparsert and Aleksandar Radivojevi{\'c} and Milos Milovancevic and Afrasyab Khan and Vidosavljevi{\'c}, \{Sla{\d}ana T.\} and Neboj{\v s}a Deni{\'c} and Boris Kuzman",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.",

year = "2023",

month = feb,

doi = "10.1007/s13399-021-01314-2",

language = "English",

volume = "13",

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journal = "Biomass Conversion and Biorefinery",

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TY - JOUR

T1 - Computational evaluation of microalgae biomass conversion to biodiesel

AU - Milić, Momir

AU - Petković, Biljana

AU - Selmi, Abdellatif

AU - Petković, Dalibor

AU - Jermsittiparsert, Kittisak

AU - Radivojević, Aleksandar

AU - Milovancevic, Milos

AU - Khan, Afrasyab

AU - Vidosavljević, Slađana T.

AU - Denić, Nebojša

AU - Kuzman, Boris

PY - 2023/2

Y1 - 2023/2

N2 - Since there are large requirements for green energy sustainability, in this study, optimization of in situ transesterification of microalgae slurry conversion into biodiesel was performed. The main aim was to perform a selection procedure of the optimal predictors for fatty acid methyl ester yield and exergy efficiency in situ transesterification process. The adaptive neuro fuzzy inference system (ANFIS) as a soft computing approach was used for the optimization of the predictors for the fatty acid methyl ester yield and exergy efficiency. Based on the obtained results, the optimal combination for fatty acid methyl ester yield was ultrasonic power and reaction time while the optimal combination for the exergy efficiency was concentrations of methanol and chloroform in oil. These selected predictors could be used effectively in order to maximize the fatty acid methyl ester yield and exergy efficiency.

AB - Since there are large requirements for green energy sustainability, in this study, optimization of in situ transesterification of microalgae slurry conversion into biodiesel was performed. The main aim was to perform a selection procedure of the optimal predictors for fatty acid methyl ester yield and exergy efficiency in situ transesterification process. The adaptive neuro fuzzy inference system (ANFIS) as a soft computing approach was used for the optimization of the predictors for the fatty acid methyl ester yield and exergy efficiency. Based on the obtained results, the optimal combination for fatty acid methyl ester yield was ultrasonic power and reaction time while the optimal combination for the exergy efficiency was concentrations of methanol and chloroform in oil. These selected predictors could be used effectively in order to maximize the fatty acid methyl ester yield and exergy efficiency.

KW - ANFIS

KW - Biodiesel

KW - Microalgae

KW - Optimization

KW - Soft computing

UR - https://www.scopus.com/pages/publications/85101528248

U2 - 10.1007/s13399-021-01314-2

DO - 10.1007/s13399-021-01314-2

M3 - Article

AN - SCOPUS:85101528248

SN - 2190-6815

VL - 13

SP - 3179

EP - 3186

JO - Biomass Conversion and Biorefinery

JF - Biomass Conversion and Biorefinery

IS - 4

ER -

Computational evaluation of microalgae biomass conversion to biodiesel

Abstract

UN SDGs

Keywords

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