(755g) Effective Global Optimization Methods for Total Refinery Planning Operations

In the last
twenty years, the petroleum industry, which is the largest sources of energy
products in the world, has succeeded by creating markets and supplying them
with suitable products. Tighter competition, strict environmental regulations,
and lower-margin profits, drive the petroleum refinery to apply new
technologies to improve their planning operations.

The
entire refinery operations can be divided into three sections including crude
oil unloading and blending, production unit operations, and the product
blending and distribution[1-2]. The refinery planning addresses the entire refinery
operations involving crude purchase, processing amounts for production units,
their production mode, flow connections between production units, and pooling
and blending operations to satisfy quality requirements of production units,
intermediates and final products. Mathematical modeling of production units, pooling
and blending operations introduces bilinear, trilinear terms, and higher order
terms, which transform the entire problem into non-convex nonlinear
optimization problem.

The refinery
planning problem has received considerable attention since the introduction of
linear programming in 1950s.  They focused on developing different models and
algorithms to solve large-scale industrial problems. Some commercial software
such as RPMS (Refinery and Petrochemical Modeling System),[3] PIMS (Process
Industry Modeling System),[4] and GRTMPS (Haverly Systems)[5] have been
developed. However, inaccuracy caused by nonrigorous linear models and approximate
algorithm may reduce the overall profitability or sacrifice product quality. Moreover,
no global optimality is guaranteed. Pinto and Moro[6] developed a nonlinear
planning model for production planning which allows the implementation of
nonlinear process models as well as blending relations. Li et al.[7] presented
a refinery planning model that utilizes simplified empirical nonlinear process
models with considerations for crude characteristics, product yields, and
qualities, etc. Alhajri et al.[8] developed a nonlinear model to address
refinery planning problem. Alattas et al.[9] developed a fractionation index
based nonlinear model for crude distillation unit and integrated it into the
linear refinery planning model. They solved their NLP models with NLP solvers
without guaranteeing global optimality. The review on refinery planning can be
found in Shah et al.[10] It can be concluded that there is no existing global
optimization method to address the total refinery planning problems.

In this presentation,
we first present the entire nonlinear optimization model for the total refinery
planning operations. The model involves several bilinear and trilinear terms
arising from the intermediate pooling and product blending operations. Then, we
propose an optimization-based procedure to obtain the tightest lower and upper
bounds for variables especially the variables involving in bilinear and
trilinear terms. Then, we incorporate those tightest lower and upper bounds
into commercial solver GloMIQO[11-12] to obtain e-global
optimality. A large-scale industrial case study is solved to illustrate the
efficiency of our developed global optimization approach. The computational
results show that our developed optimization-based procedure greatly improves
the lower and upper bounds of all variables especially those variables existing
in the bilinear and trilinear terms without much computational effort. Furthermore,
we can obtain 8%-global optimal solution for this example, which is 2.5% to
3.8% improvement compared to those from PIMS.

References

[1] Pinto, J.
M.; Joly, M.; Moro, L. F. L. Planning and Scheduling Models for Refinery
Operations. Computers and Chemical Engineering 2000, 24, 2259-2276.

[2] Shah, N. K.;
Ierapetritou, M. G. Short-Term Scheduling of a Large-Scale Oil-Refinery Operaitons:
Incorporating Logistics Details. AIChE Journal 2011, 57, 1570-1584.

[3] RPMS
(Refinery and Petrochemical Modeling Systems): A System Description; Bonner and
Moore: Houston, TX, 1979.

[4] Aspen PIMS
System Reference (v7.2), Aspen Technology Inc.; Burlington, MA, 2010.

[5] GRTMPS
(Haverly Systems), http://www.haverly.com/main-products/13-products/9-grtmps.

[6] Pinto, J.
M.; Moro, L. F. L. A Planning Model for Petroleum Refineries. Braz. J. Chem.
Eng. 2000, 17, 575-585.

[7] Li, W. K.;
Hui, C. W.; Li, A. X. Integrating CDU, FCC, and Product Blending Models into
Refinery Planning. Computers and Chemical Engineering 2005, 29,
2010-2028.

[8] Alhajri, I.;
Elkamel, A.; Albahri, T.; Douglas, P. L. A Nonlinear Programming Model for
Refinery Planning and Optimization with Rigorous Process Models and Product
Quality Specifications. International Journal of Oil, Gas, and Coal Technology
2008, 1, 283-307.

[9] Alattas, A.
M.; Grossmann, I. E.; Palou-Rivera, I. Integration of Nonlinear Crude
Distillation Unit Models in Refinery Planning Optimization. Industrial and
Engineering Chemistry Research 2011, 50, 6860-6870.

[10] Shah, N.
K.; Li, Zu; Ierapetritou, M. G. Petroleum Refining Operations: Key Issues,
Advances, and Opportunities, Industrial and Engineering Chemistry Research,
2011, 50, 1161-1170.

[11] Misener, R.;
Floudas, C. A. Global Optimization of Mixed-Integer Quadratically-Constrained
Quadratic Programs (MIQCQP) through Piecewise-Linear and Edge-Concave
Relaxations. Mathematical Programming  Series B. 2012, 136, 155-182.

[12] Misener,
R.; Floudas, C. A. GloMIQO: Global Mixed-Integer Quadratic Optimizer. Journal
of Global Optimization, 2012, In press. DOI: 10.1007/s10898-012-9874-7.