Phase behaviour prediction for ill-defined hydrocarbon mixtures

Phase behaviour information is essential for the development and optimization of hydrocarbon resource production, transport and refining technologies. Experimental data sets for mixtures containing heavy oil and bitumen are sparse as phase behaviour data are difficult to obtain and cost remains proh...

Full description

Bibliographic Details
Main Author: Saber, Nima
Other Authors: Shaw, John (Department of Chemical and Materials Engineering)
Format: Others
Language:en
Published: 2011
Subjects:
Online Access:http://hdl.handle.net/10048/1757
id ndltd-LACETR-oai-collectionscanada.gc.ca-AEU.10048-1757
record_format oai_dc
spelling ndltd-LACETR-oai-collectionscanada.gc.ca-AEU.10048-17572011-12-13T13:54:46ZShaw, John (Department of Chemical and Materials Engineering)Saber, Nima2011-01-26T22:00:21Z2011-01-26T22:00:21Z2011-01-26T22:00:21Zhttp://hdl.handle.net/10048/1757Phase behaviour information is essential for the development and optimization of hydrocarbon resource production, transport and refining technologies. Experimental data sets for mixtures containing heavy oil and bitumen are sparse as phase behaviour data are difficult to obtain and cost remains prohibitive for most applications. A computational tool that predicts phase behaviours reliably for mixtures containing such ill-defined components, over broad temperature, pressure and composition ranges would play a central role in the advancement of bitumen production and refining process knowledge and would have favourable impacts on the economics and environmental effects linked to the exploitation of such ill-defined hydrocarbon resources. Prior to this work, predictive computational methods were reliable for dilute mixtures of ill-defined constituents. To include a much wider range of conditions, three major challenges were addressed. The challenges include: creation of a robust and accurate numerical approach, implementation of a reliable thermodynamic model, and speciation of ill-defined constituents like Athabasca Bitumen Vacuum Residue (AVR). The first challenge was addressed by creating a novel computational approach based on a global minimization method for phase equilibrium calculations. The second challenge was tackled by proposing a thermodynamic model that combines the Peng-Robinson equation of state with group contribution and related parameter prediction methods. The speciation challenge was addressed by another research group at the University of Alberta. Pseudo components they proposed were used to assign groups and estimate thermodynamic properties. The new phase equilibrium computational tool was validated by comparing simulated phase diagrams with experimental data for mixtures containing AVR and n-alkanes. There is good qualitative and quantitative agreement between computed and experimental phase diagrams over industrially relevant ranges of compositions, pressures and temperatures. Mismatch was only observed over a limited range of compositions, temperatures and pressures. This computational breakthrough provides, for the first time, a platform for reliable phase behaviour computations with broad potential for application in the hydrocarbon resource sector. The specific computational results can be applied directly to solvent assisted recovery, paraffinic deasphalting, and distillation and refining processes for Athabasca bitumen a strategic resource for Canada.952306 bytesapplication/pdfenSaber, N., and Shaw, J. M. (2008) Fluid Phase Equilibria. 264 137-146Saber, N., and Shaw, J. M. (2009) Fluid Phase Equilibria. 285 73-82Saber, N. and Shaw, J. M. (2010) Fluid Phase Equilibria.doi:10.1016/j.fluid.2010.09.038phase behaviour predictionstability analysisflash calculationthermodynamic modelgroup contributionphase diagramheavy oilAthabasca bitumen vacuum residuephase equilibriumPhase behaviour prediction for ill-defined hydrocarbon mixturesThesisDoctor of PhilosophyDoctoralDepartment of Chemical and Materials EngineeringUniversity of Alberta2011-06Chemical EngineeringElliot, Janett (Department of Chemical and Materials Engineering)Yeung, Anthony (Department of Chemical and Materials Engineering)Babadagli, Tayfun (Department of Civil and Environmental Engineering)Chapman, Walter (Rice University)
collection NDLTD
language en
format Others
sources NDLTD
topic phase behaviour prediction
stability analysis
flash calculation
thermodynamic model
group contribution
phase diagram
heavy oil
Athabasca bitumen vacuum residue
phase equilibrium
spellingShingle phase behaviour prediction
stability analysis
flash calculation
thermodynamic model
group contribution
phase diagram
heavy oil
Athabasca bitumen vacuum residue
phase equilibrium
Saber, Nima
Phase behaviour prediction for ill-defined hydrocarbon mixtures
description Phase behaviour information is essential for the development and optimization of hydrocarbon resource production, transport and refining technologies. Experimental data sets for mixtures containing heavy oil and bitumen are sparse as phase behaviour data are difficult to obtain and cost remains prohibitive for most applications. A computational tool that predicts phase behaviours reliably for mixtures containing such ill-defined components, over broad temperature, pressure and composition ranges would play a central role in the advancement of bitumen production and refining process knowledge and would have favourable impacts on the economics and environmental effects linked to the exploitation of such ill-defined hydrocarbon resources. Prior to this work, predictive computational methods were reliable for dilute mixtures of ill-defined constituents. To include a much wider range of conditions, three major challenges were addressed. The challenges include: creation of a robust and accurate numerical approach, implementation of a reliable thermodynamic model, and speciation of ill-defined constituents like Athabasca Bitumen Vacuum Residue (AVR). The first challenge was addressed by creating a novel computational approach based on a global minimization method for phase equilibrium calculations. The second challenge was tackled by proposing a thermodynamic model that combines the Peng-Robinson equation of state with group contribution and related parameter prediction methods. The speciation challenge was addressed by another research group at the University of Alberta. Pseudo components they proposed were used to assign groups and estimate thermodynamic properties. The new phase equilibrium computational tool was validated by comparing simulated phase diagrams with experimental data for mixtures containing AVR and n-alkanes. There is good qualitative and quantitative agreement between computed and experimental phase diagrams over industrially relevant ranges of compositions, pressures and temperatures. Mismatch was only observed over a limited range of compositions, temperatures and pressures. This computational breakthrough provides, for the first time, a platform for reliable phase behaviour computations with broad potential for application in the hydrocarbon resource sector. The specific computational results can be applied directly to solvent assisted recovery, paraffinic deasphalting, and distillation and refining processes for Athabasca bitumen a strategic resource for Canada. === Chemical Engineering
author2 Shaw, John (Department of Chemical and Materials Engineering)
author_facet Shaw, John (Department of Chemical and Materials Engineering)
Saber, Nima
author Saber, Nima
author_sort Saber, Nima
title Phase behaviour prediction for ill-defined hydrocarbon mixtures
title_short Phase behaviour prediction for ill-defined hydrocarbon mixtures
title_full Phase behaviour prediction for ill-defined hydrocarbon mixtures
title_fullStr Phase behaviour prediction for ill-defined hydrocarbon mixtures
title_full_unstemmed Phase behaviour prediction for ill-defined hydrocarbon mixtures
title_sort phase behaviour prediction for ill-defined hydrocarbon mixtures
publishDate 2011
url http://hdl.handle.net/10048/1757
work_keys_str_mv AT sabernima phasebehaviourpredictionforilldefinedhydrocarbonmixtures
_version_ 1716389193518678016