Non-Petroleum-Based Fuels: Report on the Relationship Between Molecular Structure and Compression Ignition Fuels, Both Conventional and HCCI

Taylor, J. R.; McCormick, Robert L.; Clark, Wendy

doi:10.2172/1312467

Cited by 19 publications

(20 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data for cetane numbers used in the core data set was obtained from sets present in the NREL Compendium of Experimental Cetane Number Data [11] and other sources [6,12,13]. The core data set contains 423 molecules, with their cetane numbers derived from blend measurements, CFR, IQT or other ignition delay methods.…”

Section: Methodsmentioning

confidence: 99%

Application of a Rectified Linear Unit (ReLU) Based Artificial Neural Network to Cetane Number Predictions

Kessler

Dorian

Mack

2017

Volume 1: Large Bore Engines; Fuels; Advanced Combustion

View full text Add to dashboard Cite

Due to the high cost and time required to synthesize alternative fuel candidates for comprehensive testing, an Artificial Neural Network (ANN) can be used to predict fuel properties, allowing researchers to preemptively screen desirable fuel candidates. However, the accuracy of an ANN is limited by its error, measured by the root mean square error (RMSE), standard deviation, and r-squared values derived from a given input database. The present work improves upon an existing model for predicting the Cetane Number (CN) by changing the neuron activation function of the ANN from sigmoid to rectified linear unit (ReLU). This change to the ANN's architecture provides an increase in accuracy by reducing the RMSE by 21.4% (1.35 CN units), the average standard deviation across models by 28%, and increasing the r-squared value by 0.0492 across a wide range of molecular structures. Additionally, by using the ReLU activation function, input data is not required to be normalized, which reduces the likelihood of an inaccurate prediction on future fuel candidates which may have input parameters outside the range of normalization. Increasing the accuracy of the predictive ANN in this way will allow researchers to obtain more accurate fuel property predictions for promising fuel candidates.

show abstract

Section: Methodsmentioning

confidence: 99%

Application of a Rectified Linear Unit (ReLU) Based Artificial Neural Network to Cetane Number Predictions

Kessler

Dorian

Mack

2017

Volume 1: Large Bore Engines; Fuels; Advanced Combustion

View full text Add to dashboard Cite

show abstract

“…For this reason, cross‐validation of the model is an important step. Application of QSPR to fuel properties has resulted in models that can estimate cetane number,7, 8 octane number (research octane number, RON, and motor octane number, MON),9 and sooting tendencies10 of any fuel component, only on the basis of the molecular structure.…”

Section: Methodsmentioning

confidence: 99%

Molecular engineering approach in the selection of catalytic strategies for upgrading of biofuels

Resasco

Crossley

2009

AIChE Journal

View full text Add to dashboard Cite

“…The cetane number data used for the core data set was obtained from sets found in the NREL Compendium of Experimental Cetane Number Data [19] and other sources [7,10]. It contains 284 molecules in total.…”

Section: Input Database Constructionmentioning

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

View full text Add to dashboard Cite

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

show abstract

Non-Petroleum-Based Fuels: Report on the Relationship Between Molecular Structure and Compression Ignition Fuels, Both Conventional and HCCI

Cited by 19 publications

References 0 publications

Application of a Rectified Linear Unit (ReLU) Based Artificial Neural Network to Cetane Number Predictions

Application of a Rectified Linear Unit (ReLU) Based Artificial Neural Network to Cetane Number Predictions

Molecular engineering approach in the selection of catalytic strategies for upgrading of biofuels

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

Contact Info

Product

Resources

About