The pyrolysis study of virgin and waste polyolefins using a comprehensive two-dimensional GC×GC

Climate change and environmental pollution have become a serious issue, requiring governments and industries to improve methods to recycle waste and to prevent greenhouse gas emissions as much as possible. Polyolefins are one of the largest types of polluting waste and pyrolysis has been accepted as a promising and economical solution for the suitable recovery of valuable products. Meanwhile, the structure of polyolefins plays a key role in determining the final pyrolysis products, thermal stability, and energy-consuming which has received less attention. In this paper, the impacts of virgin and waste high-density polyethylene (HDPE) and polypropylene (PP) on the final pyrolysis products have been evaluated. The amount of comonomer and tertiary carbons in the polyolefins structure along with the double bonds formed due to environmental and mechanical degradations can affect the type, amount, and size of different hydrocarbons and formed coke. The structure of waste polyolefins regarding double bonds content, linearity, and steric hindrance can affect the primary and secondary reactions such as chain scission and Diels-Alder reactions. The produced hydrocarbons, including α-olefins, n-paraffins, isoolefins, isoparaffins, diolefins, naphthenes, and aromatics, were evaluated using a comprehensive two-dimensional GC×GC instrument

Chemical recycling of plastic waste to monomers : effect of catalyst contact time, acidity and pore size on olefin recovery in ex-situ catalytic pyrolysis of polyolefin waste

The catalytic upgrading of post-consumer mixed polyolefinic waste pyrolysis vapors has been studied over five different solid acid catalysts to understand the effect of vapor-catalyst contact time and catalyst properties on the recovery of light olefins (C2-C4). HZSM-5 with Si/Al ratio of 11 and 27 produced the highest light olefins yields (83.4 wt% and 72.1 wt%, respectively) at a very short contact time of -5 ms while extending the contact time (& GE;23 ms) decreased the olefin yields and resulted in higher yields of aromatics (-30.0 wt% and -40.0 wt%, respectively). Short contact time (5 ms) was not sufficient for HZSM-22 to crack pyrolysis vapors to C2-C4 olefins (8.0 wt%) but increasing contact time improved C2-C4 yields to 53.5 wt% without increased production of aromatics, which may be attributed to its small pore size (shape selectivity). SAPO-34, SAPO-11, and Al-MCM-41 were not selective to light olefins at any studied contact time

Active learning-based exploration of the catalytic pyrolysis of plastic waste

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