Stochastic Programming with Economic and Operational Risk Management in Petroleum Refinery Planning under Uncertainty
Rising crude oil price and global energy concerns have revived great interests in the oil and gas industry, including the optimization of oil refinery operations. However, the economic environment of the refining industry is typically one of low margins with intense competition. This state of the...
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my-utp-utpedia.92412013-10-22T11:20:23Z http://utpedia.utp.edu.my/9241/ Stochastic Programming with Economic and Operational Risk Management in Petroleum Refinery Planning under Uncertainty Nguyen, Thi Huynh Nga TP Chemical technology Rising crude oil price and global energy concerns have revived great interests in the oil and gas industry, including the optimization of oil refinery operations. However, the economic environment of the refining industry is typically one of low margins with intense competition. This state of the industry calls for a continuous improvement in operating efficiency by reducing costs through engineering strategies. These strategies are derived based on an understanding of the world energy market and business processes, with the incorporation of advanced financial modeling and computational tools. Regard to the matter, this work proposes the application of the two-stage stochastic programming approach with fixed recourse to effectively account for both economic and operational risk management in the planning of oil refineries under uncertainty. The scenario planning and analysis approach is adopted to consider uncertainty in three parameters: prices of crude oil and commercial products, market demand for products, and production yields. However, a large number of scenarios are required to capture the probabilistic nature of the problem. Therefore, to circumvent the problem posed by the resulting largescale model, a Monte Carlo simulation approach is implemented based on the sample average approximation (SAA) technique. The SAA technique enables the determination of the minimum number of scenarios required yet still able to compute the true optimal solution of the problem for a desired level of accuracy within the specified confidence intervals. Two measures of risk are considered, namely mean-absolute deviation (MAD) and Conditional Value-at-Risk (CVaR). A representative numerical example is presented to illustrate the proposed modeling approach using GAMS modeling language with the nonlinear solver CONOPT3. in Universiti Teknologi PETRONAS 2009-01 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/9241/1/2009%20Bachelor%20-%20Stochasting%20Programming%20With%20Economic%20And%20Operational%20Risk%20Management%20In%20Petroleu.pdf Nguyen, Thi Huynh Nga (2009) Stochastic Programming with Economic and Operational Risk Management in Petroleum Refinery Planning under Uncertainty. Universiti Teknologi PETRONAS. (Unpublished) |
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TP Chemical technology Nguyen, Thi Huynh Nga Stochastic Programming with Economic and Operational Risk Management in Petroleum Refinery Planning under Uncertainty |
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Rising crude oil price and global energy concerns have revived great interests in the
oil and gas industry, including the optimization of oil refinery operations. However,
the economic environment of the refining industry is typically one of low margins
with intense competition. This state of the industry calls for a continuous
improvement in operating efficiency by reducing costs through engineering
strategies. These strategies are derived based on an understanding of the world
energy market and business processes, with the incorporation of advanced financial
modeling and computational tools. Regard to the matter, this work proposes the
application of the two-stage stochastic programming approach with fixed recourse to
effectively account for both economic and operational risk management in the
planning of oil refineries under uncertainty. The scenario planning and analysis
approach is adopted to consider uncertainty in three parameters: prices of crude oil
and commercial products, market demand for products, and production yields.
However, a large number of scenarios are required to capture the probabilistic nature
of the problem. Therefore, to circumvent the problem posed by the resulting largescale
model, a Monte Carlo simulation approach is implemented based on the
sample average approximation (SAA) technique. The SAA technique enables the
determination of the minimum number of scenarios required yet still able to
compute the true optimal solution of the problem for a desired level of accuracy
within the specified confidence intervals. Two measures of risk are considered,
namely mean-absolute deviation (MAD) and Conditional Value-at-Risk (CVaR). A
representative numerical example is presented to illustrate the proposed modeling
approach using GAMS modeling language with the nonlinear solver CONOPT3.
in |
format |
Final Year Project |
author |
Nguyen, Thi Huynh Nga |
author_facet |
Nguyen, Thi Huynh Nga |
author_sort |
Nguyen, Thi Huynh Nga |
title |
Stochastic Programming with Economic and Operational Risk Management in
Petroleum Refinery Planning under Uncertainty |
title_short |
Stochastic Programming with Economic and Operational Risk Management in
Petroleum Refinery Planning under Uncertainty |
title_full |
Stochastic Programming with Economic and Operational Risk Management in
Petroleum Refinery Planning under Uncertainty |
title_fullStr |
Stochastic Programming with Economic and Operational Risk Management in
Petroleum Refinery Planning under Uncertainty |
title_full_unstemmed |
Stochastic Programming with Economic and Operational Risk Management in
Petroleum Refinery Planning under Uncertainty |
title_sort |
stochastic programming with economic and operational risk management in
petroleum refinery planning under uncertainty |
publisher |
Universiti Teknologi PETRONAS |
publishDate |
2009 |
url |
http://utpedia.utp.edu.my/9241/1/2009%20Bachelor%20-%20Stochasting%20Programming%20With%20Economic%20And%20Operational%20Risk%20Management%20In%20Petroleu.pdf http://utpedia.utp.edu.my/9241/ |
_version_ |
1739831649182416896 |
score |
13.18916 |