Predicting Fuel Ignition Quality Using ¹H NMR Spectroscopy and Multiple Linear Regression

Abdul Jameel A.G., Elbaz A.M., Emwas A., Roberts W.L., Sarathy S.M.

Heavy fuel oil (HFO) is primarily used as fuel in marine engines and boilers to generate electricity. Nuclear magnetic resonance (NMR) is a powerful analytical tool for structure elucidation, and in this study, 1H and 13C NMR spectroscopy were used for the structural characterization of two HFO samples. The NMR data were combined with elemental analysis and average molecular weight to quantify average molecular parameters (AMPs), such as the number of paraffinic carbon, naphthenic carbon, aromatic hydrogen, olefinic hydrogen, etc., in the HFO samples. Recent formulas published in the literature were used for calculating various derived AMPs, such as aromaticity factor (fa), C/H ratio, average paraffinic chain length (n), naphthenic ring number (RN), aromatic ring number (RA), total ring number (RT), aromatic condensation index (φ), and aromatic condensation degree (Ω). These derived AMPs help in understanding the overall structure of the fuel. A total of 19 functional groups were defined to represent the...

Mueller C.J., Cannella W.J., Bays J.T., Bruno T.J., DeFabio K., Dettman H.D., Gieleciak R.M., Huber M.L., Kweon C., McConnell S.S., Pitz W.J., Ratcliff M.A.

The primary objectives of this work were to formulate, blend, and characterize a set of four ultralow-sulfur diesel surrogate fuels in quantities sufficient to enable their study in single-cylinder-engine and combustion-vessel experiments. The surrogate fuels feature increasing levels of compositional accuracy (i.e., increasing exactness in matching hydrocarbon structural characteristics) relative to the single target diesel fuel upon which the surrogate fuels are based. This approach was taken to assist in determining the minimum level of surrogate-fuel compositional accuracy that is required to adequately emulate the performance characteristics of the target fuel under different combustion modes. For each of the four surrogate fuels, an approximately 30 L batch was blended, and a number of the physical and chemical properties were measured. This work documents the surrogate-fuel creation process and the results of the property measurements.

Qing W., Chunxia J., Jianxin G., Wenxue G.

In this study, 1H NMR and quantitative 13C NMR spectroscopies were used to investigate the chemical transformations of oil during the retorting process. Oil samples were obtained by the pyrolysis of Indonesian oil sands at various heating rates and final retorting temperatures. A modified Brown–Ladner method was used to determine the structural parameters of the samples. The results showed that the effects of the final retorting temperature on the chemical structures were significant, because decomposition and polymerization occurred during the retorting process. The chemical structure was also significantly affected by the heating rate, which influenced the degrees of distillation and pyrolysis. For comparison and accuracy, the equations proposed by Poveda and Molina (J. Pet. Sci. Eng. 2012, 84–85, 1−7) were also used to calculate the structural parameters, based on a combination of the 1H and 13C NMR spectra. The observed trends in the changes in the structural parameters were similar for the two methods.

Lapuerta M., Hernández J.J., Sarathy S.M.

The auto-ignition quality of diesel fuels, quantified by their cetane number or derived cetane number ( DCN ), is a critical design property to consider when producing and upgrading synthetic paraffinic fuels. It is well known that auto-ignition characteristics of paraffinic fuels depend on their degree of methyl substitution. However, there remains a need to study the governing chemical functionalities contributing to such ignition characteristics, especially in the case of methyl substitutions, which have not been studied in detail. In this work, the auto-ignition of 2,6,10-trimethyltridecane has been compared with the reference hydrocarbons used for cetane number determination, i.e. n -hexadecane and heptamethylnonane, all of them being C16 isomers. Results from a constant-volume combustion chamber under different pressure and temperature initial conditions showed that the ignition delay time for both cool flame and main combustion events increased less from n -hexadecane to trimethyltridecane than from trimethyltridecane to heptamethylnonane. Additional experimental results from blends of these hydrocarbons, together with kinetic modelling, showed that auto-ignition times and combustion rates were correlated to the concentration of the functional groups indicative of methyl substitution, although not in a linear manner. When the concentration of these functional groups decreased, the first stage OH radical concentration increased and ignition delay times decreased, whereas when their concentration increased, H 2 O 2 production was slower and ignition was retarded. Contrary to the ignition delay times, DCN was correlated linearly with functional groups, thus homogenizing the range of values and clarifying the differences between fuels.

Elbaz A.M., Gani A., Hourani N., Emwas A., Sarathy S.M., Roberts W.L.

There is an increasing interest in the comprehensive study of heavy fuel oil (HFO) due to its growing use in furnaces, boilers, marines, and recently in gas turbines. In this work, the thermal combustion characteristics and chemical composition of HFO were investigated using a range of techniques. Thermogravimetric analysis (TGA) was conducted to study the nonisothermal HFO combustion behavior. Chemical characterization of HFO was accomplished using various standard methods in addition to direct infusion atmospheric pressure chemical ionization Fourier transform ion cyclotron resonance mass spectrometry (APCI-FTICR MS), high resolution 1H nuclear magnetic resonance (NMR), 13C NMR, and two-dimensional heteronuclear multiple bond correlation (HMBC) spectroscopy. By analyzing thermogravimetry and differential thermogravimetry (TG/DTG) results, three different reaction regions were identified in the combustion of HFO with air, specifically, low temperature oxidation region (LTO), fuel deposition (FD), and hig...

Yang S.Y., Naser N., Chung S.H., Cha J.

This paper was supported by Saudi Aramco FUELCOM Program and Clean Combustion Research Center, King Abdullah University of Science and Technology.

Dahmen M., Marquardt W.

The assessment of the ignition quality of a wide range of oxygenated hydrocarbons is one key challenge in the identification of novel molecular entities qualifying as biofuels or biofuel blend components derived from oxygen-rich lignocellulosic feedstocks. The present contribution summarizes the results from a comprehensive experimental screening campaign targeting a diverse set of pure-component oxygenated hydrocarbon fuels and their ignition characteristics in an ASTM D6890 Ignition Quality Tester (IQT). This constant-volume combustion chamber experiment has been chosen because of its rapid screening potential. The unique collection of data is utilized for group contribution modeling with the aim of unraveling relationships between the ignition delay observed in IQT experiments and the fuel’s molecular structure. We propose a simple, yet predictive, estimator for the derived cetane number of pure oxygenated hydrocarbons covering acyclic and cyclic, branched and straight, saturated and unsaturated hydroc...

Li S., Sarathy S.M., Davidson D.F., Hanson R.K., Westbrook C.K.

High molecular weight iso-paraffinic molecules are found in conventional petroleum, Fischer–Tropsch (FT), and other alternative hydrocarbon fuels, yet fundamental combustion studies on this class of compounds are lacking. In the present work, ignition delay time measurements in 2,7-dimethyloctane/air were carried out behind reflected shock waves using conventional and constrained reaction volume (CRV) methods. The ignition delay time measurements covered the temperature range 666–1216 K, pressure range 12–27 atm, and equivalence ratio of 0.5 and 1. The ignition delay time temperatures span the low-, intermediate- and high-temperature regimes for 2,7-dimethyloctane (2,7-DMO) oxidation. Clear evidence of negative temperature coefficient behavior was observed near 800 K. Fuel time-history measurements were also carried out in pyrolysis experiments in mixtures of 2000 ppm 2,7-DMO/argon at pressures near 16 and 35 atm, and in the temperature range of 1126–1455 K. Based on the fuel removal rates, the overall 2,7-DMO decomposition rate constant can be represented with k = 4.47 × 105 exp(−23.4[kcal/mol]/RT) [1/s]. Ethylene time-history measurements in pyrolysis experiments at 16 atm are also provided. The current shock tube dataset was simulated using a novel chemical kinetic model for 2,7-DMO. The reaction mechanism includes comprehensive low- and high-temperature reaction classes with rate constants assigned using established rules. Comparisons between the simulated and experimental data show simulations reproduce the qualitative trends across the entire range of conditions tested. However, the present kinetic modeling simulations cannot quantitatively reproduce a number of experimental data points, and these are analyzed herein.

Kang D., Lilik G., Dillstrom V., Agudelo J., Lapuerta M., Al-Qurashi K., Boehman A.L.

The ignition process of ethylcyclohexane (ECH) and its two isomers, 1,3-dimethylcyclohexane (13DMCH) and 1,2-dimethylcyclohexane (12DMCH) was investigated in a modified CFR engine. The experiment was conducted with intake air temperature of 155 °C, equivalence ratio of 0.5 and engine speed of 600 rpm. The engine compression ratio (CR) was gradually increased in a stepwise manner until autoignition occurred. It was found that ECH exhibited a significantly higher oxidation reactivity compared to its two isomers. The autoignition criterion was based on CO emissions and the apparent heat release rates. Ethylcyclohexane (ECH) indicated noticeable two stage ignition behavior, while less significant heat release occurred for the two isomers at comparable conditions. The mole fractions of unreacted fuel and stable intermediate species over a wide range of compression ratios were analyzed by GC–MS and GC–FID. Most of the species indicated constant rates of formation and the trends of relative yield to unreacted fuel are well in agreement with the oxidation reactivity in the low temperature regime. The major intermediate species are revealed as a group of conjugate olefins, which possess the same molecular structure as the original fuel compound except for the presence of a double carbon bond. Conjugate olefins were mostly formed through (1,4) H-shift isomerization during the low temperature oxidation of alkylcyclohexanes. Conformation analysis explains the reactivity differences in the three isomers as well as the fractions of intermediate species. The hydrogen availability located in alkyl substituents plays an important role in determining oxidation reactivity, requiring less activation energy for abstraction through the (1,5) H-shift isomerization. This reactivity difference contributes to building up the major intermediate species observed during oxidation of each test fuel. 12DMCH, whose ignition reactivity is the lowest, less favors β-scission of C–C backbone of cyclic ring, thereby resulting in lower concentrations of small olefins and higher concentrations of conjugate olefins and large oxygenated species in the low temperature regime, prior to autoignition.

Ahmed A., Goteng G., Shankar V.S., Al-Qurashi K., Roberts W.L., Sarathy S.M.

Gasoline is the most widely used fuel for light duty automobile transportation, but its molecular complexity makes it intractable to experimentally and computationally study the fundamental combustion properties. Therefore, surrogate fuels with a simpler molecular composition that represent real fuel behavior in one or more aspects are needed to enable repeatable experimental and computational combustion investigations. This study presents a novel computational methodology for formulating surrogates for FACE (fuels for advanced combustion engines) gasolines A and C by combining regression modeling with physical and chemical kinetics simulations. The computational methodology integrates simulation tools executed across different software platforms. Initially, the palette of surrogate species and carbon types for the target fuels were determined from a detailed hydrocarbon analysis (DHA). A regression algorithm implemented in MATLAB was linked to REFPROP for simulation of distillation curves and calculation of physical properties of surrogate compositions. The MATLAB code generates a surrogate composition at each iteration, which is then used to automatically generate CHEMKIN input files that are submitted to homogeneous batch reactor simulations for prediction of research octane number (RON). The regression algorithm determines the optimal surrogate composition to match the fuel properties of FACE A and C gasoline, specifically hydrogen/carbon (H/C) ratio, density, distillation characteristics, carbon types, and RON. The optimal surrogate fuel compositions obtained using the present computational approach was compared to the real fuel properties, as well as with surrogate compositions available in the literature. Experiments were conducted within a Cooperative Fuels Research (CFR) engine operating under controlled autoignition (CAI) mode to compare the formulated surrogates against the real fuels. Carbon monoxide measurements indicated that the proposed surrogates accurately reproduced the global reactivity of the real fuels across various combustion regimes.

Stratiev D., Marinov I., Dinkov R., Shishkova I., Velkov I., Sharafutdinov I., Nenov S., Tsvetkov T., Sotirov S., Mitkova M., Rudnev N.

A database of 140 diesel fuels having cetane numbers in the range of 10–70 points; densities at 15 °C; and distillation characteristics according to ASTM D-86 T10%, T50%, and T90% was used to develop new procedures for predicting diesel cetane numbers by application of the least-squares method (LSM) using MAPLE software and an artificial neural network (ANN) using MATLAB. The existing standard methods of determining cetane-index values, ASTM D-976 and ASTM D-4737, which are correlations of the cetane number, confirmed the earlier conclusions that these methods predict the cetane number with a large variation. The four-variable ASTM D-4737 method was found to better approximate the diesel cetane number than the two-variable ASTM D-976 method. The developed four cetane-index models (one LSM and three ANN models) were found to better approximate the middle-distillate cetane numbers. Between 4% and 5% of the selected database of 140 middle distillates were samples with differences between their measured cetan...

Sánchez-Borroto Y., Piloto-Rodriguez R., Errasti M., Sierens R., Verhelst S.

This work deals with obtaining models for predicting the cetane number and ignition delay using artificial neural networks. Models for the estimation of the cetane number of biodiesel from their methyl ester composition and ignition delay of palm oil and rapeseed biodiesel using artificial neural networks were obtained. For the prediction of the cetane number model, 38 biodiesel fuels and 10 pure fatty acid methyl esters from the available literature were given as inputs. The best neural network for predicting the cetane number was a conjugate gradient descend (11:4:1) showing 96.3% of correlation for the validation data and a mean absolute error of 1.6. The proposed network is useful for prediction of the cetane number of biodiesel in a wide range of composition but keeping the percent of total unsaturations lower than 80%. The model for prediction of the ignition delay was developed from 5 inputs: cetane number, engine speed, equivalence ratio, mean pressure and temperature. The results showed that the neural network corresponding to a topology (5:2:1) with a back propagation algorithm gave the best prediction of the ignition delay. The correlation coefficient and the mean absolute error were 97.2% and 0.03 respectively. The models developed to predict cetane number and ignition delay using artificial neural networks showed higher accuracy than 95%. Hence, the ANN models developed can be used for the prediction of cetane number and ignition delay of biodiesel.

Kang D., Kirby S., Agudelo J., Lapuerta M., Al-Qurashi K., Boehman A.L.

The impact of a branched and unsaturated compound (diisobutylene) mixed with simple hydrocarbons such as n-heptane and isooctane in binary blends on the autoignition behavior were investigated in a modified cooperative fuel research (CFR) engine at an equivlanece ratio of 0.5 and intake temperature of 120 °C. From this test condition, a homogeneous charge of fuel and intake air can be achieved. The test fuels were prepared by addition of 5–20 vol % diisobutylene into n-heptane and isooctane. The engine compression ratio (CR) was gradually increased from the lowest point to the point where significant high temperature heat release (HTHR) was observed, and this point is also referred to as the critical compression ratio (CCR). Heat release analysis showed that each n-heptane blend had a noticeable low temperature heat release (LTHR), which was not observed in the isooctane blends. The gradual addition of diisobutylene into each primary reference fuel contributed to retarded high temperature heat release in ...

Yanowitz J., Ratcliff M., McCormick R., Taylor J., Murphy M.

Souza C.R., Silva A.H., Nagata N., Ribas J.L., Simonelli F., Barison A.

The cetane number (CN) is one of the most important parameters regarding diesel fuel oil quality, mainly ignition properties. Traditionally, the CN determination is performed by a quite laborious and high-cost method on an explosion engine. On the other hand, nuclear magnetic resonance (NMR) spectroscopy is increasing as a versatile tool for quality control in several areas, such as petroleum and fuels, which permits the fast and direct investigation on samples. In this work, two NMR-based methods for CN determination based on multivariate calibration were developed with advantages of time saving, without the need of any sample treatment.

Huggins P., Martin J.S., Downey A.R., Won S.H.

Parker R.P., Kelly M., Watson-Murphy T., Ghaani M.R., Dooley S.

Li Y., Ma F., Wang Z., Chen X.

Islam K.M., Ahmad N., Ahmed U., Siddiqui M.N., Ummer A.C., Abdul Jameel A.G.

AbstractMicrowave (MW)‐assisted catalytic pyrolysis represents a promising method for transforming petroleum‐based plastic waste into valuable chemicals, offering a pathway towards more sustainable circular economy. In this study, catalytic pyrolysis of low‐density polyethylene (LDPE) was conducted under MW irradiation. The influence of various catalyst types (HZSM‐5, Ga/ZSM‐5, Ga/Ni/ZSM‐5, Ga/Co/ZSM‐5, and Ga/Cu/ZSM‐5) on product yield and distribution was examined. The results revealed that the Ga/ZSM‐5 catalyst yielded the maximum liquid oil, approximately 41%. Ga/Ni/ZSM‐5 performed excellently in the production of long‐chain olefins, constituting about 27% of the liquid fraction. However, Ga/Co/ZSM‐5 led to the production of heavy pyrolysis oil containing nearly 25% long‐chain paraffins, rendering it unsuitable for producing high‐value chemicals. Conversely, the Ga/Cu/ZSM‐5 catalyst yielded an aromatic‐rich pyrolysis oil, with benzene derivatives constituting approximately 90% of the liquid oil fraction, thus proving to be a suitable catalyst for the intended application. The liquid product distribution was compared with a petroleum assay by SimDist, and this suggested that utilizing the HZSM‐5 catalyst could yield an 86.4% naphtha fraction. The study also revealed that the Ga/Cu/ZSM‐5 catalyst generated the largest amounts of hydrogen and syngas, as determined by a MicroGC analysis of the gas products. This catalyst also exhibited the maximum coke deposition (1.35%) postreaction, which was attributed to its high aromatic hydrocarbon content in the pyrolysis oil and maximal hydrogen release. A comparison of fresh and spent catalyst properties was conducted to gain insights into catalyst activity and to correlate the effects of metal doping on product distribution. These findings underscore the potential of MW‐assisted catalytic pyrolysis, particularly with the Ga/Cu/ZSM‐5 catalyst, for the efficient conversion of plastic waste into valuable chemicals, thereby contributing to sustainable resource utilization and environmental conservation.

Lawal R., Farooq W., Abdulraheem A., Abdul Jameel A.G.

Boehm R.C., Yang Z., Bell D.C., Faulhaber C., Mayhew E., Bauder U., Eckel G., Heyne J.S.

Creton B., Brassart N., Herbaut A., Matrat M.

Sarathy S.M., Eraqi B.A.

Chen Y., Zheng Z., Lu Z., Wang H., Wang C., Sun X., Xu L., Yao M.

The design of high-performance fuels is crucial for achieving clean and efficient combustion of engines. In the current study, a framework for machine learning (ML) model-based fuel design is presented to identify compounds with desired properties for internal combustion engine (ICE) applications. Descriptors computed from newly proposed structural and positional-based group extraction method in this study are used as input in ML models due to the simplicity and computational-efficiency. The ML models were trained and validated using publically available experimental data for up to 1135 compounds. The results demonstrated a high level of predictive accuracy, with R2 values generally exceeding 0.99 for 11 physicochemical properties including melting point, boiling point, enthalpy of vaporization, surface tension, dynamic viscosity, low heating value, liquid density, yield sooting index, cetane number, research octane number and motor octane number. Furthermore, by employing the developed ML models to predict new data, a Fuel Property Database containing 1135 fuels with 12 fuel properties is established, enhanced by an interface for a user-friendly Fuel Physicochemical Properties Prediction Tool that facilitates swift property predictions and database expansion. The Pearson correlation coefficient (PCC) approach is subsequently employed to assess the correlation between descriptors and predicted or experimental properties. The strong agreement between the correlations affirms the effective predictive performance of the ML models within the calibrated data range. Additionally, a PCC analysis is conducted to reveal the individual effects of influential factors and their interactions, highlighting significant features like molecular weight size, degree of branching, and functional group content that influence physical and chemical properties, thereby providing valuable insights for fuel design and optimization. Finally, a initial comprehensive data-driven screening was carried out to identify potential fuel candidates that meet the key property limits for combustion applications in both spark-ignition (SI) and compression-ignition (CI) engines.

Flora G., Karimzadeh F., Kahandawala M.S., DeWitt M.J., Corporan E.

This study presents a computational methodology for determining the Derived Cetane Number (DCN) of practical aviation fuels. The proposed approach integrates a novel Quantitative Structure-Property Relationship (QSPR) model designed to predict DCN for hydrocarbon species and mixtures with fuel composition analysis obtained through Two-Dimensional Gas Chromatography (GCxGC). The QSPR model used 20 independent variables computed based on selected hydrocarbon molecular descriptors, including functional groups and distance-based topological indexes. The multivariate regression analysis was used to train the QSPR model based on a dataset consisting of 48 individual hydrocarbon species and 157 surrogate mixtures. The model demonstrated robust predictive capabilities with high coefficients of determination (R2) of 0.96 on the training dataset and 0.94 on the independent testing dataset. The latter consisted of 43 surrogate mixtures formulated both in-house and sourced from archived literature. The application of the QSPR model for practical jet fuels involves specifying the detailed jet fuel compositions using GCxGC analysis and a randomization algorithm based on a database featuring over 17,000 distinct hydrocarbon species. The overall model's performance on practical jet fuels aligns closely with its performance on the training and testing datasets, affirming its practical utility. To enhance prediction accuracy of the proposed computational approach for practical jet fuels, density was explored as a potential constraining property to narrow randomization results with those detailed composition having a density similar to the actual fuel. In this regard, a novel relationship was established to predict fuel densities based on compositional characteristics. Despite the promising results in density prediction, this study indicates that density alone is insufficient to effectively constrain randomized compositions for significantly improved DCN predictions, and thus, further development is required.

Qin X., Hou L., Ye L., Wang T., Pu X., Han X., Jiang P., Liu J., Huang S.

Based on the Structure Oriented Lumping (SOL) method and the Artificial Neural Network (ANN) algorithm, a SOL-ANN property prediction model was constructed to predict the properties of molecules and products in the fluid catalytic cracking (FCC) process. The properties of each structural vector in the molecular composition matrices of gasoline and diesel were calculated. The influences of reaction temperature on the properties of gasoline and diesel were investigated from the perspective of molecular composition. When the reaction temperature increased from 490 °C to 510 °C, the content of aromatics and olefins in gasoline and the content of aromatics in diesel increased, resulting in the research octane number (RON) of gasoline increasing by 2.96 units and the cetane number (CN) of diesel decreasing by 1.37 units. Using the molecular composition information of products to calculate the properties of molecules and products could guide the product quality evaluation and process optimization.

Krivdin Leonid B.

Present review covers the most recent advances in the NMR studies of oil refining products - light fractions, different types of fuels, heavy fractions, and crude oil itself. By no means does it discuss NMR applications in this field in detail providing only a brief overview of different NMR methods used for structural elucidation of the oil refining products together with crude oil with particular emphasis on recent achievements and advances in this field.The bibliography includes 48 references.

Ahmed Qasem M.A., Alquaity A.B., Ahmed U., Al-Mutairi E.M., Abdul Jameel A.G.

In accordance with the European commission’s renewable energy proposal for 2030, advanced biofuels should be blended at a minimum of 3.6 % with conventional fuels used for powering internal combustion engines. Ethers, which may be produced using modern production methods from sustainable sources like biomass, may be crucial in this regard in the future. This study demonstrates the impact of diethyl ether as a blend component (mixed with diesel) on the characteristics of the soot generated from the diethyl ether-atmospheric diesel's combustion. The soot used in this work came from a smoke point lamp that burned a mixture of diesel and diethyl ether as per ASTM D1322 standard. Diethyl ether (DEE) and diesel (D) were combined in the following blends: DEE5D (5 % diethyl ether and 95 % diesel v/v), and DEE20D (20 % diethyl ether and 80 % diesel v/v). The obtained soot was characterized in detail using the following: elemental analyzer, X-Ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), scanning electron microscopy (SEM), transmission electron microscopy (TEM), RAMAN spectroscopy, thermogravimetric analysis (TGA), and proton nuclear magnetic resonance (1H NMR) spectroscopy. According to the results from XRD and Raman spectroscopy, the soot nanostructure is influenced by the amount of diethyl ether present in the mixture, which raises the reactivity. The results of the elemental analyzer and 1H NMR matched those of the TGA's soot reactivity (oxidations). The results from the SEM and TEM tests, however, were not enough to support the experimental findings.

Zhang X., Khandavilli M., Gautam R., AlAbbad M., Li Y., Chatakonda O., Kloosterman J.W., Middaugh J., Mani Sarathy S.

Gasification of heavy petroleum residues can convert low-value feedstock to hydrogen-rich syngas, which can further be used for power generation and/or chemical production. The present work proposed an updated functional-group-based approach (FGMech) to modeling the gasification of a vacuum residue oil (VRO), which is helpful for better understanding the detailed kinetics of the gasification and improving gasifier performance. Elemental, average molecular weight (AMW) and nuclear magnetic resonance (NMR) analyses were conducted experimentally to characterize VRO, including elemental composition, average molecular formula and the functional group distribution, which were further used for model construction. A lumped mechanism for VRO devolatilization was constructed based on the updated FGMech approach: stoichiometric parameters of stable gases, tars and char were obtained from experiments, while those of radicals were based on the multiple linear regression (MLR) correlations; thermodynamic and kinetic parameters were derived from Benson group additivity method and rate rules, respectively. A merged detailed model was adopted for describing the conversion of gases and tars, and a global model was used for char. To test the reliability of the present model approach, Orimulsion gasification experiments from literature were simulated using an integrated perfectly stirred reactor (PSR) and plug flow reactor (PFR) model. It shows that the present model can reasonably predict measured results under various equivalence ratios, and has better performance on the prediction of CH4 compared with most literature models. Based on model analyses, syngas comes from the conversion of C2H4, H2S, CH4 and char in different gasification stages. Benzene, toluene, naphthalene and 1-methylnaphthalene are initial tar species considered in the devolatilization of the VRO. They can undergo hydrogen abstraction acetylene addition (HACA) and C3H3/C5H5 addition reaction pathways to produce large PAHs.

Sheyyab M., Lynch P.T., Mayhew E.K., Brezinsky K.

Derived Cetane Number (DCN) serves as a critical indicator for assessing the ignition quality of fuels in diesel engines. Training generalized regression models for DCN is challenging due to limited data availability. In this study, we propose a novel semi-supervised approach that combines real hydrocarbon mixtures and synthetically generated mixtures to overcome this data scarcity obstacle. The synthetic mixtures are generated using a Sequential Least Squares Programming (SLSQP) optimization method, targeting comprehensive coverage of UNIFAC chemical functional group compositions. By utilizing a dataset of real and synthetic mixtures, an Artificial Neural Network (ANN) model is trained based on the UNIFAC chemical functional group composition of fuels to improve DCN prediction accuracy and model reliability compared to models trained solely on real data. The improved model achieves excellent performance on the training and testing datasets, as indicated by high R2 Score, Mean Square Error (MSE), and Mean Absolute Error (MAE) values. Moreover, the model demonstrates accurate predictions on ten real fuels and their eighteen mixtures, with 100 % of samples falling within ±10 % of the measured DCN by an Ignition Quality Tester (IQT). The results demonstrate the potential of augmenting real data using appropriate data generation techniques to improve the representativeness and predictive capabilities of models in the presence of limited experimental data.