Prediction of cetane number by group additivity and carbon-13 Nuclear Magnetic Resonance

DeFries, T.; Kastrup, Rodney V.; Indritz, Doren

doi:10.1021/ie00062a002

Cited by 42 publications

(39 citation statements)

References 4 publications

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“…The cetane numbers for both the butene oligomer mixtures and the dimerized 2‐ethyl‐1‐hexene mixtures have not yet been determined. However, a calculation utilizing a simple additive method for a hydrogenated butene trimer suggests that mixed oligomer fuels will have moderate cetane numbers (>35) 55. This may allow for these mixtures to act as standalone diesel fuels or blending agents with other butanol derived fuels such as dibutyl ether.…”

Section: Saturated Hydrocarbon Fuels From Butanolmentioning

confidence: 99%

The role of butanol in the development of sustainable fuel technologies

Harvey

Meylemans

2010

J of Chemical Tech & Biotech

257

189

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OVERVIEW: The development of innovative methods to efficiently convert biomass to fuels and industrial chemicals is one of the grand challenges of the current age. n-Butanol is a versatile and sustainable platform chemical that can be produced from a variety of waste biomass sources. The emergence of new technologies for the production of fuels and chemicals from butanol will allow it to be a significant component of a necessarily dynamic and multifaceted solution to the current global energy crisis.IMPACT: The production of butanol from biomass and its utilization as a precursor to a diverse set of fuel products has the potential to reduce petroleum use worldwide. In concert with other emerging renewable technologies, significant reductions in greenhouse gas emissions may be realized. The rapid incorporation of renewables into the world fuel supply may also help to offset predicted increases in transportation fuel prices as the supply of oil declines. APPLICATIONS: Recent work has shown that butanol is a potential gasoline replacement that can also be blended in significant quantities with conventional diesel fuel. These efforts have transitioned to research focused on the development of viable methods for the production of an array of oxygenated and fully saturated jet and diesel fuels from butanol. The technologies discussed in this paper will help drive the commercialization and utilization of a spectrum of butanol based sustainable fuels that can supplement and partially displace conventional petroleum derived fuels. Published

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Section: Saturated Hydrocarbon Fuels From Butanolmentioning

confidence: 99%

The role of butanol in the development of sustainable fuel technologies

Harvey

Meylemans

2010

J of Chemical Tech & Biotech

257

189

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“…Many empirical relationships between the aromaticity and CN of diesel fuels have been reported elsewhere [27,28]. On the basis of such correlations, the increase in CN of LCO was determined from the change in aromaticity (f a ).…”

Section: Temp °Cmentioning

confidence: 99%

Upgrading of light cycle oil by partial hydrogenation and selective ring opening over an iridium bifunctional catalyst

et al. 2011

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An iridium catalyst supported on USY zeolite has been evaluated for hydrotreating of light cycle oil (LCO) to reduce its aromaticity and improve its cetane index. The products were analyzed by GC and 13 C NMR spectrometry to determine quantitatively the aromatic carbon content and the increase in cetane index. Addition of an appropriate amount of potassium was found to be an effective way of optimizing the acid properties of the catalyst; reducing the number of strongly acid sites reduced its cracking activity. The results confirm that 0.9% (w/w) Ir/USY zeolite catalyst doped with 0.75% (w/w) K was highly suitable for partial hydrogenation and ring opening of LCO to improve cetane quality, thus increasing the extent of its blending ratio with the diesel pool.

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“…Prior models based on quantitative structure property relationships (QSPR) have been developed to predict the CN of different compounds, which included an early, but limited, application of backpropagating neural networks for predicting the CN of isoparaffins and diesel fuels [5]. Though the study was limited to branched paraffins, the model showed a superior predictive power compared to conventional equations [6]. A subsequent study used quantitative structure property relationship (QSPR) software to generate 100 molecular descriptors for a set of 275 compounds, including 147 hydrocarbons and 128 oxygenates [7]; a genetic algorithm, or a search heuristic mimicking natural selection in regards to optimization problems, was used to identify which descriptors might influence CN.…”

Section: Predicting the Cetane Numbermentioning

confidence: 99%

“…This is due to the fact that many of the values for some parameters are equal for the majority of molecules, which is detrimental to the neural network and unhelpful in capturing the nonlinear behavior. Historically, 14-23 descriptors have been chosen for similar approaches in the literature [5][6][7]11]. The process is repeatable across multiple attempts, which suggests that the chosen set is likely to be the most influential descriptors in regards to CN prediction for this database.…”

Section: Neural Network Architecturementioning

confidence: 99%

Predicting the Cetane Number of Furanic Biofuel Candidates Using an Improved Artificial Neural Network Based on Molecular Structure

Kessler¹,

Sacia²,

Bell³

et al. 2017

Preprint

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TitlePredicting the cetane number of furanic biofuel candidates using an improved artificial neural network based on molecular structure b s t r a c tThe next generation of alternative fuels is being investigated through advanced chemical and biological production techniques for the purpose of finding suitable replacements for diesel and gasoline while lowering production costs and increasing process yields. Chemical conversion of biomass to fuels provides a plethora of pathways with a variety of fuel molecules, both novel and traditional, which may be targeted. In the search for new fuels, an initial, intuition-driven evaluation of fuel compounds with desired properties is required. Due to the high cost and significant production time needed to synthesize these materials at a scale sufficient for exhaustive testing, a predictive model would allow chemists to preemptively screen fuel properties of potentially desirable fuel candidates. Recent work has shown that predictive models, in this case artificial neural networks (ANN's) analyzing quantitative structure property relationships (QSPR's), can predict the cetane number (CN) of a proposed fuel molecule with relatively small error. A fuel's CN is a measure of its ignition quality, typically defined using prescribed ASTM standards and a cetane testing engine. Alternatively, the analogous derived cetane number (DCN), obtained using an Ignition Quality Tester (IQT), is a direct measurement alternative to the CN that uses an empirical inverse relationship to the ignition delay found in the constant volume combustion chamber apparatus. DCN data points acquired using an IQT were utilized for model validation and expansion of the experimental database used in this study. The present work improves on an existing model by optimizing the model architecture along with the key learning variables of the ANN and by making the model more generalizable to a wider variety of fuel candidate types, specifically the class of furans and furan derivatives, by including specific molecules for the model to incorporate. The new molecules considered include tetrahydrofuran, 2-methylfuran, 2-methyltetrahydrofuran, 5,5 0 -(furan-2-ylmethylene)bis(2-methyl furan), 5,5 0 -((tetrahydrofuran-2-yl)methylene)bis(2-methyltetrahydrofuran), tris(5-methylfuran-2-yl) methane, and tris(5-methyltetrahydrofuran-2-yl)methane. Model architecture adjustments improved the overall root-mean-square error (RMSE) of the base database predictions by 5.54%. Additionally, through the targeted database expansion, it is shown that the predicted cetane number of the furanbased molecules improves on average by 49.21% (3.74 CN units) and significantly for a few of the individual molecules. This indicates that a selected subset of representative molecules can be used to extend the model's predictive accuracy to new molecular classes. The approach, bolstered by the improvements http://dx

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Prediction of cetane number by group additivity and carbon-13 Nuclear Magnetic Resonance

Cited by 42 publications

References 4 publications

The role of butanol in the development of sustainable fuel technologies

The role of butanol in the development of sustainable fuel technologies

Upgrading of light cycle oil by partial hydrogenation and selective ring opening over an iridium bifunctional catalyst

Predicting the Cetane Number of Furanic Biofuel Candidates Using an Improved Artificial Neural Network Based on Molecular Structure

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