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Due to the complexity and diversity of polyolefinic plastic waste streams and the inherent non-selective nature of the pyrolysis chemistry, the chemical decomposition of plastic waste is still not fully understood. Accurate data of feedstock and products that also consider impurities is, in this context, quite scarce. Therefore this work focuses on the thermochemical recycling via pyrolysis of different virgin and contaminated waste-derived polyolefin feedstocks (i.e., low-density polyethylene (LDPE), polypropylene (PP) as main components), along with an investigation of the decomposition mechanisms based on the detailed composition of the pyrolysis oils. Crucial in this work is the detailed chemical analysis of the resulting pyrolysis oils by comprehensive two-dimensional gas chromatography (GC × GC) and ICP-OES, among others. Different feedstocks were pyrolyzed at a temperature range of 430–490 °C and at pressures between 0.1 and 2 bar in a continuous pilot-scale pyrolysis unit. At the lowest pressure, the pyrolysis oil yield of the studied polyolefins reached up to 95 wt%. The pyrolysis oil consists of primarily α-olefins (37–42 %) and n-paraffins (32–35 %) for LDPE pyrolysis, while isoolefins (mostly C9 and C15) and diolefins accounted for 84–91 % of the PP-based pyrolysis oils. The post-consumer waste feedstocks led to significantly less pyrolysis oil yields and more char formation compared to their virgin equivalents. It was found that plastic aging, polyvinyl chloride (PVC) (3 wt%), and metal contamination were the main causes of char formation during the pyrolysis of polyolefin waste (4.9 wt%).

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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.

Rationale for catalytic activity and product distribution in HDPE hydrocracking promoted by zeolite catalysts. Assessment of catalyst stability.

The current kinetic models for polystyrene (PS) pyrolysis contain many simplifications to reduce their size and the corresponding simulation time. Moreover, they are often based on rate coefficients determined using non -ideal experimental data featuring ambiguous process conditions with respect to mixing and temperature uni-formity. The practical interest of PS pyrolysis is the production of styrene monomer to be reused as a feedstock in the polymerization of styrene. In the present work, a lab-scale tree-based kinetic Monte Carlo (kMC) model is presented that differentiates between 18 reaction families and 26 end-group pairs to study the product yield variations for thermal degradation of PS. Model parameters follow from Bayesian optimization to experimental data recorded with an in-house micro-pyrolysis unit coupled with comprehensive two-dimensional gas chro-matography. Low chain length (CL) anionic-made PS is specifically considered to gain an understanding of the role of specific end-groups. The experimental yields of the major products (monomer: 74.7-80.8 wt%, dimer: 5.1-5.5 wt%, trimer: 1.6-7.7 wt%) are well-predicted with the fine-tuned parameters. The main reaction pathway in the formation of styrene monomer is end-chain beta-scission, while mid-chain beta-scission is primarily involved in the formation of the styrene dimer and trimer. Our model shows that the pyrolysis of low CL anionic-made PS leads to better rate coefficients than those obtained from state-of-the-art pyrolysis of long CL PS, in which end-groups play a much smaller role.

Plastic recycling rate in Europe is low, urging developments in recycling technology and strategies to increase circularity. Mechanical recycling (MR) has been the reference recycling technology for years, but in the near future chemical recycling (CR) options are expected to contribute to improve plastic circularity. This study uses a material flow analysis (MFA) at European level to provide quantitative estimates of the contribution of CR technologies to plastic recycling. Ten most used polymer types from five sectors are selected. A status quo 2018 scenario is modelled and compared to five potential future scenarios (in 2030) of plastic waste treatment, including one that only looks at improved waste collection, sorting, and MR technologies and four exploring developments of CR options. The so-called ‘missing plastics’, i.e., plastic waste generated but currently not accounted for in statistics, is considered in one of the future scenarios. The MFA results are compared by calculating four circularity indicators namely end-of-life recycling rate (EoL-RR), plastic-to-plastic rate, plasticto-chemicals rate, and plastic-to-fuels rate. The results indicate that in the most optimistic scenario the EoL-RR in 2030 is 73–80% (sum of plastic-to-plastic and plastic-to-chemical rates, excluding plastic-to-fuel rate), in which 41–46% is plastic-to-plastic from MR, 15–38% is plastic-to-plastic from CR and 19–35% is plastic-to-chemicals. The highest achievable plastic-to-plastic rate is estimated to be 61% (46% from MR and 15% from CR). In all future scenarios, the plastic-to-fuel rate is estimated to be 3–6%. The MFA results are also used to estimate potential recycled content availability in 2030, which suggest that closed-loop recycling and processing the ‘missing plastics’ will be necessary to achieve the targets.

Functionalization of bio-based aromatics offers an appealing opportunity toward a renewable way of fulfilling our current needs for chemicals and materials. Here, an atom-efficient Cu-catalyzed hydrogen borrowing strategy is presented, which successfully functionalizes aliphatic alcohols in aromatic monomers and dimers, derived from lignin by the reductive catalytic fraction (RCF), into tertiary dimethylamines. Kinetic experiments and ToF-SIMS analysis of the supported copper catalyst demonstrated a reduced catalytic activity for monomeric methoxyphenolics, such as guaiacol and syringols, relative to phenolic and nonphenolic model compounds. This is explained by the formation through demethylation and the adsorption of strong coordinating catechol species. The nature of the catalyst support proved to be key to cope with the catechol deactivation and keep high catalytic activity, with Cu supported on SiO2 outperforming earlier reported Cu-ZrO2. The hydrogen borrowing method was extended to real spruce wood-derived RCF lignin oil fractions, containing both phenolic mono- and oligomers. Special effort was done to identify the composition and molecular structure of the resulting phenolic dimer amines by GC × GC-TOF/MS and 1H-13C-NMR techniques. The stable lignin-derived tertiary amines displayed excellent antioxidant activity during an ABTS assay, highlighting the added value of the products obtained by the hydrogen borrowing upgrading strategy.

The current state-of-the-art analysis techniques for petroleum fractions has progressed substantially during the past decade. This has helped to further improve the lumping procedures and modeling approaches of these complex systems. Recent advances in gas chromatography (GC), GC-field ionization mass spectrometry (GC-FIMS), and comprehensive gas chromatography (GCXGC) have made it possible to determine the compositions of fractions with up to 45 carbon atoms and in some cases up to C80. The combination of MS techniques with other selective detectors and reversed-phase column combinations has made it possible to quantify even traces of heteroatomic compounds in these complex hydrocarbon matrices. Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS), in some cases combined with GCXGC for the lighter part, has pushed the characterization of larger macromolecules in particular asphaltenes. Matrix-assisted laser desorption/ionization (MALDI) is also used widely for this purpose but has the disadvantage that quantification is not obvious. The development of more detailed characterization techniques has not remained unnoticed in the petrochemical society and more recently in the petrochemical kinetic modeling society. More detailed characterization of petrochemical fractions has made the implementation of detailed kinetic models for simulation and optimization possible including more and more molecular detail. Additionally, advances in photoionization mass spectrometry (PI-MS) have allowed the detection of reactive intermediates and direct kinetic measurements in time-resolved experiments. It can only be expected that this trend will continue and that the application field will move from now primarily petrochemistry (from catalytic cracking, over hydrotreating, and hydrocracking to pyrolysis, combustion, and steam cracking) to larger-scale chemical recycling and biomass conversion processes.

Advanced plastic waste recycling via pyrolysis and subsequent steam cracking of pyrolysis oils has the potential to partly close the cycle between the petrochemical production of plastics and current end-of-life waste man-agement (i.e., downcycling, incineration, landfilling). However, the greatest obstacle is the complex composition of real plastic waste and their contamination with numerous additives and residues. Consequently, the lower quality of pyrolysis products compared to fossil feedstocks needs to be drastically improved by universally applicable upgrading and decontamination techniques. Techniques range from waste pre-treatment to reduce the halogen and additive contents, via in-situ techniques applied during pyrolysis to post-treatment techniques to purify the obtained pyrolysis oils using hydrotreatment, filtration or adsorption. Incorporated into a petro-chemical cluster, high-quality petrochemical feedstocks can be produced from plastic waste, which, combined with electrification, could lead to a CO2 emission reduction of >90% compared to incineration as the current mostly used disposal method.

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.

Anthropogenic climate change due to, among other causes, unhindered CO2 emissions is a major concern worldwide. The post combustion capture (PCC) process using a solvent, known as chemical absorption, is currently the most effective way to reduce CO2 emissions from large point sources. However, high capital investment costs when using the conventional packed bed absorber/desorber technology and high energy requirements during solvent regeneration are the primary obstacles for its large-scale implementation. Different process intensification (PI) technologies to desorb CO2 from the solvent have been introduced to mitigate the energy consumption compared to the conventional packed bed technology. This work reviews different technologies for intensification of CO2 desorption. In this context, rotating packed beds, microreactors, and membrane contactors have been explored as potential alternatives to intensify the desorption because of their superior mass and heat transfer. Alternative energy sources like ultrasound and microwaves have also been used to improve the desorption performance of conventional equipments. PI can also be realized by using novel solvents with improved desorption kinetics in combination with intensification equipment. Thus in this review, a comprehensive assessment of different existing PI technologies based on regeneration energies and regeneration efficiencies relative to conventional technology is presented. The intensification of mass transfer for the different technologies is compared, and a new parameter, named the regeneration factor, is proposed to evaluate the performance of PI equipment. This study outlines the advances in process intensification of CO2 desorption technologies to date and presents an overview of the merits and limits of all technologies.

ispartof: Progress In Energy And Combustion Science vol:96 status: published

The use of intensified equipment, heavily advocated by academia, encounters several hurdles to break through in the chemical industry. This has several reasons, such as the high capital expenditures and the associated long depreciation times, the strong focus on robustness/availability, preferably 8000 h per year, or the fact that benefits are only incremental. Next to the fact that all chemical companies are anxious to be the first to demonstrate new technology, a methodology or a road map for process intensification implementation in the chemical industry is beneficial. The presented work makes an attempt to bridge this gap by proposing a methodology perspective based on coupling: (1) lab-scale fundamental experiments, (2) computational fluid dynamics simulations & optimization, and (3) process simulations & assessment. This could enable low-cost process intensification innovation and help to bring the targeted process intensification technology to technology readiness level 5 and higher at a faster pace.

An important impediment to the acceptance of recyclates into a broader market is their unwanted odor after reprocessing. Different types of washing procedures are already in place, but fundamental insights into the deodorization efficiencies of different washing media are still relatively scarce. Therefore, in this study, the deodorization efficiencies of different types of plastics after washing with different media were determined via gas chromatography and mass spectrometry analysis. A total of 169 compounds subdivided into various chemical classes, such as alkanes, terpenes, and oxygenated compounds, were detected across all packaging types. Around 60 compounds were detected on plastic bottles, and around 40 were detected on trays and films. Owing to the differences in physicochemical properties of odor compounds, different deodorization efficiencies were obtained with different washing media. Water and caustic soda were significantly more efficient for poly(ethylene terephthalate) bottles with deodorization efficiencies up to 80%, whereas for polyethylene (PE) and polypropylene bottles, the washing media were relatively inefficient (around 30-40%). Adding a detergent or an organic solvent could increase deodorization efficiencies by up to 70-90% for these packaging types. A similar trend was observed for PE films having deodorization efficiencies in the range of 40-50% when washing with water or caustic soda and around 70-80% when a detergent was added. Polystyrene trays were most effectively deodorized with a detergent, achieving efficiencies up to 67%. Hence, this study shows that optimal washing processes should be tailored to specific packaging types to further improve deodorization and to eventually be able to meet ambitious European recycling targets.

Cycloalkanes are known to impact the metal-acid balance of the hydrocracking of n-alkanes with similar carbon number, however, how this impact varies with the carbon number of the components involved is still to be understood in more detail. For this purpose, the hydroconversion of n-octane and tert-butylcyclohexane, and n- decane and methylcyclohexane mixtures was investigated in this work over Pt/HUSY catalyst in both the ideal and the nonideal hydrocracking regime. Irrespective of the hydrocracking regime for pure n-octane, its con-version and isomer yields were significantly lowered upon admixture of tert-butylcyclohexane. On the other hand, n-octane admixture did not impact the tert-butylcyclohexane conversion nor its cyclic isomer yields. Upon methylcyclohexane admixture with n-decane, no impact was observed over the catalyst ensuring ideal hydro-cracking of pure n-decane. Over the catalyst with significantly lower Pt loading, which does not ensure ideal hydrocracking of n-decane, methylcyclohexane lowered the n-decane conversion. A slightly negative impact of n- decane admixture on methylcyclohexane conversion was also observed. The observed effects are mainly attributed to the differences in adsorption strengths within the zeolite pores on top of differences in alkane and cycloalkane adsorption on the metal sites.

Industrial-scale application of end-of-life plastic pyrolysis still faces significant problems, such as a lack of detailed knowledge on degradation mechanisms, parameters affecting the degradation, and formation pathways of the primary pyrolysis products. Today, the degradation mechanisms based on radical chain scission are insufficiently understood to explain the pyrolysis chemistry from feedstock-to-product comprehensively. In this study, the impact of operating conditions on the degradation mechanism is evaluated by pyrolyzing end-of-life and virgin high-density polyethylene (HDPE) in a continuous pilot-scale unit at temperature and pressure ranges of 450–504 ºC and 0.1–2 bara. The pyrolysis products were analyzed based on the detailed product composition obtained using comprehensive two-dimensional gas chromatography (GC×GC). A simplified kinetic mechanism was proposed to describe the main production pathways of the various components by considering the weakest points along the polymer chain. The results showed that the chain-end scission mechanism is the main mechanism in the HDPE pyrolysis process, even at low temperatures and pressures in the studied ranges. The pyrolysis of virgin HDPE under sub-atmospheric pressure, 0.1 bara, at 464 ºC reactor temperature, yields the highest concentration of linear hydrocarbons in the pyrolysis oil (93.2 wt%). At higher pressure and temperature, the cyclic and branched hydrocarbons had a higher share of up to 17.4 wt% compared to 6.8 wt% at vacuum pressure and lower temperature. Interestingly, the pyrolysis of end-of-life HDPE at atmospheric pressure and 450 ºC led to more cyclic and branched hydrocarbons (sum: 22.1 wt%), as opposed to that of virgin HDPE which is more prone to the production of linear hydrocarbons at the studied conditions. Regarding the additives and contaminants, a large amount of different metals and halogen atoms in the ppm range were detected in end-of-life HDPE, of which a small amount was still found in the pyrolysis oil.

The gas–solid vortex reactor (GSVR) has enormous process intensification potential. However the huge gas consumption can be a serious disadvantage for the GSVR in some applications such as fast pyrolysis. In this work, we demonstrate a recent novel design, where a stator-rotor vortex chamber (STARVOC) is driven by the fluid’s kinetic energy, to decouple the solids bed rotation and gas. Gas-solid fluidization by using air and monosized aluminum balls was performed to investigate the hydrodynamics. A constructed fluidization flow regime map for a fixed solids loading of 100 g shows that the bed can only be fluidized for a rotation speed between 200 and 400 RPM. Below 200 RPM, particles settle down on the bottom plate and cannot form a stable bed due to inertia and friction. Above 400 RPM, the bed cannot be fluidized with superficial velocities up to 1.8 m/s (air flow rate of 90 Nm3/h). The bed thickness shows some non-uniformities, being smaller at the top of the bed than at the bottom counterpart. However by increasing the air flow rate or rotation speed the axial nonuniformity can be resolved. The bed pressure drop first increases with increasing gas flow rate and then levels off, showing similar characteristics as conventional fluidized beds. Theoretical pressure drops calculated from mathematical models such as Kao et al. model agree well with experimental measurements. Particle velocity discrepancies between the top and bottom particles reveal that the impact of gravity cannot be completely neglected. Design guidelines and possible applications for further development of STARVOC concept are proposed based on fundamental data provided in this work.

Closing the carbon cycle and enabling a carbon circular economy in energy intensive industries (iron and steel, cement, refineries, petrochemistry and fertilizers) are topics of increasing interest to meet the demanding target of defossilizing the production. The focus of this perspective contribution is on CO

Research in chemical engineering requires experiments, which are often expensive, time-consuming, and labo-rious. Design of experiments (DoE) aims to extract maximal information from a minimum number of experi-ments. The combination of DoE with machine learning leads to the field of active learning, which results in a more flexible, multi-dimensional selection of experiments. Active learning has not yet been applied in reaction modeling, as most active learning techniques still require an excessive amount of data. In this work, a novel active learning framework called GandALF that combines Gaussian processes and clustering is proposed and validated for yield prediction. The performance of GandALF is compared to other active learning strategies in a virtual case study for hydrocracking. Compared to these active learning methods, the novel framework out-performs the state-of-the-art and achieves a 33%-reduction in experiments. The proposed active learning approach is the first to also perform well for data-scarce applications, which is demonstrated by selecting ex-periments to investigate the ex-situ catalytic pyrolysis of plastic waste. Both a common DoE-technique, and our methodology selected 18 experiments to study the effect of temperature, space time, and catalyst on the olefin yield for the catalytic pyrolysis of LDPE. The experiments selected with active learning were significantly more informative than the regular DoE-technique, proving the applicability of GandALF for reaction modeling and experimental campaigns.