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Permanent link (DOI): https://doi.org/10.7939/R37659S20

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Economic optimization of steam plant operation Open Access

Descriptions

Other title
Subject/Keyword
Hidden Markov model
Electricity price prediction
Steam plant optimization
Gaussian mixture
Distribution metric
EM algorithm
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Liu, Tianbo
Supervisor and department
Forbes, J.Fraser (Chemical and Materials Engineering)
Huang, Biao (Chemical and Materials Engineering)
Examining committee member and department
Huang, Biao (Chemical and Materials Engineering)
Liu, Jinfeng (Chemical and Materials Engineering)
Forbes, J.Fraser (Chemical and Materials Engineering)
Department
Department of Chemical and Materials Engineering
Specialization
Process control
Date accepted
2013-09-28T16:08:45Z
Graduation date
2013-11
Degree
Master of Science
Degree level
Master's
Abstract
The utility system plays an important role in efficient plant operations of chemical processes. In this thesis, economic optimization of steam utility system is investigated in detail. The objective is: 1) to calculate the optimal generation amount of steam and electricity under uncertainty in process and electricity market; 2) to distribute the generated steam in a most efficient way throughout the steam network. In this work, steam distribution system is represented as a network with dynamic process equipment models. Operating constraints and uncertain process disturbances are included to accurately represent plant operations. A cost-benefit analysis reveals that electricity price plays an important role in optimal plant operations. Thus, to maximize the economic profit of a steam plant in the long term, a high quality electricity price prediction model is developed based on a robust switched system identification algorithm. The algorithm is formulated using Expectation-Maximization (EM) algorithm to estimate parameters in prediction model, noise distribution and switching dynamics. Dynamic process models and electricity price prediction models are integrated into a linear programming problem that uses plant profit as the performance objective. Random process variables are included to represent process uncertainty. The optimization effect is evaluated by comparing the plant profit from routine operations and from optimized operations. The distribution of optimized plant profit is obtained by solving the distribution problem of stochastic linear programming (SLP). A metric based on Earth Mover's Distance (EMD) is introduced to measure the difference between plant profit distributions. Based on the validation results of developed models and proposed performance evaluation method, the optimized steam plant operations show significant advantage over the routine ones when electricity prices vary considerably.
Language
English
DOI
doi:10.7939/R37659S20
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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