(733a) Optimal Model-Based Production Planning for Refinery Operations

Historically,
the petroleum industry has used linear programming (LP) to address its planning
and optimization needs (Favennec, 2001; Li et al 2005).  The simplicity,
robustness and convenience of this approach are tradeoffs for the true optimal
and accurate solution to the planning model.  In fact, planning technology is
considered well developed and progress is expected in model refinement through
the use of nonlinear programming (NLP) (Pelham & Pharris, 1996), which
accommodates the use of nonlinear process models.  These improvements are more
pressing now with the changing market, increasing demands and limited refining
capacities.  This paper is an attempt to address this need with the goal to
develop more accurate refinery planning models, using the latest NLP algorithms
and implementing more accurate process modeling.  The objectives are to
establish the current status of planning models and propose nonlinear process
model equations for implementation into a refinery planning model.

Starting with the configuration
of a complex refinery, production planning model is designed to determine the
types, quantities and mixing strategies for the different crude oils available
for purchase, so that the refinery will meet the objective of maximizing
profits while satisfying specific demands over a specified time period.  Based
on the available information for feedstock, products slate, unit capacities and
conditions, the refinery planning model elements are the process units,
separators, mixers, product blending and feedstock.  The process units are
modeled as a linear function to calculate the yields based on the feed
streams.  This approach satisfies the LP modeling requirements.  The focus of
this paper is the front end of the refinery, namely the crude distillation unit
(CDU).

The CDU yield prediction is
modeled using linear functions of the crude feed.   However, it does not
reflect true refinery operations where there are different operating modes
(such as maximizing naphtha or light distillate, or to meet temporary
limitations on utilities or maintenance).  Each mode has its own set of coefficients
for yield prediction.  Therefore, the fixed-yield based model does not optimize
the yield calculations or allow blending the discrete operating modes for
optimum operation.  The swing cut approach addresses this problem by allowing
the exact cut or fraction to be optimized or ?fine tuned?.  After determining
the desired product cuts of the crude, about 5% to 7% of the yield around adjacent
fractions of the crude is allowed to change or ?swing?, changing the yields so
as to improve the objective function (Zhang, 2001, Trierwiler, & Tan,
2001).  The minimum modifications required for the swing cuts approach allow more
optimization opportunity and possible blending of different operating modes. 
Despite the improvement of the swing cut model, the model does not reflect the
nonlinearity of the process, but it provides an incentive to further improve
the planning model and calculate more accurate yields.

The CDU model can be upgraded
from the linear swing cut equation using an aggregate model approach based on
the work of Caballero & Grossmann (1999) for the synthesis of distillation
columns.  The principle of their approach is to treat the column sections above
and below the feed tray as two integrated heat and mass exchangers.  This
aggregate representation, which includes a modest number of nonlinear
equations, reflects the nonlinear nature of the process without the increasing
computation or complexity of conventional short cut models or rigorous
tray-by-tray model (Suphanit, 1999).  Furthermore, this approach can serve as a
precursor to more complex nonlinear models.

The aggregate model used applies
to a typical distillation column, with a condenser, a reboiler and only top and
bottom products.  However, the CDU is a complex distillation column with
multiple side streams, side strippers and condensers.  It also depends on steam
stripping and uses no bottom reboiler.  Therefore, the aggregate model cannot
be applied in its simple format directly to the CDU.  This limitation applies
also to the typical Fenske-Underwood-Gilliland (FUG) shortcut model (Suphanit,
1999).  To overcome this limitation, the CDU is represented as a set of simple
and thermodynamically equivalent cascaded distillation columns.   The cascaded
approach will be used for the aggregate model, as well as future nonlinear
modeling approaches.

A key to a successful
nonlinear programming model is good initialization.  It helps the model to find
a solution and converge.  The initialization schemes are based on the
understanding of the problem and foundation principles.  For the CDU aggregate
model, the adapted initialization scheme involves two stages, solving an LP
model followed by a simple NLP model.  The results of the second initialization
stage serve as the initial data for the aggregate model.  The two stage
initialization scheme allows the aggregate model to converge and give a
solution.  Generating this initialization scheme allowed for analysis of the
cascaded columns model and identifying additional constraints.  These
constraints are added to the aggregate model to further define the feasible
region of the problem.

The developed NLP-based
aggregate model for the CDU is integrated into a refinery production planning
model.  The results of the new NLP production planning model are compared with
the current LP models, namely fixed yield and swing cuts based models.  The
benefits of introducing the NLP model are assessed in terms of accuracy,
robustness and complexity.

[1] Caballero, J.A.;
Grossmann, I.E.  (1999).  Aggregate models for integrated distillation
systems.   Industrial & Engineering Chemistry Research, 38(6),
2330-2344.

[2] Favennec, JP. (2001).  Refinery
Operation & Management. Editions Technip. Paris.

[3] Li, W.; Hui, C.W.; Li,
A.X.  (2005). Integrated CDU, FCC and product blending models into refinery
planning.  Computers and Chemical Engineering, 29, 2010-2028.

[4] Pelham, R.; Pharris, C. (1996).
Refinery operation and control: a future vision.  Hydrocarbon Processing,
75(7), 89-94.

[5] Suphanit, B. (1999).  The
design of complex distillation systems, PhD Thesis.  UMIST. Manchester.

[6] Trierwiler, D.; Tan, R.L.
(2001) Advances in crude oil LP modeling. Hydrocarbon Asia, 8, 52-58

[7] Zhang, J.; Zhu, X.X.;
Towler, G.P. (2001). A simultaneous optimization strategy for overall
integration in refinery planning.  Industrial & Engineering Chemistry
Research, 40, 2640-2653.