Kiran K. Yalamanchi

Sulfur-containing fuels, such as petroleum fuels, natural gas, and biofuels, produce SO₂, SO_3, and other highly toxic gases upon combustion, which are harmful to human health and the environment, making it essential to understand their thermochemical properties. This study used high-level quantum chemistry calculations to determine thermodynamic parameters, including entropy, enthalpy, and specific heat capacity for an extensive set of sulfur-containing species. The B3LYP/cc-pVTZ level of theory was used for geometry optimization, vibration frequency, and dihedral scan calculations. To determine an appropriate ab initio method for energy calculation, the Bland-Altman diagram, a statistical analysis method, was employed to visualize the 298 K enthalpy value between experimental data and three sets of ab initio methods: G3, CBS-QB3, and the average of G3 plus CBS-QB3. The CBS-QB3 method exhibited the highest accuracy and was eventually selected for the energy calculation in this study. Thermochemical property parameters were then calculated with the MultiWell program suite for all these sulfur-containing species, and the results were in good agreement with the thermochemical data of organic compounds and the National Institute of Standards and Technology Chemistry WebBook databases. The thermochemical property database established in this study is essential to studying sulfur-containing species in desulfurization.

Summary: A reliable data set covering a parametric space of process conditions is essential for realizing catalyst informatics. A high-throughput screening (HTS) instrument was used to obtain a parametric data set to develop a detailed reaction microkinetic model for the oxidative coupling of methane (OCM) over a La2O3/CeO2 catalyst. The model was combined with well-validated gas-phase kinetics to describe the interactions between homogeneous and heterogeneous reaction chemistry. Methane and oxygen conversions and selectivities of ethylene, ethane, carbon monoxide, and carbon dioxide were measured experimentally in the temperature range of 500 to 800°C, CH4/O2 ratio between 3 and 13, and pressure between 1 bar and 10 bar. The proposed reaction network consists of 52 irreversible elementary steps describing catalytic reactions between 11 surface species and 123 reversible steps describing the contribution of gas phase between 25 species. A packed-bed reactor model was developed based on the dimensions of the experimental setup and catalyst characterization results to account for homogeneous-heterogeneous interactions. The proposed mechanism was tested and validated over a wide range of operating conditions and showed a reasonable fit with an average difference of less than 5% compared to experimentally measured methane conversion and selectivities of ethylene and ethane. Rate of production (ROP) and species sensitivity analyses were performed to identify the main reaction pathways and highlight the important reactions in the OCM.

In spite of increasing importance of cyclic hydrocarbons in various chemical systems, fundamental properties of these compounds, such as enthalpy of formation, are still scarce. One of the reasons for this is the fact that the estimation of thermodynamic properties of cyclic hydrocarbon species via cost-effective computational approaches, such as group additivity (GA), has several limitations and challenges. In this study, a machine learning (ML) approach is proposed using support vector regression (SVR) algorithm to predict standard enthalpy of formation of cyclic hydrocarbon species. The model is developed based on a thoroughly selected dataset of accurate experimental values of 192 species collected from the literature. The molecular descriptors used as input to the SVR are calculated via alvaDesc software, which computes in total 5255 features classified into 30 categories. The developed SVR model has an average error of approximately 10 kJ/mol. In comparison, the SVR model outperforms the GA approach for complex molecules, and can be therefore proposed as a novel data-driven approach to estimate enthalpy values for complex cyclic species. A sensitivity analysis is also conducted to examine the relevant features that play a role in affecting the standard enthalpy of formation of cyclic species. Our species dataset is expected to be updated and expanded as new data is available in order to develop a more accurate SVR model with broader applicability.

Detailed gas‐phase chemical kinetic models are widely used in combustion research, and many new mechanisms for different fuels and reacting conditions are developed each year. Recent works have highlighted the need for error checking when preparing such models, but a useful community tool to perform such analysis is missing. In this work, we present a simple online tool to screen chemical kinetic mechanisms for bimolecular reactions exceeding collision limits. The tool is implemented on a user‐friendly website, cloudflame.kaust.edu.sa, and checks three different classes of bimolecular reactions; (ie, pressure independent, pressure‐dependent falloff, and pressure‐dependent PLOG). In addition, two other online modules are provided to check thermodynamic properties and transport parameters to help kinetic model developers determine the sources of errors for reactions that are not collision limit compliant. Furthermore, issues related to unphysically fast timescales can remain an issue even if all bimolecular reactions are within collision limits. Therefore, we also present a procedure to screen ultrafast reaction timescales using computational singular perturbation. For demonstration purposes only, three versions of the rigorously developed AramcoMech are screened for collision limit compliance and ultrafast timescales, and recommendations are made for improving the models. Larger models for biodiesel surrogates, tetrahydropyran, and gasoline surrogates are also analyzed for exemplary purposes. Numerical simulations with updated kinetic parameters are presented to show improvements in wall‐clock time when resolving ultrafast timescales.

Thermodynamic properites of molecules are used widely in the study of reactive processes. Such properties are typically measured via experiments or calculated by a variety of computational chemistry methods. In this work, machine learning (ML) models for estimation of standard enthalpy of formation at 298.15 K are developed for three classes of acyclic and closed shell hydrocarbons viz. alkanes, alkenes and alkynes. Initially, an extensive literature survey is performed to collect standard enthalpy data for training ML models. A commercial software (Dragon) is used to obtain a wide set of molecular descriptors by providing SMILES strings. The molecular descriptors are used as input features for the ML models. Support Vector Regression (SVR) and Artificial Neural Networks (ANN) are used with a two-level K-fold Cross Validation (K-fold CV) workflow. The first level is for estimation of accuracy of both the ML models and the second level is for generation of the final models. The SVR model is selected as the best model based on error estimates over 10-fold CV. The final SVR model is compared against conventional Benson’s Group Additivity for a set of octene isomers from the database, illustrating the advantages of the proposed ML modeling approach.