The Influences of Promising Feedstock Variability on Advanced Biofuel Production: A Review
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The contribution of biofuels is expected to continuously increase in the global fuel market, as they are environmentally-friendly and provide renewable energy. Four generations of biofuels are categorized and are primarily based on their feedstock sources and the production technologies that are used. The influence of promising feedstock types and the availability on the production technologies and the fuel properties of advanced-generation biofuels are not systematically examined in the literature. Hence, this research extensively reviews the potential impact of feedstock sources and their variability on the production and characteristics of biofuels. The approaches of theoretical analysis and inference referred to relevant works in the literature were applied. The findings suggest that the potency of the commercialized mass production of advanced-generation biofuels is facilitated by a much more flexible selection and the sufficient availability of promising feedstocks. Lignocellulose biomass is recognized as the most significant feedstock source for second-generation biofuels, while microalgae do the same for third-generation biofuels. Moreover, the microalgae of some strains are able to produce the highest amount of bio-alcohol of all available feedstock sources. The cell walls of lignocellulose biomass and microalgae mostly consist of lignin compounds and cellulose materials, respectively. Biological pretreatment is considered to be the most promising process, prior to biofuel production. The biofuel yields from lignocellulose biomass and microalgae, using biological pretreatments, could increase by 120% and 22-159%, respectively, in comparison with those of any other pretreatment process. Moreover, more double bonds and larger unsaturated fatty acids in raw lipids cause the inferior oxidative stability, but superior fluidity of biofuel. The possible impact of Genetically-Modified Crops (GMC) on the eco-environment and human genes remains a serious concern and requires further tracking and analysis. Genetically-modified technology is still immature to achieve the expected characteristics of biofuels from those modified crops. The unceasing exploitation of promising biomass feedstock sources is crucial for the rapid and steady development of advanced-generation biofuels.
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Such genetic modifications define the transition towards fourth-generation concepts, targeting significantly improved biofuel yields per hectare, although specific quantitative projections remain under development.
“Such genetic modifications define the transition towards fourth-generation concepts, targeting significantly improved biofuel yields per hectare, although specific quantitative projections remain under development [118,166].”
From Biofuels, E-Fuels, and Waste-Derived Fuels: Advances, Challenges, and Future Directions (2025) · cites this paper as [118]
Section 5.3.3 says genetic modification has been used to increase crop yields and can make crops more suitable biofuel feedstocks, while cautioning that the technology remains immature. It does not project improved biofuel yields per hectare from those modifications. The Introduction and Table 1 instead describe fourth-generation biofuels as photosynthetically produced biohydrogen and bioelectricity, not as a transition defined by genetic modification (page numbers unavailable). (LLM, conf=0.88)
Experimental — accuracy not yet measuredUsed cooking oil is considered a second-generation feedstock, whereas the virgin vegetable oil from which it originated is classified as first-generation.
“Materials originally derived from food products may only be classified as 2-G feedstocks when representing unavoidable residues or waste streams not diverted from the food chain or when they have become unsuitable for consumption [118]; for example, used cooking oil (UCO) is considered a 2-G feedstock, whereas the virgin vegetable oil from which it originated is classified as 1-G [119].”
From Biofuels, E-Fuels, and Waste-Derived Fuels: Advances, Challenges, and Future Directions (2025) · cites this paper as [118]
First-generation ethanol fermentation and biodiesel transesterification are mature technologies.
“First-generation technologies like ethanol fermentation and biodiesel transesterification are mature [116,118].”
From Biofuels, E-Fuels, and Waste-Derived Fuels: Advances, Challenges, and Future Directions (2025) · cites this paper as [118]
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- Authors
- Lin, C.-Y.
- Affiliations
- National Taiwan Ocean University (TW)
- Corresponding author
- Lin, Cherng-Yuan
- ORCID
- Lin, Cherng-Yuan
- Venue
- Journal of marine science and technology
- Publisher
- National Taiwan Ocean University
- Published
- 2022-01-03
- Volume
- 29
- Issue
- 6
- Pages
- 714-730
- ISSN
- 1023-2796 (ISSN-L)
- 2709-6998
- OpenAlex
- W4206720982
- Cited by
- 15
- References
- 166
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 4 · 2023: 3
- FWCI
- 0.87
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of marine science and technology (publishedVersion)
- Funders
- Ministry of Science and Technology, Taiwan (MOST 109, MOST109-2221-E019-024, MOST 107?, MOST 109-2221)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Biofuel production and bioconversion
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Bioenergy crop production and management
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- advanced biofuels, feedstock variability, lignocellulosic biomass, microalgae, second-generation biofuels, third-generation biofuels, biological pretreatment, biofuel yield, oxidative stability, unsaturated fatty acids, genetically modified crops, bioalcohols, biofuel properties
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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