eprintid: 790
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/07/90
datestamp: 2023-11-09 15:48:56
lastmod: 2023-11-09 15:48:56
status_changed: 2023-11-09 15:23:11
type: article
metadata_visibility: show
creators_name: Khor, C.S.
creators_name: Nguyen, T.H.N.
title: Stochastic programming with tractable meanrisk objectives for refinery planning under uncertainty
ispublished: pub
note: cited By 1
abstract: The application of information technology (IT) and information systems (IS) have been crucial in enhancing the operating flexibility and resiliency of refineries. In particular, the process systems engineering (PSE) community has been instrumental in carrying out a key role in extending the systems engineering boundaries from mere chemical process systems to the incorporation of business process systems with consideration for risk. Thus, this paper considers a robust framework for the economic and operational risk management of a refinery under uncertainty by extending an existing two-stage stochastic program with fixed recourse via scenario analysis. The problem is mathematically formulated as a two-stage stochastic nonlinear program with a tractable mean-risk structure in the objective function. Two measures of risk are considered, namely the metrics of mean-absolute deviation (MAD) and Conditional Value-at-Risk (CVaR). The scenario analysis approach is adopted to represent uncertainties in three types of stochastic parameters, namely prices of crude oil and commercial products, market demands, and production yields. However, a large number of scenarios are required to capture the stochasticity of the problem. Therefore, to circumvent the problem of the resulting large-scale model, we implement a Monte Carlo simulation approach based on the sample average approximation (SAA) technique to generate the scenarios. A statistical-based scenario reduction strategy is applied to determine the minimum number of scenarios required yet still able to compute the true optimal solution for a desired level of accuracy within the specified confidence intervals. The proposed model is illustrated through a representative numerical example, with computational results demonstrating how risk-averse-and risk-inclined solutions in the face of uncertainty can be attained in a risk-conscious model. © 2009 Elsevier B. V. All rights reserved.
date: 2009
publisher: Elsevier B.V.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-77649323938&doi=10.1016%2fS1570-7946%2809%2970718-7&partnerID=40&md5=a8cf47f67372c788e29f00b62f5a12eb
id_number: 10.1016/S1570-7946(09)70718-7
full_text_status: none
publication: Computer Aided Chemical Engineering
volume: 27
number: C
pagerange: 1965-1970
refereed: TRUE
issn: 15707946
citation:   Khor, C.S. and Nguyen, T.H.N.  (2009) Stochastic programming with tractable meanrisk objectives for refinery planning under uncertainty.  Computer Aided Chemical Engineering, 27 (C).  pp. 1965-1970.  ISSN 15707946