| dc.description.abstract | The pooling problem is an important optimization problem that is encountered in
operation and scheduling of important industrial processes within petroleum refineries.
The key objective of pooling is to mix various intermediate products to achieve desired
properties and quantities of products. First, intermediate streams from various processing
units are mixed and stored in intermediate tanks referred to as pools. The stored streams
in pools are subsequently allowed to mix to meet varying market demands. While these
pools enhance the operational flexibility of the process, they complicate the decisionmaking
process needed for optimization. The problem to find the least costly mixing
recipe from intermediate streams to pools and then from pools to sale products is
referred to as the pooling problem. The research objective is to contribute an approach to
solve this problem.
The pooling problem can be formulated as an optimization program whose objective is
to minimize cost or maximize profit while determining the optimal allocation of
intermediate streams to pools and the blending of pools to final products. Because of the
presence of bilinear terms, the resulting formulation is nonconvex which makes it very
difficult to attain the global solution. Consequently, there is a need to develop
computationally-efficient and easy-to-implement global-optimization techniques to solve
the pooling problem. In this work, a new approach is introduced for the global
optimization of pooling problems. The approach is based on three concepts: linearization
by discretizing nonlinear variables, pre-processing using implicit enumeration of the
discretization to form a convex-hull which limits the size of the search space, and
application of integer cuts to ensure compatibility between the original problem and the discretized formulation. The continuous quality variables contributing to bilinear terms
are first discretized. The discretized problem is a mixed integer linear program (MILP)
and can be globally solved in a computationally effective manner using branch and
bound method. The merits of the proposed approach are illustrated by solving test case
studies from literature and comparison with published results. | en |
