Feedstocks, catalysts, process variables and techniques for biodiesel production by one-pot extraction-transesterification: a review

Ahmed, Mukhtar; Abdullah, Anas; Patle, Dipesh S.; Shahadat, Mohammad; Ahmad, Zainal Arifin; Athar, Manazir; Aslam, Mohammad; Vo, Dai‐Viet N.

doi:10.1007/s10311-021-01358-w

Cited by 19 publications

(3 citation statements)

References 280 publications

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“…The choice of feedstock for biodiesel production is a major deciding factors that affect the production cost, infrastructure, conversion technique, conversion efficiency, the pump price, and sustainability of biodiesel production. For example, the feedstock accounted for about 75%-88% of production cost and is a key consideration in fixing the pump price of biodiesel (Ahmed et al, 2021). The selection of feedstock also greatly influences the type of catalyst, process parameters, intensification techniques, and purification methods (Athar and Zaidi, 2020;Sitepu et al, 2020;Mohiddin et al, 2021;Awogbemi and Kallon, 2022c).…”

Section: Feedstock For Biodiesel Productionmentioning

confidence: 99%

Application of machine learning technologies in biodiesel production process—A review

Awogbemi

Kallon²

2023

Front. Energy Res.

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The search for renewable, affordable, sustainable, and ecologically benign fuels to substitute fossil-based diesel fuels has led to increased traction in the search for biodiesel production and utilization in recent times. Biodiesel, a form of liquid biofuel, has been found to alleviate environmental degradation, enhance engine performance, and reduce emissions of toxic gases in transportation and other internal combustion engines. However, biodiesel production processes have been dogged with various challenges and complexities which have limited its expected progression. The introduction of data-based technologies is one of the remedies aimed at deescalating the challenges associated with biodiesel synthesis. In this study, the application of machine learning (ML) –based technologies including artificial neural network (ANN), response surface methodology (RSM), adaptive network-based fuzzy inference system (ANFIS), etc. As tools for the prediction, modeling, and optimization of the biodiesel production process was interrogated based on the outcomes of previous studies in the research domain. Specifically, we review the influence of input variables like alcohol: oil molar ratio, catalyst concentration, reaction temperature, residence time, and agitation speed on the biodiesel yield (output variable). The outcome of this investigation shows that the usage of ANN, RSM, ANFIS, and other machine learning technologies raised biodiesel yield to between 84% and 98% while the statistical verification shows that the Pearson correlation coefficient and coefficient of determination are close to 1. Going forward, more targeted and collaborative research is needed to escalate the use of innovative technologies for the entire biodiesel value chain to enhance production efficiency, ensure economic feasibility, and promote sustainability.

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Section: Feedstock For Biodiesel Productionmentioning

confidence: 99%

Application of machine learning technologies in biodiesel production process—A review

Awogbemi

Kallon²

2023

Front. Energy Res.

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“…46 Of them, the process of catalytic upgrading of carbohydrates to HMF has attracted wide attention from researchers around the world. 47 Along with catalysts, the selection of suitable solvents is also an important parameter for HMF production, 48 and various solvent systems including water and organic solvents such as n-hexane, benzene, tetrahydrofuran (THF), methyl isobutyl ketone (MIBK) and ionic liquids have been developed. 49,50 Nevertheless, from the perspective of green chemistry and sustainable development, finding 'green solvents' to replace 'nongreen solvents' is a major obstacle that should be overcome.…”

Section: Introductionmentioning

confidence: 99%

Deep eutectic solvents for catalytic biodiesel production from liquid biomass and upgrading of solid biomass into 5-hydroxymethylfurfural

Li¹,

Chen²,

Zheng³

et al. 2023

Green Chem.

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“…The sustainable production and use of renewable fuels to enable the transition to a low-carbon and more sustainable economy have been promoted in recent years in response to the global climate crisis and the growing energy needs [ 1 , 2 ]. Liquid biofuels play a central role in that transition, in particular biodiesel which is considered an ideal candidate for the replacement of petroleum-derived diesel due to its high cetane number and flash point and the possibility to be used in any compression-ignition engine without the need for modification [ 3 , 4 ]. Biodiesel results from the reaction of one triacylglycerol molecule (consisting of three long-chain fatty acids attached to glycerol) with three alcohol molecules (usually methanol or ethanol) to produce three biodiesel molecules, i.e., methyl esters or ethyl esters, and one glycerol molecule [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring Yeast Diversity to Produce Lipid-Based Biofuels from Agro-Forestry and Industrial Organic Residues

Mota

Múgica²,

Correia

2022

JoF

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Exploration of yeast diversity for the sustainable production of biofuels, in particular biodiesel, is gaining momentum in recent years. However, sustainable, and economically viable bioprocesses require yeast strains exhibiting: (i) high tolerance to multiple bioprocess-related stresses, including the various chemical inhibitors present in hydrolysates from lignocellulosic biomass and residues; (ii) the ability to efficiently consume all the major carbon sources present; (iii) the capacity to produce lipids with adequate composition in high yields. More than 160 non-conventional (non-Saccharomyces) yeast species are described as oleaginous, but only a smaller group are relatively well characterised, including Lipomyces starkeyi, Yarrowia lipolytica, Rhodotorula toruloides, Rhodotorula glutinis, Cutaneotrichosporon oleaginosus and Cutaneotrichosporon cutaneum. This article provides an overview of lipid production by oleaginous yeasts focusing on yeast diversity, metabolism, and other microbiological issues related to the toxicity and tolerance to multiple challenging stresses limiting bioprocess performance. This is essential knowledge to better understand and guide the rational improvement of yeast performance either by genetic manipulation or by exploring yeast physiology and optimal process conditions. Examples gathered from the literature showing the potential of different oleaginous yeasts/process conditions to produce oils for biodiesel from agro-forestry and industrial organic residues are provided.

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Feedstocks, catalysts, process variables and techniques for biodiesel production by one-pot extraction-transesterification: a review

Cited by 19 publications

References 280 publications

Application of machine learning technologies in biodiesel production process—A review

Application of machine learning technologies in biodiesel production process—A review

Deep eutectic solvents for catalytic biodiesel production from liquid biomass and upgrading of solid biomass into 5-hydroxymethylfurfural

Exploring Yeast Diversity to Produce Lipid-Based Biofuels from Agro-Forestry and Industrial Organic Residues

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