Optimal Scheduling of Refinery Crude-Oil Operations.pdf (1.68 MB)
Optimal Scheduling of Refinery Crude-Oil Operations
thesis
posted on 2010-12-01, 00:00 authored by Sylvain MouretThis thesis deals with the development of mathematical models and algorithms for optimizing
refinery crude-oil operations schedules. The problem can be posed as a mixed-integer
nonlinear program (MINLP), thus combining two major challenges of operations research:
combinatorial search and global optimization.
First, we propose a unied modeling approach for scheduling problems that aims at
bridging the gaps between four different time representations using the general concept of
priority-slots. For each time representation, an MILP formulation is derived and strengthened
using the maximal cliques and bicliques of the non-overlapping graph. Additionally,
we present three solution methods to obtain global optimal or near-optimal solutions. The
scheduling approach is applied to single-stage and multi-stage batch scheduling problems
as well as a crude-oil operations scheduling problem maximizing the gross margin of the
distilled crude-oils.
In order to solve the crude-oil scheduling MINLP, we introduce a two-step MILP-NLP
procedure. The solution approach benefits from a very tight upper bound provided by the
first stage MILP while the second stage NLP is used to obtain a feasible solution.
Next, we detail the application of the single-operation sequencing time representation
to the crude-oil operations scheduling problem. As this time representation displays many
symmetric solutions, we introduce a symmetry-breaking sequencing rule expressed as a
deterministic finite automaton in order to efficiently restrict the set of feasible solutions.
Furthermore, we propose to integrate constraint programming (CP) techniques to the
branch & cut search to dynamically improve the linear relaxation of a crude-oil operations
scheduling problem minimizing the total logistics costs expressed as a bilinear objective.
CP is used to derived tight McCormick convex envelopes for each node subproblem thus
reducing the optimality gap for the MINLP.
Finally, the refinery planning and crude-oil scheduling problems are simultaneously solved
using a Lagrangian decomposition procedure based on dualizing the constraint linking crude
distillation feedstocks in each subproblem. A new hybrid dual problem is proposed to update
the Lagrange multipliers, while a simple heuristic strategy is presented in order to obtain
feasible solutions to the full-space MINLP. The approach is successfully applied to a small
case study and a larger refinery problem.
History
Date
2010-12-01Degree Type
- Dissertation
Department
- Chemical Engineering
Degree Name
- Doctor of Philosophy (PhD)