Large scale nonlinear programming (NLP) has proven to be an effective framework
for obtaining profit gains through optimal process design and operations in
chemical engineering. While the classical SQP and Interior Point methods have been
successfully applied to solve many optimization problems, the focus of both academia
and industry on larger and more complicated problems requires further development
of numerical algorithms which can provide improved computational efficiency.
The primary purpose of this dissertation is to develop effective problem formulations
and an advanced numerical algorithms for efficient solution of these challenging
problems. As problem sizes increase, there is a need for tailored algorithms that
can exploit problem specific structure. Furthermore, computer chip manufacturers
are no longer focusing on increased clock-speeds, but rather on hyperthreading and
multi-core architectures. Therefore, to see continued performance improvement, we
must focus on algorithms that can exploit emerging parallel computing architectures.
In this dissertation, we develop an advanced parallel solution strategy for nonlinear
programming problems with block-angular structure. The effectiveness of this and
modern off-the-shelf tools are demonstrated on a wide range of problem classes.
Here, we treat optimal design, optimal operation, dynamic optimization, and
parameter estimation. Two case studies (air separation units and heat-integrated columns) are investigated to deal with design under uncertainty with rigorous models.
For optimal operation, this dissertation takes cryogenic air separation units as
a primary case study and focuses on formulations for handling uncertain product
demands, contractual constraints on customer satisfaction levels, and variable power
pricing. Multiperiod formulations provide operating plans that consider inventory to
meet customer demands and improve profits.
In the area of dynamic optimization, optimal reference trajectories are determined
for load changes in an air separation process. A multiscenario programming
formulation is again used, this time with large-scale discretized dynamic models.
Finally, to emphasize a different decomposition approach, we address a problem
with significant spatial complexity. Unknown water demands within a large scale
city-wide distribution network are estimated. This problem provides a different decomposition
mechanism than the multiscenario or multiperiod problems; nevertheless,
our parallel approach provides effective speedup.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2011-05-9093 |
| Date | 2011 May 1900 |
| Creators | Zhu, Yu |
| Contributors | Laird, Carl |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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