Dec 26, 2013 - Plots of C# vs DBE for species containing one and two S-atom before and ..... 4 h for optimal PDFs parameters on an i7 desktop computer with.
Article pubs.acs.org/EF
Molecular Representation of Petroleum Vacuum Resid Linzhou Zhang,†,‡ Zhen Hou,‡ Scott R. Horton,‡ Michael T. Klein,*,‡ Quan Shi,† Suoqi Zhao,*,† and Chunming Xu† †
State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing 102249, China Department of Chemical and Biomolecular Engineering and the Energy Institute, University of Delaware, Newark, Delaware 19716, United States
‡
S Supporting Information *
ABSTRACT: A novel methodology was extended for modeling the detailed composition of petroleum heavy vacuum resid fractions. The resid molecules were organized in terms of basic structural attributes: cores, intercore linkages, and side chains. The identities of the structural attributes were determined both from the extrapolation of chemical characteristics of light petroleum and the analysis of detailed mass spectrometric measurement of heavy resid fragmentation products. A building block library was constructed containing ∼600 attributes. The molecular composition was constructed by the combination of attributes, or building blocks, into discrete molecules. The quantitative abundance of each molecule was determined by the juxtaposition of a set of structural attribute probability density functions (PDFs) constraining pure hydrocarbon and heteroatom mixtures. Quantitative structure−property relationships (QSPRs) were applied to calculate the bulk properties of both the constructed molecules and the mixture. The adjustable parameters of the PDFs were determined using an optimization loop that employed an objective function that contained a term for each of the available analytical data points. The resulting optimal molecular compositions were in good agreement with the experimental structural information.
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INTRODUCTION
Resid, as the highest boiling point petroleum fraction, has the highest complexity, which creates challenges for measurements. Recently, the most substantial progress in heavy resid composition analysis is the successful application of high field Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS).6−8 With ultrahigh resolution, detailed qualitative composition information can be measured in terms of carbon number, double bound equivalent (DBE), or chemical formula. It has been extensively applied in feedstock characterization and molecular conversion of the petroleum upgrading process.9−19 However, usage of FT-ICR MS data as input for molecular-based models is made more difficult due to uncertainties of composition concentrations. These uncertainties arise because the signal responses of analytes are highly dependent on their molecular structure for popular ionization methods, such as electrospray ionization (ESI) or atmospheric pressure photoionization (APPI).20 Moreover, probing molecular identity by FT-ICR MS is still a challenge as it is hard to recognize structure (e.g., ring configuration) from the chemical formula. Because of these limitations, a set of measurements is generally used to create input for a molecular-based model. Table 1 lists the common measurement methods and the obtained information for petroleum resid. It is apparent that each measurement only reveals limited aspects of structure. Current modeling methods use computational representations of molecules and quantitative
The continued depletion of conventional crude oil will cause heavy petroleum feedstocks to be used more and more in refinery operations. Heavy petroleum usually refers to feedstocks with high density or boiling point, including heavy crude oil and distillation resid (e.g., vacuum resid). These resid feedstocks often cause problems for upgrading processes as they have high viscosity, aromaticity, heteroatom species, and metallic compounds.1 The economical utilization of heavy feedstocks is a critical challenge for modern refineries. Process modeling plays a very important role for chemical process design. In recent years, due to the increasing technical, economic, and environmental concerns, molecular level information is being implemented into process model in order to optimize both yield and quality of the products. For example, sulfur contents in gasoline and diesel must be strictly controlled to meet environmental specifications. This demand is beyond the ability of traditional lumped models for petroleum processes and therefore motivates model development on a molecular basis.2 The starting point of molecule-based model development is to determine the molecular composition of the feedstock. However, even state-of-the-art analytical measurements fail to give both quantitative and qualitative molecular compositions for resid fractions. One challenge with resid is its high boiling point. Traditional gas chromatography (GC) based methods, such as gas chromatography field ionization time-of-flight mass spectrometry (GC-FI-ToF MS), cannot be applied for resid feedstock as the resid molecules cannot be sufficiently vaporized.3,4 Resid fractions are also very complex mixtures of thousands of unique molecules. The structural isomer number of petroleum molecules grows exponentially with boiling point.5 © 2013 American Chemical Society
Special Issue: 14th International Conference on Petroleum Phase Behavior and Fouling Received: October 17, 2013 Revised: December 21, 2013 Published: December 26, 2013 1736
dx.doi.org/10.1021/ef402081x | Energy Fuels 2014, 28, 1736−1749
Energy & Fuels
Article
(VR).36−38 Recently, Pyl et al. demonstrated the compositional modeling of a crude fraction by sampling the molecular type with structural attribute distributions and carbon number distributions for each homologous series.39 Although there are many literature reports, all compositional models for heavy resid are still developing their capabilities. The first challenge is how to overcome the complexity of composition. The result by FT-ICR MS qualitatively shows that there are several classes of species which are defined by the combination of functional groups and are located in different DBE and carbon number regions.8 It indicates that the number of the constructed molecules should be large enough to cover all the structural distributions. The second challenge is to find accurate molecular identites, which requires the implementation of more experimental information during model development. Recent reports that use the fragmentation technique in FT-ICR MS provide a practical way to analyze the structure of resid molecule building blocks.15,40,41 If this information can be used for molecular construction, the model accuracy will be significantly improved. In the present paper, we propose a novel compositional model development method to represent petroleum resid composition. By inferring structural information from fragmentation mass spectrometry and knowledge of light petroleum fractions, a structural attribute library was built. Through sampling with PDFs and juxtaposition of the attributes, ∼0.4 million molecules were constructed containing all the common classes of species. Choosing appropriate PDF form during sampling is also discussed.
Table 1. Typical Measurement Methods for Heavy Petroleum measurement name density analysis elemental analysis SARA compositional analysis vapor pressure osmometry (VPO) gel permission chromatography (GPC) low resolution mass spectrometry (e.g., ToF MS) traditional simulated distillation (SimDis) high-temperature simulated distillation (HT-SimDis) potentiometric titration nuclear magnetic resonance (NMR) high performance liquid chromatography (HPLC)a RICOa XPSa XANESa high resolution mass spectrometry (high field FTICR MS or Orbitrap MS)a
obtained information N/A C, H, S, N, O, Ni, V elemental fraction saturates, aromatics, resins, asphaltenes weight fraction number average molecular weight molecular weight distribution molecular weight distribution boiling point distribution (
