Energy Management and Environmental Sustainability of the Canadian Oil Sands Industry

By 2030 the worldwide energy demand is expected to increase by twofold, in which fossil fuels inevitably will still play a major role in this transition. Canadian oil sands, the second largest proven oil reserves, represent a major pillar in providing energy and economic security in North America. Their development on a large scale is hindered due to associated environmental impacts, which include greenhouse gas emissions, water usage, and management of by-products of downstream operations (e.g. Sulfur, petroleum coke, etc.). In this work optimization techniques are employed to address the management of various environmental issues while minimizing the cost of operations of the oil sands industry. In this context, this thesis makes four principal contributions. First, an extensive review is conducted on potential production pathways of renewable energy that can be integrated in the energy infrastructure of oil sands. Renewable technologies such as wind, geothermal, hydro, bioenergy, and solar are considered the most environmentally benign options for energy production that would contribute in achieving significant carbon emissions reductions. A mixed integer non-linear optimization model is developed to simultaneously optimize the capacity expansion and new investment decisions of both conventional and renewable energy technologies, and determine the optimal configurations of oil producers. The rolling horizon approach is used for the consecutive planning of multiple operational periods. To illustrate the applicability of the model, it was applied to a case study based on operational data for oil sands operators in Alberta for the period of 2010 – 2025. Second, a generalized optimization model was developed for the energy planning of energy intensive industries. An extensive superstructure was developed that incorporates conventional, renewable, nuclear, and gasification of alternative fuels (e.g. petroleum coke, asphaltenes, etc.) technologies for the production of energy in the form of power, heat and hydrogen. Various carbon mitigation measures were incorporated, including carbon capture and sequestration, and purchase of carbon credits to satisfy emission targets. Finally, the superstructure incorporated the possibility of selling excess energy commodities in competitive markets. The superstructure is represented by a multi-period mixed integer optimization model with the objective of identifying the optimal set of energy supply technologies to satisfy a set of demands and emission targets at the minimum cost. Time-dependent parameters are incorporated in the model formulation, including energy demands, fuel prices, emission targets, carbon tax, construction lead time, etc. The model is applied to a case study based on the oil sands operations over the planning period 2015–2050. A scenario based approach is used to investigate the effect of variability in energy demand levels, various carbon mitigation policies, and variability in fuel and energy commodity prices. Third, a multi-objective and multi-period mixed integer linear programming model is developed for the integrated planning and scheduling of the energy infrastructure of the oil sands industry incorporating intermittent renewable energy. The contributions of various energy sources including conventional, renewable, and nuclear are investigated using a scenario based approach. Power-to-gas for energy storage is incorporated to manage surplus power generated from intermittent renewable energy sources, particularly wind. The wind-electrolysis system incorporates two hydrogen recovery pathways, which are power-to-gas and power-to-gas-to-power using natural gas generators. The model takes into account interactions with the local Alberta grid by incorporating unit commitment constraints for the grid’s existing power generation units. Three objective functions are considered, which are the total system cost, grid operating cost and total emissions. The epsilon constraint method is used to solve the multi-objective aspect of the proposed model. Fourth, extensive research has been done on the components that constitute the sulfur supply chain, including sulfur recovery, storage, forming, and distribution. These components are integrated within a single framework to assist in the design optimization of sulfur supply chains. This represents a starting point in understanding the trade-offs involved in the sulfur supply chain from an optimization point of view. Optimization and mathematical modeling techniques were implemented to generate a decision support system that will provide an indication of the optimal design and configuration of sulfur supply chains. The resulting single-period mixed-integer linear programming model was aimed at minimizing total capital and operating costs. The model was illustrated through a case study based on Alberta’s Industrial Heartland. A deterministic approach in an uncertain environment was implemented to investigate the effect of supply and demand variability on the design of the supply chain. This was applied to two scenarios, which are steady state operation and sulfur surplus accumulation. The model identified the locations of forming facilities, the forming, storage and transportation technologies, and their capacities. The contributions of this thesis are intended to support effective carbon mitigation policy making and to address the environmental sustainability of the oil sands industry

Design and simulation of a petcoke gasification polygeneration plant integrated with a bitumen extraction and upgrading facility and net energy analysis

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.energy.2017.09.072 © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The in-situ extraction of bitumen from oil sands, particularly steam assisted gravity drainage, has been the fastest growing production technology in the industry. Integrated with upgrading operations to enhance the fuel quality, the process consumes significant amounts of energy, which are currently mostly derived from burning natural gas. On the other hand, considerable amounts of petroleum coke residues are generated in the refineries. This petcoke ends up stockpiled as a waste byproduct with associated environmental concerns. The aim of this study is to evaluate the feasibility of integrating a petroleum coke residue gasification plant to the energy infrastructure of an integrated SAGD/upgrading facility. The petcoke gasification process is specifically designed to fulfill the demands of of a facility processing 112,500 barrels per day of Athabasca bitumen. Two plant configurations are compared, one without and one with CO2 capture and storage. The gasification-based polygeneration plant is modeled with the Aspen Plus flowsheeting software. Two levels of energy demands (i.e. high and low energy scenarios), reflecting the range of variability in the energy requirements of extraction and upgrading operations (e.g. steam to oil ratio), are considered. The net efficiency for polygeneration plant was determined to be in the range of 48 – 58%. The gasification of approximately 190 t/h of petroleum coke is required to achieve the power, thermal and hydrogen demands. The incorporation of carbon capture imposes significant energy penalties, which requires the addition of natural gas fueled gas turbines to meet the power requirements

Multi-products productions from Malaysian oil palm empty fruit bunch (EFB): Analyzing economic potentials from the optimal biomass supply chain

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.jclepro.2017.08.088 © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The economic potentials of Malaysian oil palm empty fruit bunch are realized by several motivating factors such as abundance, cheapness and are generally feasible to produce multi-products that range from energy, chemicals and materials. Amid continuing supports from the government in terms of policies, strategies and funding, manufacturing planning and decision to utilize this biomass resource requires a decision-support tool. In this regard, biomass supply chain modeling serves as the supportive tool and can provide economic indications for guided future investments. Sequential steps in modeling and optimization of the supply chain that utilized empty fruit bunch were shown. In a form of superstructure, the supply chain consisted processing stages for converting the biomass into intermediates and products, transportation networks that used truck, train or pipeline, and the options for product's direct sales or for further refinements. The developed optimization model has considered biomass cost, production costs, transportation costs, and emission treatment costs from transportation and production activities in order to determine the annual profit. By taking a case study of Peninsula Malaysia, optimal value showed a profit of $ 713,642,269/y could be achieved which has assumed a single ownership for all of the facilities in the supply chain. Besides, the tabulated values of yields and emission levels could provide comparative analysis between the processing routes. Sensitivity analysis was then performed to perturb the approximated parameters or data that have been used in this study.Ministry of Higher Education of MalaysiaUniversiti Malaysia Pahang (UMP)Natural Sciences and Engineering Research Council of Canada (NSERC

Multiobjective Integrated Planning and Scheduling of the Energy Infrastructure of the Oil Sands Industry Incorporating Intermittent Renewable Energy

The energy infrastructure for oil sands operations can be classified as a decentralized energy system, in which energy requirements (i.e., power, heat and hydrogen) are generated near the end-users, and can operate with interactions with the local Alberta grid, in which it feeds surplus power generated to it. In this study, a mathematical optimization model is developed for the integrated planning and scheduling of the energy infrastructure of the oil sands industry. The contributions of various energy sources including conventional, renewables, and nuclear are investigated. Power-to-gas for energy storage is incorporated to manage surplus power generated from intermittent renewable energy sources, particularly wind. The wind-electrolysis system included incorporates two hydrogen recovery pathways, which are power-to-gas and power-to-gas-to-power using natural gas generators. The problem is modeled as a multiobjective and multiperiod mixed integer linear programming model that minimizes the system cost (energy production and storage), grid cost, and total greenhouse gas emissions. In addition to including the grid cost and emissions in the objective function, grid-interaction is incorporated in the optimization model through the unit commitment operations of the existing power generation units of the grid. The proposed model is designed to evaluate the optimal operation and sizing of the energy producers and the energy storage system as well as the interactions between them. The epsilon constraint method is used to solve the multiobjective aspect of the proposed model. To illustrate its applicability, the model is applied to a case study based on the oil sands industry in Alberta for the integrated planning and scheduling of its energy infrastructure for the year 2017

Energy infrastructure modeling for the oil sands industry: Current situation

none6siIn this study, the total energy requirements associated with the production of bitumen from oil sands and its upgrading to synthetic crude oil (SCO) are modeled and quantified. The production scheme considered is based on the commercially applied steam assisted gravity drainage (SAGD) for bitumen extraction and delayed coking for bitumen upgrading. In addition, the model quantifies the greenhouse gas (GHG) emissions associated with the production of energy required for these operations from technologies utilized in the currently existing oil sands energy infrastructure. The model is based on fundamental engineering principles, and Aspen HYSYS and Aspen Plus simulations. The energy demand results are expressed in terms of heat, power, hydrogen, and process fuel consumption rates for SAGD extraction and bitumen upgrading. Based on the model's output, a range of overall energy and emission intensity factors are estimated for a bitumen production rate of 112,500 BPD (or 93,272 BPD of SCO), which were determined to be 262.5–368.5 MJ/GJSCO and 14.17–19.84 gCO2/MJSCO, respectively. The results of the model indicate that the majority of GHG emissions are generated during SAGD extraction (up to 60% of total emissions) due to the combustion of natural gas for steam production, and the steam-to-oil ratio is a major parameter affecting total GHG emissions. The developed model can be utilized as a tool to predict the energy demand requirements for integrated SAGD/upgrading projects under different operating conditions, and provides guidance on the feasibility of lowering GHG emissions associated with their operation.Lazzaroni, Edoardo Filippo; Elsholkami, Mohamed; Arbiv, Itai; Martelli, Emanuele; Elkamel, Ali; Fowler, MichaelLazzaroni, Edoardo Filippo; Elsholkami, Mohamed; Arbiv, Itai; Martelli, Emanuele; Elkamel, Ali; Fowler, Michae

Optimization of biofuel production from corn stover under supply uncertainty in Ontario

In this paper, a biofuel production supply chain optimization framework is developed that can supply the fuel demand for 10% of Ontario. Different biomass conversion technologies are considered, such as pyrolysis and gasification and subsequent hydro processing and the Fischer-Tropsch process. A supply chain network approach is used for the modeling, which enables the optimization of both the biorefinery locations and the associated transportation networks. Gasification of corn stover is examined to convert waste biomass into valuable fuel. Biomass-derived fuel has several advantages over traditional fuels including substantial greenhouse gas reduction, generating higher quality synthetic fuels, providing a use for biomass waste, and potential for use without much change to existing infrastructure. The objective of this work is to show the feasibility of the use of corn stover as a biomass feedstock to a hydrocarbon biofuel supply chain in Ontario using a mixed-integer linear programming model while accounting for the uncertainty in the availability of corn stover. In the case study, the exact number of biorefineries is left as a policy decision and the optimization is carried out over a range of the possible numbers of facilities. The results obtained from the case study suggests implementing gasification technology followed by Fischer-Tropsch at two different sites in Ontario. The optimal solution satisfied 10% of the yearly fuel demand of Ontario with two production plants (14.8 billion L of fuel) and requires an investment of $42.9 billion, with a payback period of about 3 years

Optimal Design of Petroleum Refinery Configuration Using a Model-Based Mixed-Integer Programming Approach with Practical Approximation

We present a model-based optimization approach to determine the configuration of a petroleum refinery for grassroots (new) or existing site that considers a large number of commercial technologies particularly for heavy oil processing of crude oil residue from an atmospheric distillation unit. First, we develop a superstructure representation for the refinery configuration to encompass all possible topology alternatives comprising 96 technologies and their interconnectivities. The superstructure is postulated by decomposing it to incorporate representative heavy oil processing scheme alternatives that center on the technologies for atmospheric residual hydrodesulfurization (ARDS), vacuum residual hydrodesulfurization (VRDS), and residual fluid catalytic cracking (RFCC). We formulate a mixed-integer linear program (MILP) based on the superstructure by devising logic propositions on design and structural specifications that represent these processing options to aid convergence to an optimal refinery configuration. A numerical example is illustrated to implement the proposed technique in which an equivalent of more than two million refinery plot plans is evaluated. To assess the applicability and value of the approach, we validate the results against the literature as well as compare with existing real-world refinery configurations. A main contribution of this work is to demonstrate how a mixed-integer programming approach can be applied to a large-scale petroleum refinery design problem with suitable approximations informed by practical considerations to obtain results with reasonable computational load