(745e) Bitumen Upgrading and Synthetic Crude Oil Blending Optimization

Bitumen upgrading is an important procedure of oil sands production. Its main objective is to upgrade the extra heavy bitumen into light synthetic crude oil (SCO) that can be processed in refineries. Bitumen can also be blended with SCO or condensate to reduce the viscosity for pipeline transportation. The SCO and bitumen products have to satisfy certain specifications (e.g., Sulphur concentration, density, and viscosity) so as to be processed in downstream refineries. Simultaneously producing various SCO and bitumen products and taking full advantages of available resources to increase the economic benefits are the main motivations for the bitumen upgrading and synthetic crude oil blending optimization problem.

There are a number of publications related to the blending optimization problem in literature. To name a few, the gasoline blending and distribution scheduling problem was studied in [1];  the work of [2] addressed the simultaneous optimization of the off-line blending and the short-term scheduling problem in oil-refinery applications; the scheduling of crude oil operations under demand uncertainty is studied in [3]; in the work of [4], the crude oil scheduling problem was modeled as a control problem and the crude oil blending and inventory management was tackled using the receding horizon strategy. In general, mathematical modeling of the blending problem lead to challenging nonlinear optimization problem. Therefore, creative methods are needed to circumvent the difficulties for the optimization.

Although much work has been done in the area of blending optimization, they are mainly limited to the crude oil or product blending in the conventional refineries. This paper presents a novel method which addresses the optimization of blending in oil sands upgrading plant. While process unit model (e.g., Distillation unit, Coker, Hydrotreater) and blending relations for various properties [5] are the major parts of the mathematical model, the objective function is based on profits calculated from product sales minus raw bitumen cost, hydrogen costs and unit operation cost. Presence of nonconvex terms (i.e., bilinear and trilinear terms) causes both local and global optimization solvers to fail. To address this issue, a multistep optimization strategy is proposed to tackle the optimization problem. First, a set of mass ratio before each blender can be found through a small nonlinear optimization problem in which product’s qualities play the major role. By knowing the optimal mass ratio of each blender which can provide desired product specifications, blending rules will be redundant hereafter and the problem will be reduced into a linear optimization problem. The simplified case is easy to solve and the optimality of its results can be guaranteed. In the next step, the obtained results are applied as an initial guess of the original nonlinear nonconvex blending optimization problem to improve the current optimal point using either local or and global optimization solver in GAMS. The method is illustrated through a case study of bitumen upgrading process with Coker unit and the required data is adapted from simulation studies of bitumen upgrading process [6].

References

[1] Jia, Z., M. Ierapetritou. Mixed-integer linear programming model for gasoline blending and distribution scheduling. Industrial & Engineering Chemistry Research 42 (2003): 825-835.

[2] Méndez, C. A., Grossmann, I. E., Harjunkoski, I., & Kaboré, P. A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations. Computers & chemical engineering, 30(2006), 614-634.

[3] J. Li, R. Misener, and C. A. Floudas. Scheduling of Crude Oil Operations Under Demand Uncertainty: A Robust Optimization Framework Coupled with Global Optimization. AIChE Journal, 58 (2012) 2373–2396.

[4] A. A. Hamisu, Y. Cao, and A. Kokossis, Scheduling Crude Oil Blending and Tanks Inventory Control under CDU Demand Uncertainty: A Receding Horizon Approach. Proceedings of the 20th International Conference on Automation & Computing, Cranfield University, Bedfordshire, UK, 12-13 September 2014.

[5] M. A. Fahim, T. A. Al-Sahhaf, and A. Elkilani. Fundamentals of Petroleum Refining. Elsevier Science & Tech, 2009.

[6] A. Alvarez-Majmutov and J. Chen. Simulation of bitumen upgrading processes. Digital Refining, 2 PTQ Q2 2013.