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Improving Productivity through Linear Programming: A Case of Oil Refinery Industry
Technical Paper
2020-01-5128
ISSN: 0148-7191, e-ISSN: 2688-3627
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Automotive Technical Papers
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English
Abstract
In recent years, the petroleum industry has faced an unpredictable and increasingly unstable market. This instability causes drastic fluctuations in the oil prices, which in turn affects the demand for the product. Refineries have confronted an impossible situation, where if crude oil is purchased at a certain price, in a matter of days for a what-so-ever reason the oil prices take a hit and they are forced to sell the oil at a lower price, which is not desirable. If the refinery gambles to buy bulk of crude oil at a bargain, the literature suggests that the chances are of a decrease in a product demand due to increasing oil price, which again is not desirable. Moreover, refinery industries also have to face the consequences of rapidly changing exchange rates. In situations like this, it becomes essential for the refineries to reduce losses as much as possible, increase productivity, and reduce the cost of its operations. In this research, techniques of linear programming (LP) were used to increase the productivity of processing plant’s high-speed diesel, kerosene, naphtha, vacuum gas oil, and vacuum residue. For this, a model was developed to achieve the optimized productivities of all the products in a single blend. Productions were simulated on the results obtained by the developed model. It was found that the developed model can effectively reduce the associated cost and deliver the current production quantity much quicker. Further, the proposed scheme produced promising and better results than the currently available methods (commercial software).
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Fatima, A. and Tufail, M., "Improving Productivity through Linear Programming: A Case of Oil Refinery Industry," SAE Technical Paper 2020-01-5128, 2020, https://doi.org/10.4271/2020-01-5128.Data Sets - Support Documents
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References
- Ewing , B.T. and Thompson , M.A. Dynamic Cyclical Comovements of Oil Prices with Industrial Production, Consumer Prices, Unemployment, and Stock Prices Energy Policy 35 11 5535 5540 2007
- Baumeister , C. , and Kilian , L. Forty Years of Oil Price Fluctuations: Why the Price of Oil May Still Surprise Us Journal of Economic Perspectives 30 1 139 160 2016
- Boué , J.C. 1997
- Hartmann , J. Crude Valuation for Crude Selection Petroleum Technology Quarterly 123 128 2003
- Stratiev , D. et al. Evaluation of Crude Oil Quality Petroleum & Coal 52 1 35 43 2010
- Kelly , J.D. , Menezes , B.C. , and Grossmann , I.E. Distillation Blending and Cutpoint Temperature Optimization Using Monotonic Interpolation Industrial & Engineering Chemistry Research 53 39 15146 15156 2014
- Krutof , A. and Hawboldt , K. Blends of Pyrolysis Oil, Petroleum, and Other Bio-Based Fuels: A Review Renewable and Sustainable Energy Reviews 59 406 419 2016
- Gulsun , B. et al. An Aggregate Production Planning Strategy Selection Methodology Based on Linear Physical Programming International Journal of Industrial Engineering 16 2 135 146 2009
- Sudit , E.F. Productivity Measurement in Industrial Operations European Journal of Operational Research 85 3 435 453 1995
- Garvin , W.W. et al. Applications of Linear Programming in the Oil Industry Management Science 3 4 407 430 1957
- Strayer , J.K. Linear Programming and Its Applications New York Springer-Verlag 1989
- Hassan , M. , Kandeil , A. , and Elkhayat , A. Improving Oil Refinery Productivity through Enhanced Crude Blending Using Linear Programming Modeling Asian Journal of Scientific Research 4 95 113 2011
- Hou , J. , Li , X. , and Sui , H. The Optimization and Prediction of Properties for Crude Oil Blending Computers & Chemical Engineering 76 21 26 2015
- Charnes , A. , Cooper , W.W. , and Mellon , B. Blending Aviation Gasolines: A Study in Programming Interdependent Activities in an Integrated Oil Company Econometrica (pre-1986) 20 2 135 1952
- European Commission Amended proposal for a European Parliament and Council Directive relating to the quality of petrol and diesel fuels and amending Council Directive 93/12/EEC 1997 https://ec.europa.eu/search/?QueryText=93%2F12%2FEEC&op=Search&swlang=en&form_build_id=form-ys_7-kek7oQ4pFpHPB9IF3JgXxxdzGwXG48YvNff5Jk&form_id=nexteuropa_europa_search_search_form
- Marco , G.L. Use of the Logistic Model as an Alternative to Linear Interpolation for Computing Percentile Ranks Journal of Educational Measurement 14 3 271 275 1977
- Demirbas , A. and Bamufleh , H.S. Optimization of Crude Oil Refining Products to Valuable Fuel Blends Petroleum Science and Technology 35 4 406 412 2017
- Popoola , L.T. , Adeniran , J.A. , and Akinola , S.O. Investigations into Optimization Models of Crude Oil Distillation Column in the Context of Feed Stock and Market Value Advances in Chemical Engineering and Science 02 04 7 2012
- ASTM International 2020
- Li , S. et al. Distillation Yields and Properties from Blending Crude Oils: Maxila and Cabinda Crude Oils, Maxila and Daqing Crude Oils Energy & Fuels 21 2 1145 1150 2007
- Schreyer , P. The OECD Productivity Manual: A Guide to the Measurement of Industry-Level and Aggregate Productivity International Productivity Monitor 2 2 37 51 2001