Developing a parametric system model to describe the product distribution of steam pyrolysis in a Dual Fluidized bed

Steam pyrolysis is a thermochemical process that converts carbon-based materials into valuable gases. In general, the products of the reaction are syngas (H2,CO,CO2), low-molecular-weight hydrocarbon gases (methane, ethylene, and propylene), pyrolytic gasoline and oils, monoaromatic and polyaromatic species (tar), and carbonaceous residues (char) with ashes. However, the intricacy of the reactions comprising the process, the diversity of the product species, and the constraints linked to the sampling and measurement equipment, create a highly complex system. In this work, a method for data representation is presented based on a special Parametric System Model (PSM) that portrays product species measurements in a way that provides relevant information and valuable insights into the process. The method incorporates generic knowledge of the chemical nature of the reactions to create a constrained system in which the data can be expressed in parametric terms with meaningful statistical functions. The evaluated data were obtained from a high-temperature steam pyrolysis process performed in the 2–4-MW Dual Fluidized Bed reactor at Chalmers University using polyethylene as feedstock. The quantities of the hydrocarbon species detected in the gas product were taken for the PSM as a probabilistic system that can be described with a set of distribution functions. The carbon, hydrogen and oxygen balances were taken into account to build a constrained set of equations to find the parameters of the functions. The resulting model was proven to be useful as a prediction tool to quantify unmeasured carbon group species and to estimate process variables, such as the oxygen transport of the bed material. Also, it was demonstrated the potential of the model as a method to identify and estimate inconsistencies in the measurements, which improve the quality of the characterization data. The modeĺs outcomes find application in providing critical information for the control and evaluation of pyrolysis process and downstream operation of biorefineries

Correlations between product distribution and feedstock composition in thermal cracking processes for mixed plastic waste

Thermal conversion can transform the carbon-based waste into valuable chemicals to be further used in the petrochemical industry for a polymeric carbon circular economy. This work\u27s aim was to identify chemical correlations between the thermal-cracking products and the feedstock polymer composition when using highly blended waste streams. The challenges addressed were to: (i) access a pool of experimental data on the monomer recovery potential of real-life, highly blended waste streams; (ii) estimate the polymer constituents of the mixed waste streams; and (iii) formulate a generic and systematic method to identify correlations between feedstock constituents and cracking products. Different post-consumer waste streams were investigated, including cardboard, automotive shredder residues, cable stripping waste, and textile waste. The cracking experiments were performed in a 2–4MWth industrial-scale Dual Fluidized Bed system at 800 \ub0C using steam as fluidization agent. The polymeric constituents of the feedstocks were estimated using a numerical convex optimization method. To identify correlations between the feedstocks and products, a carbon bond-based classification was introduced. The experimental monomer yield ranged from 0.08 kg/kgf to 0.3 kg/kgf (f = feedstock) for the evaluated materials, corresponding to a carbon feedstock conversion rate between 14 % and 44 %. High yields of valuable monomers were obtained for the materials with the highest polyolefin content. The olefin monomer production correlated positively to the amount of aliphatic carbon in the original material and negatively to the carbon contents of the aromatic rings. From the trends observed, it was concluded that a framework based on carbon bond types is a promising approach to identify such correlations, which could serve as predictive tools for monomer recovery based on material\u27s composition and overall process conditions