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K. H. Bawazeer, “Prediction of Crude Oil Product Quality Parameters Using Neural Networks,” M.S. Thesis, Florida Atlantic University, Boca Raton, 1996.

has been cited by the following article:

• TITLE: Investigations into Optimization Models of Crude Oil Distillation Column in the Context of Feed Stock and Market Value

  AUTHORS: Lekan T. Popoola, Jamiu A. Adeniran, Solomon O. Akinola

  KEYWORDS: Feed Stock; Crude Oil Distillation Column; Constraints; Market Condition; Reboiler; Crude Composition; Optimization

  JOURNAL NAME: Advances in Chemical Engineering and Science, Vol.2 No.4, November 1, 2012

  ABSTRACT: This paper proposes optimization models of crude oil distillation column for both limited and unlimited feed stock and market value of known products prices. The feed to the crude distillation column was assumed to be crude oil containing naphtha gas, kerosene, petrol and diesel as the light-light key, light key, heavy key and heavy-heavy key respectively. The models determined maximum concentrations of heavy key in the distillate and light key in the bottom for limited feed stock and market condition. Both were impurities in their respective positions of the column. The limiting constraints were sales specification concentration of light key in the distillate [ ], heavy key in the bottom [ ] and an operating loading constraint of flooding above the feed tray. For unlimited feed stock and market condition, the optimization models determined the optimum separation [ and ] and feed flow rate that would give maximum profit with minimum purity sales specification constraints of light key in the distillate and heavy key in the bottom as stated above. The feed loading was limited by the reboiler capacity. However, there is need to simulate the optimization models for an existing crude oil distillation column of a refinery in order to validate the models.