Synthesis pathway and combustion mechanism of a sustainable biofuel 2,5-Dimethylfuran: Progress and prospective

Hoang, Anh Tuan; Nižetić, Sandro; Ölçer, Aykut I.; Ong, Hwai Chyuan

doi:10.1016/j.fuel.2020.119337

Cited by 36 publications

(10 citation statements)

References 201 publications

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“…Wood, animal, and plant wastes all have biomass potential. , Biomass can be directly used as fuel, although direct combustion is highly inefficient and polluting. The biomass can be converted to fuel in liquid or gaseous. , Among the biomass conversion methods, pyrolysis has a lot of promise. , Thermochemical conversion, which includes combustion, pyrolysis, torrefaction, hydrothermal liquefaction, and also gasification, is the most practical method for converting solid biomass into biofuel. , By optimizing process parameters, the fundamental objective of thermochemical conversion is to remove unwanted byproducts. Pyrolysis is a potential technology for converting biomass into biofuel in an inert environment at high temperatures (250–600 °C). , Pyrolysis technology is being used to produce biobased fuels and chemicals from biomass, which is a relatively new technique.…”

Section: Machine Learning For Rementioning

confidence: 99%

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Sharma¹,

Said

Kumar³

et al. 2022

Energy Fuels

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208

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Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required.

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Section: Machine Learning For Rementioning

confidence: 99%

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Sharma¹,

Said

Kumar³

et al. 2022

Energy Fuels

Self Cite

208

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“…Currently, the conversion of cellulose biomass to DMF is accomplished through two steps (Figure 1): The first step is to pre-treat the biomass and reduce it to glucose or fructose, then dehydration to generate to 5hydroxymethylfurfural (HMF); The next step is to convert HMF to DMF through hydrodeoxygenation (HDO) (Ong and Wu, 2020) (Ong et al, 2021b) (Katagi et al, 2021).…”

Section: Dmf Synthesis Pathwaymentioning

confidence: 99%

Laminar Flame Characteristics of 2,5-Dimethylfuran (DMF) Biofuel: A Comparative Review with Ethanol and Gasoline

Hoang

Nguyen

Truong

et al. 2021

Int. J. Renew. Energy Dev.

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Since the early years of the 21st century, the whole world has faced two very urgent problems: the depletion of fossil energy sources and climate change due to environmental pollution. Among the solutions sought, 2,5-Dimethylfuran (DMF) emerged as a promising solution. DMF is a 2nd generation biofuel capable of mass production from biomass. There have been many studies confirming that DMF is a potential alternative fuel for traditional fuels (gasoline and diesel) in internal combustion engines, contributing to solving the problem of energy security and environmental pollution. However, in order to apply DMF in practice, more comprehensive studies are needed. Not out of the above trend, this paper analyzes and discusses in detail the characteristics of DMF's combustible laminar flame and its instability under different initial conditions. The evaluation results show that the flame characteristics of DMF are similar to those of gasoline, although the burning rate of DMF is much higher than that of gasoline. This shows that DMF can become a potential alternative fuel in internal combustion engines.

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“…An example is corn stoverethanol biorefinery with value-added byproducts such as acetic acid, phenol, furfural, cresols, catechol, formic acid, and acetaldehyde [28]. Besides, furan-based biofuels, specifically dimethylfuran, have been currently considered as a target product of lignocellulosic biorefinery [29][30][31]. Therefore, commercializing biofuel production from corn stover significantly depends on the generation of additional value-added byproducts in the biorefinery [3].…”

Section: Introductionmentioning

confidence: 99%

Biorefining of corn stover for efficient production of bioethanol, biodiesel, biomethane, and value-added byproducts

Alavijeh

Shahvandi

Okoro

et al. 2023

Energy Conversion and Management

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Synthesis pathway and combustion mechanism of a sustainable biofuel 2,5-Dimethylfuran: Progress and prospective

Cited by 36 publications

References 201 publications

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Laminar Flame Characteristics of 2,5-Dimethylfuran (DMF) Biofuel: A Comparative Review with Ethanol and Gasoline

Biorefining of corn stover for efficient production of bioethanol, biodiesel, biomethane, and value-added byproducts

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