Molecular-Level Kinetic Modeling of Resid Pyrolysis

Abstract

A molecular-level kinetic model of heavy oil pyrolysis was developed for a Venezuelan vacuum residue. Model development proceeded in three major steps: creation of a molecular description of the feedstock, generation of a reaction network, and model solution and parameter tuning. The feedstock composition, as described in previous work [Zhang et al. Energy Fuels 2014, 28, 1736−1749, was modeled in terms of probability density functions (PDFs) of three finite attribute groups (385 cores, 2 intercore linkages, and 194 side chains) and a PDF for each of a cluster-size and binding site distribution. These attributes, or molecule building blocks, represent more than 0.4 M molecules. An attribute reaction network was developed using the fundamental reaction chemistry for resid pyrolysis including 6274 reactions that fall into one of 11 reaction families. To make solution time tractable, we used attribute reaction modeling (ARM) which constrained the number of material balances to the number of attributes and irreducible molecules in the system or 2841 total equations. Therefore, reactor output was a set of reaction-altered attribute PDFs and molar amounts of irreducible molecules. The quantitative molecular composition of the reactor outlet was obtained through the juxtaposition of the final attribute PDFs. The properties of both the sampled molecules and the char fraction were obtained using quantitative structure−property relationships (QSPRs). The kinetic model was tuned using a least-squares objective function comparing the model predictions to measurements from the molecular to bulk-property level for all relevant boiling point fractions. The tuned model showed reasonably good agreement with the experimental measurements.

Thermal Conversion Modeling of Visbreaking at Temperatures below 400 °C

“…Fundamentally, a more gratifying approach is to incorporate knowledge about the compound classes, as described in some of the work by Mike Klein . It is actually quite interesting to see how his ideas to implement a tractable description using probability density functions developed over an extended period of time. − By incorporation of more knowledge of the chemical identity of species, the pragmatic utility of these models for refining was not lost.…”

“…When the chemical identity of species or compound classes is captured in the definition of pseudo-compounds, 9 the number of pseudo-compounds and potential reactions make regression of experimental data intractable as an approach to obtain kinetic parameters. Other strategies that draw on fundamental knowledge of the reaction chemistry are needed to obtain the kinetic parameters.…”

“…As long as a model describes the process in a way that captures the empirical reality, then it is not necessary to reflect all of the complexity that we know, only as much as is needed. To those familiar with the contributions of Dr. Michael (Mike) T. Klein, − it would not be surprising that modeling of thermal conversion was selected as the topic of this contribution in honor of him.…”

Enhanced Representation of Thermal Cracking Chemistry in the Context of Bitumen Partial Upgrading

“…Cracking of paraffins is considered to take place in or near the middle of the paraffinic chain, resulting in a smaller alkane and an olefin. Dealkylation reactions shorten the length of alkyl branches of aromatic and naphthenic cores, releasing either paraffinic or olefinic chain fragments depending on the cracking pathway . Thus, the remaining alkyl segment on a core unit that undergoes dealkylation may have a terminal double bond, as exemplified in Table .…”

“…There has been increasing interest in applying such detailed models to complex refinery processes, such as hydrotreating, − hydrocracking, − and fluid catalytic cracking . However, fewer efforts , have focused on developing process models of this nature for thermal cracking with commercially relevant feedstocks despite the considerable foundational work on hydrocarbon pyrolysis theory. − …”