There is signi cant interest among researchers in nding economically sustainable alternatives to fossil-derived drop-in fuels and fuel additives. Fast pyrolysis, a method for converting biomass into liquid hydrocarbons with the potential for use as fuels or fuel additives, is a promising technology that can be two to three times less expensive at scale when compared to alternative approaches such as gasi cation and fermentation. However, many bio-oils directly derived from fast pyrolysis have a high oxygen content and high acidity, indicating poor performance in diesel engines when used as fuels or fuel additives. Thus, a combination of selective fast pyrolysis and chemical catalysis could produce tuned bioblendstocks that perform optimally in diesel engines. The variance in performance for derived compounds introduces a feedback loop in researching acceptable fuels and fuel additives, as var- ious combustion properties for these compounds must be determined after pyrolysis and catalytic upgrading occurs. The present work aims to reduce this feedback loop by utilizing arti cial neural networks trained with quantitative structure-property relationship values to preemptively screen pure component compounds that will be produced from fast pyrolysis and catalytic upgrading. The quantitative structure-property relationship values selected as inputs for models are discussed, the cetane number and sooting propensity of compounds derived from the catalytic upgrading of phenol are predicted, and the viability of these compounds as fuels and fuel additives isanalyzed. The model constructed to predict cetane number has a test set prediction root-mean-squared error of 9.874 cetane units, and the model constructed to predict yield sooting index has a test set prediction root-mean-squared error of 13.478 yield sooting index units (on the uni ed scale). 1 INTRODUCTION The emphasis on nding renewable and cleaner sources of energy has become prevalent throughout the greater scien- ti c community due to concerns regarding global climate change and decreasing reserves of traditional petroleum- based fuels. Consequently, researchers have focused their e orts on discovering novel fuels derived from renewable sources (i.e. biofuels). One method for converting biomass into bio-oil is fast pyrolysis; however, not all compounds de- rived from fast pyrolysis perform optimally in existing en- gines and/or produce high amounts of negative byproducts, as do the compounds resulting from catalytically upgrading the products of fast pyrolysis. This highlights the need to screen compounds that will be produced from fast pyrol- ysis and catalytic upgrading before the procedures occur, ultimately providing researchers with more promising can- didate compounds. To reduce the inherent feedback loop associated with alternative fuel research, machine learning (speci cally arti cial neural networks, ANNs) can be em- ployed to predict the cetane number and yield sooting index of the products of fast pyrolysis and catalytic upgrading. Page 1 doi: 10.1115/ICEF2019-7170 Pre-Print 1.1 Background Information Fast pyrolysis converts biomass into bio-oils by rapidly heat- ing (>1000 °C/sec) the biomass to temperatures ranging from 400-600 °C for between 1-5 seconds and may yield up to 70 wt% of liquid phase products (bio-oils) [1]. It has been shown in recent techno-economic analyses that the cost to produce bio-oils via fast pyrolysis is comparable to the cost of producing bio-oils via alternative methods such as gasi cation, indicating fast pyrolysis is viable at scale [2]. The products of fast pyrolysis can range from alcohols and ethers to furanic and phenolic compounds depending on the feedstock utilized in the procedure [3]. Some prod- ucts that form as a result of using lignocellulosic biomass as the primary feedstock in fast pyrolysis have been identi ed as potential replacements for fossil-derived fuel additives and drop-in fuels [4], however many of these compounds have a high oxygen content (indicating poor performance as a drop-in fuel additive). For these compounds with sub- optimal characteristics, selective catalytic upgrading may result in compounds that perform better as fuel additives while decreasing soot formation. Catalytic upgrading is signi cant because it increases the hydrogen-to-carbon ratio, reduces oxygen content and in- creases molecular weights for oxygenated compounds de- rived from fast pyrolysis through a process called hy- drodeoxygenation [5]. Many hydrocarbon products were ob- served as a result of hydrotreating oxygen-rich compounds using Pd/C catalysts at 310-375 °C and then subjecting them to hydrocracking at 400 °C and 2000 PSI [6]. Hy- drogenolysis of phenolic compounds using Ru/TiO 2cata- lysts results in aromatic ring hydrogenation occurring on the Ru/Ti nanoparticle surface, in turn forming compounds with fewer oxygen atoms and that are aromatic in nature (both important qualities for indicating favorable perfor- mance as drop-in fuel additives) [7] [8]. The present work focusses on evaluating compounds resulting from catalyti- cally upgrading products of fast pyrolysis using Ru/TiO 2 catalysts and phenol as an additive during the reaction. Two properties will be evaluated for products of fast py- rolysis and catalytic upgrading { cetane number and yield sooting index. Cetane number (CN) measures a fuel's ig- nition quality in a diesel engine and is derived from the ignition delay after the fuel is injected. Diesel fuel typically has a CN of 40-50. Two common techniques for measur- ing CN are with an Ignition Quality Tester (IQT) and a Cooperative Fuel Research (CFR) engine. An IQT ascer- tains the ignition delay of a given fuel in a constant volume combustion chamber by measuring the time between injec- tion and combustion [9]. Alternatively, a CFR engine uti- lizes two reference compounds, n-hexadecane and isocetane (with CN's of 15 and 100, respectively) { igniting a given fuel in an equivalent blend of these compounds results in a volume fraction of these compounds linear related to the fuel's CN [10]. A CFR is preferred over the IQT, as theCFR re ects typical engine behavior; however, the IQT re- quires less test fuel (around 100 mL). While methods like the IQT utilize a small volume of fuel for determining CN, the number of tests required for determining CN's of a suite of potential alternative fuels results in a considerable time and monetary investment. Various sooting indices are used to measure soot forma- tion and how much particulate matter is emitted by a fuel during combustion. The Threshold Sooting Index (TSI) was developed to standardize the measurement of soot through smoke point and ranks fuels on a 0-100 scale using reference molecules [11]. Measuring smoke point involves measuring the maximum ame height attainable by a fuel combust- ing in a test lamp without smoking and has been shown to be a dependable indicator of sooting propensity of aviation fuels in turbines [12] as well as emissions from SI engines [13]. To account for reduced stoichiometric air required by oxygenated fuels, the oxygen extended sooting index (OESI) was de ned as an extension to TSI [14]. While these smaller, bench-scale methods of measuring sooting propen- sity through smoke point are convenient, they su er from a few disadvantages; operator bias in estimating an appro- priate ame shape may occur, as well as the requirement of up to 20 mL of the fuel in order to measure [12]. Due in part to these disadvantages the yield sooting in- dex (YSI) was developed, whose measurement is not based on smoke point, rather the maximum soot volume frac- tion measured in a ame ignited by a fuel doped with the molecule of interest [14]. YSI is measured on a 0-100 scale, using reference fuels n-hexane and benzene with YSI val- ues of 0 and 100 respectively. Diesel fuel typically has a YSI of 235-250. This method of measurement requires a signi cantly smaller volume of the sample, and correlates adequately with TSI [15] [16] [17] [18]. A \high sooting" YSI scale was also developed to emphasize mass-fraction- based fuel doping, and recent research has provided a uni- ed YSI scale, standardizing measurements from a variety of compounds and compound groups [19]. Regardless of the advances made in measuring soot formation, a consid- erable time investment exists when testing a large number of compounds. 1.2 Predicting Cetane Number & Yield Soot- ing Index Using computational techniques to predict CN has a per- vasive history and includes a variety of methods. Such methods include consensus modeling, where linear and non- linear models are employed in parallel to obtain an aver- aged predicted CN value { these models can predict CN for a variety of molecular classes with a blind prediction root-mean-squared error of 6.5 [20], however multiple lin- ear models are surpassed by the accuracy of ANNs when fatty acid methyl esters are used to predict CN [21]. Many methods rely on quantitative structure-property relation- Page 2 doi: 10.1115/ICEF2019-7170 Pre-Print Figure 1: Work ow diagram illustrating the experimental procedure utilized by the present work ship (QSPR) descriptors, which are numerical measure- ments of an assortment of physical and chemical properties relating to molecules, and have proven to be successful when applied to predicting the CN of pure hydrocarbons [22] and branched parans [23]. Additionally, ANNs have been ap- plied to predicting YSI using QSPR descriptors as training data and obtained 95% con dence in test set prediction ac- curacy (r-squared value of predictions for compounds not used in ANN training) [24]. In accordance with these ndings, this paper utilizes feed- forward ANNs trained with QSPR descriptors to predict CN and uni ed YSI. The present work leverages the uni ed YSI scale, as the models constructed aim to predict YSI for a va- riety of compounds and compound groups. It has recently been shown that ANNs can extend their predictive capabil- ities to a variety of molecular classes, including predicting the CN of biomass-derived furanic compounds [25]. ANNs provide a non-linear model architecture, allowing a multi- dimensional input vector containing a suite of individual QSPR values to be correlated to an experimental property value. QSPR descriptors are utilized due to the wide range of physical and chemical property representations available, subsequently distinguishing one molecule from another [26]. Traditionally, QSPR descriptors used as ANN inputs are chosen via an iterative regression analysis technique, where QSPR descriptors are added to the ANN based on the ANN's performance when the value is used in conjunction with previously selected values [25]. While this method highlights the ANN's ability to determine complex relation- ships between QSPR descriptor values as they are included, it fails to provide an explanation as to why/how individual QSPR descriptors correlate to a given property. Randomforest regression has been shown to be a viable method for reducing the number of quantitative structure-activity rela- tionship values used as inputs in a binary classi cation de- cision tree while retaining predictive accuracy [27], and pro- vide a quanti able measurement (importance) of individual value correlation to a given target value [28]. Therefore, the present work leverages importance resulting from ran- dom forest regression to select QSPR descriptors as ANN inputs. 2 MATERIALS AND METHODS The experimental procedure followed in the present work is illustrated in Figure 1. Each of these tasks is outlined in more detail in the following subsections. In summary: 1. QSPR descriptors were generated for training/testing data 2. Random forest regression was utilized to select highly in uential QSPR descriptors 3. Hyperparameters for ANNs using highly in uential de- scriptors as input variables were tuned to optimize per- formance 4. An ensemble of selected candidate ANNs was employed to obtain a prediction 2.1 Experimental Data Experimental CN data was obtained from the NREL Com- pendium of Experimental Cetane Number data [29] and Page 3 doi: 10.1115/ICEF2019-7170 Pre-Print Table 1: Products/wt% of fast pyrolysis of lignocellulosic biomass, catalytically upgrading with Ru/TiO 2and Phenol Fast Pyrolysis Product wt% of FP Catalytically Upgraded Product phenol 0-5 phenoxybenzene methanol 0-1 anisole 1-hydroxypropan-2-one 0-1 1-phenoxy-2-propanone furan-2-ylmethanol 0-1 2-phenoxymethylfuran 5-(hydroxymethyl)furan-2-carbaldehyde 0-5 5-phenoxymethylfurfural 2-hydroxy-cyclopent-2-en-1one 0-1 2-phenoxy-2-cyclopentene-1-one 2-methoxy phenol 0-5 1-methoxy-2-phenoxybenzene 2-methyl phenol 0-5 1-methyl-2-phenoxybenzene 4-methyl phenol 0-5 1-methyl-4-phenoxybenzene 2-methoxy-4-methyl phenol 0-5 2-methoxy-4-methyl-1-phenoxybenzene 3,5-dimethyl phenol 0-5 1,3-dimethyl-5-phenoxybenzene 3-ethyl phenol 0-5 1-ethyl-3-phenoxybenzene 4-ethyl-2-methoxy phenol 0-5 1-ethyl-4-phenoxybenzene other sources [20] [30] [31] totaling in 445 unique com- pounds. Methods for obtaining experimental CN values include derivations from blend measurements, the use of an IQT/CFR and other, unknown ignition delay meth- ods. Most experimental values were obtained using an IQT/CFR, as these methods are more accurate than blend- ing/other methods. Experimental YSI data was obtained from a variety of sources [15] [16] [17] [18], was measured us- ing laser-induced incandescence and other gas-phase steady- state measurement techniques, and totaled in 421 com- pounds. Simple molecular-input line-entry system (SMILES) strings were obtained for all compounds comprising the CN and YSI databases using MarvinSketch [32] and validated using compound entries on PubChem [33]. SMILES strings were then converted to MDL Mol les using Open Babel to generate three-dimensional geometry for the compound [34], and the Mol les were fed into PaDEL-Descriptor to gener- ate 1444 1D/2D and 431 3D QSPR descriptors for each compound [35]. QSPR descriptor values and experimental property values comprise input and target data respectively during ANN training. Table 1 displays a list of compounds that are expected be produced by performing fast pyrolysis on lignocellulosic biomass, and includes phenolic compounds, furanic com- pounds and benzenes, with expected wt%s [36][37]. Also included in Table 1 are the expected products of perform- ing hydrogenolysis using Ru/TiO 2catalysts on each com- pound resulting from fast pyrolysis, with phenol included in the catalysis process. For each compound listed in Table 1, QSPR descriptors were generated using the previously men- tioned techniques. The purpose of this exercise is to focus on a particular byproduct of fast pyrolysis and determine whether it can be e ectively upgraded into a more suitable fuel using catalysis. Figure 2: RMSE of CN predictions vs. number of important descriptors added to the ANN's input Figure 3: RMSE of YSI predictions vs. number of impor- tant descriptors added to the ANN's input Page 4 doi: 10.1115/ICEF2019-7170 Pre-Print 2.2 QSPR Descriptor Selection Random forest regression using Scikit-learn was employed to determine QSPR descriptor \importance", a numerical measurement indicating correlation between descriptor val- ues and experimental property values for CN and YSI { the higher the importance of a descriptor, the more it con- tributes to a correlation to a given property [28]. The sum of all descriptor importances is equal to one. It was found that 15-25 descriptors balances computation time and ANN pre- dictive accuracy. Appendix Tables A1 and A2 show the se- lected QSPR descriptors for CN and YSI respectively. The descriptors selected to represent CN and YSI were used as input variables in ANNs constructed for predicting CN and YSI, both during the hyperparameter tuning process and candidate training process. Figure 2 illustrates ANN performance (RMSE of CN pre- dictions for training data after 2500 epochs) versus the num- ber of important descriptors added as inputs to the ANN, and Figure 3 illustrates the same behavior for YSI. Perfor- mance for CN degrades signi cantly past 150 descriptor ad- ditions, and performance for YSI degrades signi cantly past 300 descriptor additions. Degrading performance can be at- tributed in part to the number of constant-value descriptors, as they are pernicious to the ANN; i.e., the ANN is unable to determine any relationship between them and a given experimental value. For example, \Nn", or the number of nitrogen atoms present in the compound, plays no signi - cant role during training as all training data is comprised of hydrocarbons. Additionally, descriptor importances where performance degradation occurs are very small in compar- ison to descriptors with high importance. The descriptor ranked 150th in importance for CN has an importance of 0.00044, and the descriptor ranked 300th in importance for YSI has an importance of 0.000029. Descriptors with very low importances likely contribute to performance degrada- tion, as the relationships between their values and an ex- perimental property value are more abstract than values of more important descriptors. 2.3 ANN Hyperparameter Tuning ANNs were trained using the Adam optimization function, which possesses ve hyperparameters that a ect the quality of the ANN's training [38]:  1: exponential decay rate 1 for moment estimates  2: exponential decay rate 2 for moment estimates : number to prevent division by zero in the algorithm implementation  : learning rate (i.e. step size) learning rate decay: degradation of learning rate after each learning epochTable 2: Tuned hyperparameters for CN model Hyperparameter Tuned Value 1 0.0482 2 0.8011  0.2752 1.0 Decay 0.2710 Neurons in 1st hidden layer 35 Neurons in 2nd hidden layer 31 Table 3: Tuned hyperparameters for YSI model Hyperparameter Tuned Value 1 0.3241 2 0.6434  0.9485 0.2622 Decay 0.0145 Neurons in 1st hidden layer 39 Neurons in 2nd hidden layer 10 In addition to these ve training variables, the optimal number of neurons in the ANN's hidden layer(s) must be determined. Given the number of hyperparameters that must be manually tuned under normal circumstances, an arti cial bee colony (ABC) was utilized to algorithmically determine the optimal sets of hyperparameters for both CN and YSI models. ABCs mimic the foraging behavior of hon- eybees to search a multidimensional search space of tunable variables and have been shown to out-perform genetic algo- rithms and other particle swarm optimization algorithms in tuning various hyperparameters in ANNs [39]. The ABC was supplied with a tness function to deter- mine the ability of ANNs to predict either CN or YSI for test set molecules, where a lower test set RMSE resulting from a given set of hyperparameters was deemed better perform- ing. The tness function supplied to the ABC constructed an ANN using the QSPR descriptors selected with random forest regression as input variables and was trained for 2000 epochs. 70% of experimental data was used in the learning set, and 20% of experimental data was used in the vali- dation set. Learning/validation data was shued for each bee to mimic the ensuing candidate ANN training proce- dure. Test set data, the remaining 10%, remained constant throughout the hyperparameter tuning process. The same test set data was used during candidate ANN training. The ABC was run for 20 search cycles with 50 employer bees for both CN and YSI datasets. Tables 2 and 3 show the tuned hyperparameters for CN and YSI respectively given the use of the Adam optimization function and two hidden layers. These hyperparameters were used during candidate ANN training and ensemble model construction. Page 5 doi: 10.1115/ICEF2019-7170 Pre-Print 2.4 Candidate ANN Training/Selection ANN training was performed with ECNet, an open source machine learning toolkit created to predict fuel properties of potential next-generation fuels [40]. A model is considered a collection of ANNs whose predictions are averaged to obtain a nal prediction, producing a more accurate prediction in a similar fashion to classi er ensembles [41]. Each ANN was chosen from a pool of ANN candidates, where the pool's goal was to optimally predict either CN or YSI. The ANN that was chosen from each pool achieved the lowest RMSE in predicting unseen data across all the pool's candidates. 5 pools, each with 75 candidate ANNs were trained for both CN and YSI. Each candidate ANN was supplied with a random learn- ing set and a random validation set, 70% and 20% of the total data respectively, while the remaining 10% of data re- mained constant for all candidates to measure their perfor- mance in predicting unseen data. The ANNs were trained using the backpropagation algorithm and the learning set, while the validation set measured the progress of the ANNs' learning. Once performance stopped improving on the vali- dation set, learning was terminated { this was done to pre- vent any over tting on the learning set. Figure 4 illustrates predictive accuracy (mean absolute error of CN predictions) versus the number of ANN training epochs for the learning, validation and test sets. In this example, training would ter- minate after 3000 epochs. Establishing an early stopping point for training has proven to be successful in allowing ANNs to generalize predictions for unseen data [42]. Shuf- ing learning/validation sets allowed each ANN to learn from a di erent representation of data, and each ANN's predictions are unique (often predicting slightly higher or slightly lower than the known experimental value). Conse- quently, the nal prediction of the model tends to be more accurate. Model performance was measured by determin- ing RMSE and the r-squared correlation coecient when predicting unseen data (the constant test set). 3 RESULTS AND DISCUSSION Figures 5 and 6 show parity plots between predicted values and experimental values for CN and YSI respectively, illus- trating the performance of the training (learning and vali- dation) set and the blind test set. The center dashed lines illustrate a 1:1 parity between predicted values and experi- mental values, and the outer dashed lines show bounds im- posed by the test set's RMSE. Test set RMSE and r-squared are signi cantly better for both CN and YSI, as candidate ANN's were selected based on their ability to predict unseen data. It is worth noting that experimental data, notably exper- imental CN values, may have inherent error associated with the experimental methods used to obtain the data. For ex- ample, some CN values used in the present work that were Figure 4: Mean absolute error of CN predictions vs. train- ing epochs Figure 5: CN parity plot illustrating predicted values vs. experimental values Figure 6: YSI parity plot illustrating predicted values vs. experimental values Page 6 doi: 10.1115/ICEF2019-7170 Pre-Print Table 4: Predicted CN/YSI for products of fast pyrolysis Compound Name CN (9:874)YSI (13:478) phenol 1.84 161.71 methanol 9.10 1.55 1-hydroxypropan-2-one 8.76 14.00 2-furanmethanol 7.16 81.89 5-hydroxy methyl furfural 7.02 215.34 2-hydroxy-cyclopent-2-en-1one 7.82 71.24 2-methoxy phenol 8.75 330.34 2-methyl phenol 2.44 237.00 4-methyl phenol 2.79 244.16 2-methoxy-4-methyl phenol 8.38 403.78 3,5-dimethyl phenol 1.42 357.99 3-ethyl phenol 5.60 315.51 4-ethyl-2-methoxy phenol 11.61 479.04 measured with an IQT are on average 4.8 CN units dif- ferent than CN values for the same compounds measured with a CFR [29]. Other ignition delay methods are more drastic, having an average di erence of 15.7 compared to IQT measurements. ANNs are reliant on the accuracy of training data, and it is currently unknown how errors in ex- perimental CN/YSI values a ect ANN training (other than reducing predictive accuracy overall). Tables 4 and 5 display CN and YSI predictions for the products of fast pyrolysis and catalytic upgrading re- spectively. Potential error in these predictions is de ned by the properties' respective test set RMSEs. As seen by the disparity between pre- and post-catalytic upgrad- ing CN values, upgraded compounds such as phenoxyben- zene (a result of phenol/phenol upgrading), 1-methoxy-2- phenoxybenzene (a result of 2-methoxy phenol/phenol up- grading) and 2-methoxy-4-methyl-1-phenoxybenzene (a re- sult of 2-methoxy-4-methyl phenol/phenol upgrading) have signi cantly higher CNs. Furan/phenol-based upgrades also have higher CNs. Ethyl/phenol-based upgrades did not im- prove CN signi cantly. All upgraded compounds have sig- ni cantly higher YSIs than their pre-upgraded products ex- cept for phenoxybenzene. Figures 7 and 8 display known property values versus values of the QSPR descriptor with the highest importance for CN and YSI respectively (CN vs. RotBFrac, YSI vs. piPC5). An exponential trend is apparent in both relation- ships, represented by Equations 1 and 2. CN= 0:435e5:822(RotBFrac)+ 19:131 (1) YSI = 6:732e0:912(piPC 5)+ 13:980 (2) If Equations 1 and 2 are used to predict the CN and YSI for known test set values, CN predictions have an r- squared correlation coecient of 0.706 and YSI predictionsTable 5: Predicted CN/YSI for products of cat. upgrading Compound Name CN (9:874)YSI (13:478) phenoxybenzene 12.01 168.85 anisole 9.71 649.02 1-phenoxy-2-propanone 11.11 342.27 2-phenoxymethylfuran 12.11 451.96 5-phenoxymethylfurfural 12.94 525.03 2-phenoxy-2-cyclopentene-1- one11.80 541.33 1-methoxy-2-phenoxybenzene 16.06 689.18 1-methyl-2-phenoxybenzene 6.77 684.55 1-methyl-4-phenoxybenzene 5.83 696.12 2-methoxy-4-methyl-1- phenoxybenzene17.06 746.00 1,3-dimethyl-5- phenoxybenzene5.75 747.66 1-ethyl-3-phenoxybenzene 8.90 714.75 1-ethyl-4-phenoxybenzene 8.51 718.21 Figure 7: Relationship between CN and RotBFrac Figure 8: Relationship between YSI and piPC5 Page 7 doi: 10.1115/ICEF2019-7170 Pre-Print have an r-squared correlation coecient of 0.979. While it appears the equation derived from piPC5 is relatively robust in its ability to provide accurate YSI predictions, the considerably lower r-squared value resulting from predicting CN using RotBFrac compared to using an ANN highlights the ANN's ability to infer information about compounds from a multidimensional input vector of QSPR descriptors. Based on the relationship between experimental CN and RotBFrac (fraction of non-terminal rotatable bonds), com- pounds with an abundance of rotatable bonds tend to have higher CN. This correlates well with investigations into the bulk modulus of fuels, where a higher bulk modulus re- sulting from fewer rotatable bonds in saturated compounds indicates a fuel is relatively incompressible [43]. This in- compressibility leads to a faster injection time, increasing the ignition delay, and results in a lower cetane number. Various autocorrelation indices, such as GATS and ATSC, were selected as important descriptors to represent CN. These autocorrelation indices measure the resemblance of neighboring point values, and when applied to the anal- ysis of a given compound indicate repeating patterns in the compound [44]. The higher the value of the autocorrelation index, the more signi cant a resemblance in two neighboring points (atoms) is [45]. The various weightings of the selected autocorrelation indices, being electronegativities, mass and charges, likely indicate that patterns of electron and mass distributions play a role in determining a compound's CN. The relationship between experimental YSI and piPC5 (a measurement of atom path lengths in a given compound) indicates that longer chains of atoms and larger compounds correlate to higher soot formation. This observation aligns with existing studies indicating fuel blends containing long carbon chains tend to emit more particles as a result of com- bustion [46]. 8 of 15 descriptors selected for YSI measure path length of varying orders, strengthening this argument. Figures showing relationships between properties and se- lected QSPR descriptors were included in Appendix Figures F1-F30. 4 CONCLUSIONS The models proposed in the present work can general- ize CN/YSI predictions for molecules not observed dur- ing training with RMSEs of 9.874 and 13.478 respectively. CN/YSI of expected products of fast pyrolysis and catalytic upgrading were predicted, and pre- and post-upgraded products were compared. Additionally, the QSPR descrip- tor selection methodology utilized in the present work o ers a considerable amount of insight into individual QSPR de- scriptor/experimental value relationships. From the predicted CN/YSI values displayed in Tables 4 and 5, it can be concluded that: Catalytically upgrading phenol, methoxy-phenolic, and furanic compounds using Ru/TiO 2catalysts andphenol during the catalysis process yields compounds with higher CN values, likely attributed to the lower oxygen content of the products. Catalytically upgrading ethyl-based compounds using Ru/TiO 2catalysts and phenol during the catalysis process yields compounds with no signi cant improve- ment in CN values. Catalytically upgrading products of fast pyrolysis us- ing Ru/TiO 2catalysts and phenol during the catalysis process yields compounds with signi cantly higher sooting propensity, with the exception of diphenyl ether. From analyzing experimental CN/YSI values and indi- vidual selected QSPR descriptor values, it can be concluded that: There is a positive correlation between the fraction of non-terminal rotatable bonds and CN. Autocorrelation indices, corresponding to patterns of atoms in compounds based on electronegativity, mass and charge, a ect the CN of a given compound. There is a strong, positive correlation between atom path length and sooting propensity. Based on these ndings, further pursuit of catalytically upgrading phenolic/furanic compounds in an experimental setting is recommended. Additionally, di erent additives during the catalysis process besides phenol should be as- sessed, such as furanic additives. In particular, this study has shown that a single byproduct of fast pyrolysis (phenol) can be upgraded into hydrocarbons with more suitable CN values at the expense of YSI. Future research will investi- gate the entire matrix of bio-oil constituents and possible catalytic products. Additional investigations into why the selected QSPR de- scriptors contribute to CN/YSI from a chemical standpoint, and what role upgraded compounds play in a mixture of traditional petroleum-based fuel should be performed. 5 ACKNOWLEDGEMENTS This work was funded by the U.S. Department of Energy under award DE-EE0008479 as part of the Co-Optimization of Fuels & Engines (Co-Optima) project sponsored by the U. S. Department of Energy (DOE) Oce of Energy E- ciency and Renewable Energy (EERE) Bioenergy Technolo- gies Oce (BETO). Page 8 doi: 10.1115/ICEF2019-7170 Pre-Print