Search

The 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′-(furan-2-ylmethylene)bis(2-methylfuran), 5,5′-((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 furan-based 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 presented in this paper, enables chemists to focus on promising molecules by eliminating less favorable candidates in relation to their ignition quality.

Primary alcohols (ethanol, 1-propanol, 1-butanol, and 1-pentanol) derived from biomass offer a sustainable fuel source that can improve efficiency while reducing carbon dioxide (CO2) emissions. However, the performance of these primary alcohols in spark-ignited engines is relatively unknown. In this work, the performance of primary alcohols was experimentally determined using the research octane number (RON) and the blending research octane number (BRON). The primary alcohol mixture, or "AlcoMix," consists of 75% ethanol, 11% 1-propanol, 8% 1-butanol, and 6% 1-pentanol and was approved by the U.S. EPA for use in blending with gasoline. This mixture is the probable outcome of the thermochemical conversion of biomass using Fischer-Tropsch chemistry with synthesis gas. The purpose of this research was to determine whether AlcoMix might be a suitable replacement for ethanol in fuel blending as an antiknock blending component for spark-ignited engines. As an indicative measure of knock resistance, the RONs of AlcoMix and ethanol were estimated using a modified, validated method in a CFR engine. The antiknock properties of AlcoMix as a blending component in gasoline were determined by estimating the BRON. The results show that the measured RON of the individual primary alcohols closely match published values. Additionally, the RON and BRON of the primary alcohol mixture nearly match those of ethanol. These results indicate that the primary alcohol mixture produced by thermochemical processes could be used as a substitute for ethanol as a primary fuel or as an antiknock blending component. © 2014 American Chemical Society.

Combustion of natural gas in combination with hydrogen, coupled with argon as a working fluid, is a promising approach to increasing the efficiency of internal combustion engines while decreasing emissions. The use of argon as a working fluid effectively eradicates NOx, extends the flammability limits, and increases the thermal efficiency due to its high specific heat ratio. Additionally, the hydrogen addition aids in emissions reduction and enhances the flammability range. In this study, premixed CH4-H2-O2-Ar combustion is experimentally investigated in an optically accessible constant volume combustion chamber to determine the effect of hydrogen addition on laminar burning velocity, flame morphological structure, and instabilities when argon replaces nitrogen as the working fluid. A numerical thermodynamic model is applied to calculate the laminar burning velocity and an image processing technique is used to quantify the flame speed. The experiments show that, in comparison to methane alone, the addition of hydrogen (40%) to the mixture at atmospheric pressure (1 bar) and room temperature (298 K) increases the maximum laminar burning velocity by 37%, increases the maximum flame speed by 35%, and extends the lower and upper flammability limits from equivalence ratio of 0.4 to 0.3 and 1.6 to 1.7, respectively. [ABSTRACT FROM AUTHOR]

Sooting propensity, a measurement of how much particulate matter is produced when a fuel is burned, is a property of significant interest among researchers who are striving to discover the next generation of cleaner, more efficient fuels and fuel additives. Many compounds are not viable as fuels and/or fuel additives, and as a result, designing cleaner-burning biofuels using only experimental techniques is inefficient. Predictive models have been instrumental in reducing this inherent difficulty, providing researchers with a tool to preemptively screen compounds before production and testing. The present work compares the accuracies and interpretabilities of existing models used to predict a particular measure of sooting propensity, Yield Sooting Index (YSI). These models include artificial neural networks, graph neural networks, and multivariate equations. A novel equation for predicting YSI based on atom path count and bond order is proposed, which can highlight key structural components that contribute to YSI. It was found that artificial neural networks slightly outperform graph neural networks and greatly outperform multivariate equations in blind (test set) prediction accuracy; however, graph neural networks and multivariate equations provide significantly more interpretability as to how compound structure relates to YSI. Predictions of YSI are compared to experimental measurements for previously un-tested compounds with cetane numbers comparable to diesel fuel (50-60) (butyl decanoate, ethyl decanoate, 1,4-bis(ethenoxymethyl)cyclohexane, and 5-heptyloxolan-2-one), and it was found that these compounds produce significantly less soot compared to diesel fuel. [ABSTRACT FROM AUTHOR]