(88d) Parametric Sensitivity Analysis for the Optimal Design of a Bitumen Upgrading Plant Using a Steady-State Model

The exploitation of non-conventional oil resources have become a fundamental part of the Canadian economy during the last decade. The province of Alberta holds the majority of non-conventional oil deposits in the country. These resources originates from Oil Sands or Tart Sands deposits; which are technically defined as extra-heavy crude bitumen. The heavy crude oil must undergo an energy-intensive upgrading process in order to be transformed into commercial synthetic crude oil (SCO) blends making use of prime commodities such as steam, electricity, process heat, and hydrogen. These energy demands are essentially met using natural gas as primary energy source given its abundance and relatively low-cost in Alberta. This work presents a parametric sensitivity analysis for identifying the impact level of natural gas prices over the bitumen upgrading process using a steady-state model. The fluctuations of the natural gas prices may cause changes in: (1) the design of the optimal upgrading plant, (2) energy demands, (3) SCO product specifications, and (4) greenhouse gases emissions level. The proposed steady-state optimization model considers five basic stages in the upgrading process: primary distillation (atmospheric distillation/diluent recovery), vacuum distillation, cracking (LC-fining/delayed coking), hydrotreating, and blending. Each upgrading stage includes different processing units with a distinctive set of operating modes. These operating modes are defined in terms of specific product yields and energy requirements based on reported industrial data. The present model considers an economic objective function based on the operating energy costs of the upgrading plant. The optimization approach seeks for the optimal bitumen upgrading configuration by selecting the most suitable upgrading steps based on their corresponding unit’s operating modes. This is done to obtain a particular type of SCO blend according to composition specifications at minimum cost. The problem was modelled as a mixed-integer nonlinear program (MINLP) using the GAMS modeling system. The results show that the proposed model is a practical tool to: 1) select the most suitable bitumen upgrading configuration, 2) estimate the upgrading energy costs, 3) identify the most suitable utility supply source based on techno-economics and environmental parameters, 4) and find means to mitigate CO₂ emissions.