Kinetic modeling of the pyrolysis chemistry of fossil and alternative feedstocks

Combustion, Chemistry, and Carbon Neutrality

Combustion is a reactive oxidation process that releases energy bound in chemical compounds used as fuels─energy that is needed for power generation, transportation, heating, and industrial purposes. Because of greenhouse gas and local pollutant emissions associated with fossil fuels, combustion science and applications are challenged to abandon conventional pathways and to adapt toward the demand of future carbon neutrality. For the design of efficient, low-emission processes, understanding the details of the relevant chemical transformations is essential. Comprehensive knowledge gained from decades of fossil-fuel combustion research includes general principles for establishing and validating reaction mechanisms and process models, relying on both theory and experiments with a suite of analytic monitoring and sensing techniques. Such knowledge can be advantageously applied and extended to configure, analyze, and control new systems using different, nonfossil, potentially zero-carbon fuels. Understanding the impact of combustion and its links with chemistry needs some background. The introduction therefore combines information on exemplary cultural and technological achievements using combustion and on nature and effects of combustion emissions. Subsequently, the methodology of combustion chemistry research is described. A major part is devoted to fuels, followed by a discussion of selected combustion applications, illustrating the chemical information needed for the future.

Statistical Optimization of Pyrolysis Process for Thermal Destruction of Plastic Waste Based on Temperature-Dependent Activation Energies and Pre-Exponential Factors

The massive increase in disposable plastic globally can be addressed through effective recovery methods, and one of these methods is pyrolysis. R software may be used to statistically model the composition and yield of pyrolysis products, such as oil, gas, and waxes to deduce an effective pyrolysis mechanism. To date, no research reports have been documented employing the Arrhenius equation in R software to statistically forecast the kinetic rate constants for the pyrolysis of high-density plastics. We used the Arrhenius equation in R software to assume two series of activation energies (Ea) and pre-exponential factors (Ao) to statistically predict the rate constants at different temperatures to explore their impact on the final pyrolysis products. In line with this, MATLAB (R2020a) was used to predict the pyrolysis products of plastic in the temperature range of 370–410 °C. The value of the rate constant increased with the temperature by expediting the pyrolysis reaction due to the reduced frequency factor. In both assumed series of Ea and Ao, a significantly larger quantity of oil (99%) was predicted; however, the number of byproducts increased in the first series analysis compared to the second series analysis. It was revealed that an appropriate combination of Ea, Ao, and the predicted rate constants could significantly enhance the efficiency of the pyrolysis process. The major oil recovery in the first assumed series occurred at 390 °C to 400 °C, whereas the second assumed series of Ea and Ao occurred at 380 °C to 390 °C. In the second series at 390 °C to 400 °C, the predicted kinetic rate constants behaved aggressively after 120 min of the pyrolysis process. The second assumed series and anticipated rate constants at 380 °C to 390 °C can be applied commercially to improve oil production while saving energy and heat.

Learning Molecular Representations for Thermochemistry Prediction of Cyclic Hydrocarbons and Oxygenates

Accurate thermochemistry estimation of polycyclic molecules is crucial for kinetic modeling of chemical processes that use renewable and alternative feedstocks. In kinetic model generators, molecular properties are estimated rapidly with group additivity, but this method is known to have limitations for polycyclic structures. This issue has been resolved in our work by combining a geometry-based molecular representation with a deep neural network trained on ab initio data. Each molecule is transformed into a probabilistic vector from its interatomic distances, bond angles, and dihedral angles. The model is tested on a small experimental dataset (200 molecules) from the literature, a new medium-sized set (4000 molecules) with both open-shell and closed-shell species, calculated at the CBS-QB3 level with empirical corrections, and a large G4MP2-level QM9-based dataset (40 000 molecules). Heat capacities between 298.15 and 2500 K are calculated in the medium set with an average deviation of about 1.5 J mol^-1 K^-1 and the standard entropy at 298.15 K is predicted with an average error below 4 J mol^-1 K^-1. The standard enthalpy of formation at 298.15 K has an average out-of-sample error below 4 kJ mol^-1 on a QM9 training set size of around 15 000 molecules. By fitting NASA polynomials, the enthalpy of formation at higher temperatures can be calculated with the same accuracy as the standard enthalpy of formation. Uncertainty quantification by means of the ensemble standard deviation is included to indicate when molecules that are on the edge or outside of the application range of the model are evaluated.