Challenges of Modeling Steam Cracking of Heavy Feedstocks

Today single event microkinetic (SEMK) models for steam cracking of hydrocarbons allow simulating the conversion of heavy fractions. The key challenge to model the cracking behavior of these heavy feedstocks is related to feedstock reconstruction. The latter depends on the required level of molecular detail of the reaction network and of the feedstock characterization/ reconstruction model. This is illustrated for gas condensate feedstocks. Comparison of yield predictions with yields obtained in a pilot plant illustrate how uncertainties in the feedstock characterization propagate to the simulation results. The combination of a SEMK model and the feedstock reconstruction method based on maximization of the Shannon entropy allows to obtain accurate simulation results, provided that the specific density, the global PIONA weight or volume fractions, and the initial, 50% and final boiling point are known. Specifying less commercial indices results in a decrease of the agreement between simulated and experimentally obtained product yields. The developed methodology can be extended in a straight forward way to any heavy feedstock

An experimental and kinetic modelling study of pyrolysis and combustion of acetone-butanol-ethanol (abe) mixtures

Bio-butantol is being studied extensively as alternative to conventional fuels due to its propensity of decreasing soot formation and improving the octane number of gasoline, resulting in renewed interest in the the acetone-butanol-ethanol (ABE) fermentation process and combustion of mixtures of acetone, butanol and ethanol. Therefore in this work a detailed mechanism for the pyrolysis and oxidation of ABE is presented containing ∼200 species and ∼6000 reactions. The mechanism is validated against newly acquired pyrolysis data. Laminar flame speeds computations of alcohols and ABE complement the detailed comparisons of the pyrolysis data and allow to further validate the combustion behaviour of bio-butanol

Kinetic Monte Carlo Convergence Demands for Thermochemical Recycling Kinetics of Vinyl Polymers with Dominant Depropagation

As societal interest in recycling of plastics increases, modeling thermochemical recycling of vinyl polymers, e.g., via pyrolysis or reactive extrusion, becomes increasingly important. A key aspect remains the reliability of the simulation results with fewer evaluation studies regarding convergence as in the polymerization or polymer reaction engineering field. Using the coupled matrix-based Monte Carlo (CMMC) framework, tracking the unzipping of individual chains according to a general intrinsic reaction scheme consisting of fission, β-scission, and termination, it is however illustrated that similar convergence demands as in polymerization benchmark studies can be employed, i.e., threshold values for the average relative error predictions on conversion and chain length averages can be maintained. For this illustration, three theoretical feedstocks are considered as generated from CMMC polymer synthesis simulations, allowing to study the effect of the initial chain length range and the number of defects on the convergence demands. It is shown that feedstocks with a broader chain length distribution and a long tail require a larger Monte Carlo simulation volume, and that the head–head effects play a key role in the type of degradation mechanism and overall degradation rate. A minimal number of chains around 5 × 105 is needed to properly reflect the degradation kinetics. A certain degree of noise can be allowed at the higher carbon-based conversions due to the inevitable decrease in number of chains

The chemistry of chemical recycling of solid plastic waste via pyrolysis and gasification: State-of-the-art, challenges, and future directions

Chemical recycling of solid plastic waste (SPW) is a paramount opportunity to reduce marine and land pollution and to enable the incorporation of the circular economy principle in today's society. In addition to more conscious behaviors and wiser product design (“design for recycling”), a key challenge is the identification of the leading recycling technologies, minimizing the global warming potential in an industrially relevant context. Chemical recycling technologies based on pyrolysis and gasification are leading the way because of their robustness and good economics, but an improved understanding of the chemistry and more innovative reactor designs are required to realize a potential reduction of greenhouse gas emissions of more than 100 million tonnes of CO2-eq., primarily by more efficient use of valuable natural resources. The feed flexibility of thermal processes supports the potential of pyrolysis and gasification, however, the strong variability in time and space of blending partners such as multiple and co-polymers, additives, and contaminants (such as inorganic materials) calls for accurate assessment through fundamental experiments and models. Such complex and variable mixtures are strongly sensitive to the process design and conditions: temperature, residence time, heating rates – severity, mixing level, heat and mass transfer strongly affect the thermal degradation of SPW and its selectivity to valuable products. A prerequisite in improving design and performance is the ability to model conversion profiles and product distributions based on accurate rate coefficients for the dominating reaction families established using first-principle derived transport and thermodynamic properties. These models should also help with the “design for recycling” strategy to increase recyclability, for example by identifying additives that make chemical recycling difficult. Fundamental experiments of increased quality (accuracy, integrity, validity, replicability, completeness) together with improved deterministic kinetic models, systematically developed according to the reaction classes and rate rules approach, provide insights to identify optimal process conditions. This will allow shedding some light upon the important pathways involved in the thermal degradation of the feedstock and the formation/disappearance of desired or unwanted products. In parallel, the intrinsic kinetics of the dominating elementary reaction steps should be determined with higher accuracy, moving beyond single step kinetics retrieved from thermogravimetric analysis experiments. Together with more accurate kinetic parameters, better models to account for heat and mass transfer limitations also need to be further developed, since plastic degradation involves at least three phases (solid, liquid, gas), whose interactions should be accounted for in a more rigorous way. Novel experimental approaches (e.g. detailed feedstock and product characterization using comprehensive chromatographic techniques and photoionization mass spectrometry) and available computational tools (e.g. kinetic Monte Carlo, liquid phase, and heterogeneous theoretical kinetics) are needed to tackle these problems and improve our fundamental understanding of chemical recycling of SPW

Technical and market substitutability of recycled materials: Calculating the environmental benefits of mechanical and chemical recycling of plastic packaging waste

Most plastics are today mechanically recycled (MR), whereas chemical recycling (CR) is an emerging technology. Substitutability of virgin material is vital for their environmental performance assessed through life cycle assessment (LCA). MR faces the reduction in the material's technical quality but also the potential market because legal safety requirements currently eliminate applications such as food packaging. This study presents a data-driven method for quantifying the overall substitutability (OS), composed of technical (TS) and market substitutability (MS). First, this is illustrated for six non-food contact material (non-FCM) applications and three hypothetical future FCM applications from mechanical recyclates, using mechanical property and market data. Then, OS results are used in a comparative LCA of MR and thermochemical recycling (TCR) of several plastic waste fractions in Belgium. For mechanical recyclates, TS results for the studied non-FCM and FCM applications were comparable, but OS results varied between 0.35 and 0.79 for non-FCM applications and between 0.78 and 1 for FCM applications, reflecting the lower MS results for the current situation. Out of nine application scenarios, MR obtained a worse resource consumption and terrestrial acidification impact than CR in six scenarios. MR maintained the lowest global warming impact for all scenarios. This study contributes to an improved understanding of the environmental benefits of MR and TCR. Inclusion of other criteria (e.g. processability, colour, odour) in the quantification of the overall substitutability for MR products should be further investigated, as well as the environmental performance of TCR at industrial scale

Technical and market substitutability of recycled materials:Calculating the environmental benefits of mechanical and chemical recycling of plastic packaging waste

Most plastics are today mechanically recycled (MR), whereas chemical recycling (CR) is an emerging technology. Substitutability of virgin material is vital for their environmental performance assessed through life cycle assessment (LCA). MR faces the reduction in the material’s technical quality but also the potential market because legal safety requirements currently eliminate applications such as food packaging. This study presents a data-driven method for quantifying the overall substitutability (OS), composed of technical (TS) and market substitutability (MS). First, this is illustrated for six non-food contact material (non-FCM) applications and three hypothetical future FCM applications from mechanical recyclates, using mechanical property and market data. Then, OS results are used in a comparative LCA of MR and thermochemical recycling (TCR) of several plastic waste fractions in Belgium. For mechanical recyclates, TS results for the studied non-FCM and FCM applications were comparable, but OS results varied between 0.35 and 0.79 for non-FCM applications and between 0.78 and 1 for FCM applications, reflecting the lower MS results for the current situation. Out of nine application scenarios, MR obtained a worse resource consumption and terrestrial acidification impact than CR in six scenarios. MR maintained the lowest global warming impact for all scenarios. This study contributes to an improved understanding of the environmental benefits of MR and TCR. Inclusion of other criteria (e.g. processability, colour, odour) in the quantification of the overall substitutability for MR products should be further investigated, as well as the environmental performance of TCR at industrial scale

Active Machine Learning for Chemical Engineers: a Bright Future Lies Ahead!

By combining machine learning with design of experiments, so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible, and are better at investigating the processes spanning all length scales of chemical engineering. While the active machine learning algorithms are maturing, its applications are lacking behind. Three types of challenges faced by active machine learning are identified and ways to overcome them are discussed: the convincing of the experimental researcher, the flexibility of data creation, and the robustness of the active machine learning algorithms. A bright future lies ahead for active machine learning in chemical engineering thanks to increasing automation and more efficient algorithms to drive novel discoveries