Wastewater minimization under uncertain operational conditions

Suad A Al-Redhwan, Barry D Crittenden, Haitham M S Lababidi

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

This paper addresses the problem of uncertainty in optimizing water networks in process industries. Due to the fact that wastewater flow rates as well as the levels of contaminants may vary widely as a result of changes in operational conditions and/or feedstock and product specifications, optimal wastewater network designs should be resilient and able to accommodate such changes. Uncertainties considered in this study are derived from actual operational practice of major water-using units in a typical oil refinery of 400,000 barrels/day throughput. Rather than directly varying the concns. and mass loads, only seasonal effects have been considered in this research to illustrate applications of the models. Sensitivity analyses reveal that introducing uncertainty in operating conditions results in considerable changes in the connectivity of the units involved in wastewater reuse. The proposed stochastic optimization model produces a flexible wastewater network which is capable of accommodating uncertainties in operating temp. In the presence of uncertainties, the optimal network minimizes the impact on the reuse connectivity (topol.) by providing 32.2 tons/h of freshwater in addn. to the condensing steam. The stochastic approach adopted in this research has been found to be effective in handling uncertainties and has resulted in flexible and resilient wastewater networks with low expected value of perfect information. [on SciFinder (R)]
Original languageEnglish
Pages (from-to)1009-1021
Number of pages13
JournalComputers and Chemical Engineering
Volume29
Issue number5
Publication statusPublished - 2005

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Wastewater
Water
Steam
Feedstocks
Uncertainty
Oils
Flow rate
Throughput
Impurities
Specifications
Industry

Keywords

  • Petroleum refining
  • Wastewater (industrial
  • Industrial waters
  • Simulation and Modeling (wastewater minimization under uncertain operational conditions)
  • wastewater minimization industrial water
  • Optimization
  • wastewater minimization under uncertain operational conditions)

Cite this

Wastewater minimization under uncertain operational conditions. / Al-Redhwan, Suad A; Crittenden, Barry D; Lababidi, Haitham M S.

In: Computers and Chemical Engineering, Vol. 29, No. 5, 2005, p. 1009-1021.

Research output: Contribution to journalArticle

Al-Redhwan, Suad A ; Crittenden, Barry D ; Lababidi, Haitham M S. / Wastewater minimization under uncertain operational conditions. In: Computers and Chemical Engineering. 2005 ; Vol. 29, No. 5. pp. 1009-1021.
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AB - This paper addresses the problem of uncertainty in optimizing water networks in process industries. Due to the fact that wastewater flow rates as well as the levels of contaminants may vary widely as a result of changes in operational conditions and/or feedstock and product specifications, optimal wastewater network designs should be resilient and able to accommodate such changes. Uncertainties considered in this study are derived from actual operational practice of major water-using units in a typical oil refinery of 400,000 barrels/day throughput. Rather than directly varying the concns. and mass loads, only seasonal effects have been considered in this research to illustrate applications of the models. Sensitivity analyses reveal that introducing uncertainty in operating conditions results in considerable changes in the connectivity of the units involved in wastewater reuse. The proposed stochastic optimization model produces a flexible wastewater network which is capable of accommodating uncertainties in operating temp. In the presence of uncertainties, the optimal network minimizes the impact on the reuse connectivity (topol.) by providing 32.2 tons/h of freshwater in addn. to the condensing steam. The stochastic approach adopted in this research has been found to be effective in handling uncertainties and has resulted in flexible and resilient wastewater networks with low expected value of perfect information. [on SciFinder (R)]

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