Master of Engineering Science
Chemical and Biochemical Engineering
de Lasa, Hugo
Syncrude Canada Ltd
The availability of vapor pressure data is essential to validate thermodynamic models and enhance the thermodynamic correlations. Despite its importance, there is limited vapor pressure data of the multicomponent mixtures in open literature. This is the case for hydrocarbon/water blends as they are found in the naphtha recovery unit in the oil sand process.
This thesis uses a CREC-VL-Cell, a batch apparatus to measure the vapor pressures of n-octane/water, synthetic naphtha (SN)/water and solids/n-octane/water. The CREC-VL-Cell operates at thermal equilibrium with less than 1.6 % error using a 1080 rpm impeller speed and various optimized operational factors. This apparatus saves at least 8 hours of the degassing procedures using an air contained correction.
Aspen Hysys process simulator with the Peng Robinson Equation of State package is valuable to emulate CREC-VL-Cell dynamic data of the air-contained hydrocarbon/water by adjusting the volumetric flow of all the phases exiting a continuous separator unit. On this basis, vapor pressure data from the CREC-VL-Cell and Aspen Hysys-Peng Robinson Equation of State simulations are shown to compare well for both n-octane/water and synthetic naphtha (SN)/water blends.
On the other hand, mass balances derived CREC-VL-Cell data allows one to establish liquid and vapor molar fractions boundaries for n-octane/water blends. With these boundaries, additional discrimination of thermodynamic models is allowed. For instance, this shows significant discrepancies of the derived Aspen Hysys-Peng Robinson Equation of State molar fractions, with the anticipated molar fractions boundaries calculated via mass balances in the CREC-VL-Cell.
Summary for Lay Audience
Even though of Canada’s Oil industry has a significant role as the world’s third-largest oil reservoir, the oil sand industry is facing challenges to minimize process contaminated water effluents. Naphtha Recovery Unit (NRU) is the last process step to recover residual naphtha which is used to dilute bitumen. Therefore, the NRU process must be optimized to increase the economic benefits and minimize the environmental footprint.
Vapor pressure data is crucial to investigate the extent of phase equilibrium in the NRU. Moreover, thermodynamic models and their enhanced correlations can be validated by using experimentally measured vapor pressures. However, restricted vapor pressure data of a multicomponent system limits the optimization of the NRU with feed streams composed of water, solids, bitumen and naphtha.
This MESc thesis considers an apparatus able to measure vapor pressures of complex mixtures. This apparatus designated as the CREC-VL-Cell, provides a batch dynamic measurements of vapor pressure in the 30 ℃ to 120 ℃ range. This method includes optimized design parameters such as mixing speed and unit shape factors.
N-octane/water, synthetic naphtha/water and solids/n-octane/water mixtures are used to measure the vapor pressure and the analysis of this data is reported in this thesis. Also, n-octane/water mixtures are further investigated to establish n-octane molar fractions limits.
This MESc thesis also uses a process simulator, Aspen Hysys with a Peng-Robinson Equation of State package, to investigate the behavior of complex mixtures. The continuous process model is modified to describe the experimental condition in the CREC-VL-Cell. The simulation results are compared to the experimental data validating in this way, the simulation results obtained.
As a result, this MESc project is developed to facilitate vapor pressure measurements, proposing a simulation method which validated with experimental data. This experimental study can also contribute to help setting a valuable research methodology for other hydrocarbon-water process separation applications.
Kong, Jeonghoon, "Multiphase Equilibrium in A Novel Batch Dynamic VL-Cell Unit with High Mixing: Apparatus Design and Process Simulation" (2020). Electronic Thesis and Dissertation Repository. 7283.
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