Electronic Thesis and Dissertation Repository

Thesis Format

Monograph

Degree

Doctor of Philosophy

Program

Chemical and Biochemical Engineering

Supervisor

de Lasa, Hugo I.

2nd Supervisor

Escobedo, Salvador

Co-Supervisor

3rd Supervisor

Bhattacharya, Sujit

Affiliation

Syncrude Canada Ltd

Co-Supervisor

Abstract

Naphtha is used to dilute the froth from bitumen treatment. Naphtha is recovered using a Naphtha Recovery Unit (NRU) and sent back to the froth dilution step. To minimize the environmental and economic impact of the NRU, it is imperative to maximize the naphtha recovery. It is, in this respect, that enhanced NRU Vapour-Liquid-Liquid equilibrium data is a significant value. The prediction of phase equilibria for hydrocarbon/water blends in separators, is a subject of considerable importance for chemical processes. Despite its relevance, there are still pending questions. Among them, is the prediction of the correct number of phases. While a stability analysis using the Gibbs Free Energy of mixing and the NRTL model for n-octane/water, provide a good understanding of calculation issues when using HYSYS V9 and Aspen Plus V9 software, this shows that significant phase equilibrium uncertainties still exist. In the case of multicomponent mixtures, the Tangent Plane Distance (TPD) is evaluated as a possible criterion for calculating the number of phases. Additionally, Paraffinic Aromatic Synthetic Naphtha (PASN) with a similar True Boiling Point (TBP) as typical naphtha can be used. Runs were developed in a CREC VL Cell operated with n-octane/water and PASN/water mixtures under dynamic conditions and used to establish the two-phase (liquid-vapour) and three-phase (liquid-liquid-vapour) domains. Results obtained demonstrate that the complete solubility is larger than the predicted by simulation software or reported in the technical literature. Furthermore, and to provide an effective and accurate method for predicting the number of phases, a Classification Machine Learning (ML) technique was implemented. Finally, traditional flash split calculations are reported explaining the challenges presented for the solution of the Rachford-Rice equations. A comparison of flash calculations between water/n-octane and PASN/water mixtures using SRKKD EoS is provided. The value of an ML approach developed based on the abundant experimental data available from the CREC-VL experimental Cell experiments is presented.

Summary for Lay Audience

Canada has produced its Alberta oil sands for about 40 years and has become the world leader in oil sands production. The challenge is to reduce environmental impact and maximize project economics by optimizing each process step involved. One crucial process within bitumen production is the recovering of Naphtha. Naphtha is a chemical blend used as a solvent to facilitate the transportation of bitumen. Naphtha is recovered using a Naphtha Recovery Unit (NRU) and sent back to the dilution step. To minimize the environmental and economic impact of the NRU, it is imperative to maximize the naphtha recovery. To do this, it is essential to understand the interactions between water and hydrocarbon and establish Vapour-Liquid-Liquid equilibrium data is of considerable importance for chemical processes. However, there are still pending questions. Among them, is the prediction of the correct number of phases. While a stability analysis of simple mixtures such as n-octane/water, provide a good understanding of calculation issues, when using HYSYS V9 and Aspen Plus V9 software, this shows that significant phase equilibrium uncertainties still exist. To clarify these matters, n-octane and water blends, are good surrogates of naphtha/water mixtures. Additionally, Paraffinic Aromatic Synthetic Naphtha (PASN) similar to a typical naphtha, can be used. Runs were developed in a CREC VL Cell operated with n-octane/water and PASN/water mixtures under dynamic conditions and used to establish the two-phase (liquid-vapour) and three-phase (liquid-liquid-vapour) domains. Results obtained demonstrate, that the complete solubility in the liquid phase is larger than the one reported in the technical literature. Furthermore, and to provide an effective and accurate method for predicting the number of phases, a Classification Machine Learning (ML) technique was implemented. Finally, traditional flash split calculations are reported explaining the challenges presented for the solution. A comparison of between water/n-octane and PASN/water mixtures using an ecuation of state is provided. The value of an ML approach developed based on the experimental data available from the CREC-VL experimental Cell experiments is presented.

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