Application of sensitivity analysis to oil refinery emissions

Whitcombe, Joshua Matthew; Cropp, Roger Allan; Braddock, Roger David; Agranovski, Igor E.

doi:10.1016/s0951-8320(02)00232-6

Cited by 8 publications

(6 citation statements)

References 5 publications

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“…The model developed is an expansion of a simplified linear model previously published by the current authors [6]. The model is a simplified linear-based model based on various equations linked together to simulate the movement of particles through a FCCU.…”

Section: Development Of the Modelmentioning

confidence: 99%

“…All samples were obtained using Method 5 isokinetic conditions to ensure accuracy of the results. The actual sampling methodology and detailed results are presented elsewhere [6].…”

Section: Fccu Testsmentioning

confidence: 99%

“…This loss of particle material results in a subsequent financial and environmental burden associated with the control of potential air pollution issues as well as having to continually add more catalyst to maintain the bed level [3][4][5][6]. The correct design and operation are essential to avoid unnecessary solid loss via entrainment in a fluidized bed.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Characterization of Air Emissions from Laboratory and Industrial Fluidized Beds

2008

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A fluidized bed model using several elutriation correlations was developed and tested against an operating fluidized bed used in a Fluidized Catalytic Cracker Unit (FCCU) and a 1:8.5 scale laboratory system. It was found that there was little variation between the emission rates predicted using different elutriation correlations, although the newly developed equations were slightly more accurate for the laboratory-scale system. Although total emission rates were predicted with reasonable accuracy, the actual volatility and fluctuations seen in real fluidized beds emissions were not predicted. When the model was used to predict particle emission from the industrial FCCU, they preformed poorly, grossly overestimating the actual levels. It was determined that the attrition terms used in emission modeling were inappropriate and that the model preformed better without them, but still overestimated the actual emissions. This overestimation was greater in the industrial system compared with the smaller laboratory system. It was also found that the older elutriation terms were better for predicting industrial emissions compared with those of the smaller scale units.

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Section: Development Of the Modelmentioning

confidence: 99%

“…All samples were obtained using Method 5 isokinetic conditions to ensure accuracy of the results. The actual sampling methodology and detailed results are presented elsewhere [6].…”

Section: Fccu Testsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling and Characterization of Air Emissions from Laboratory and Industrial Fluidized Beds

2008

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“…Applications have focussed on safetycritical areas such as the modelling of nuclear waste repositories and treatment facilities [12] and food safety modelling [13] where detailed understanding of model behaviour is vital. Another significant application area has been the analysis of environmental issues [14][15][16].…”

Section: Global Sensitivity Analysismentioning

confidence: 99%

Ranking bioprocess variables using global sensitivity analysis: a case study in centrifugation

King

Titchener-Hooker

Zhou

2007

Bioprocess Biosyst Eng

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The selection of appropriate operating conditions for bioprocessing is complex due to the large number of interacting stages and variables. Bioprocesses also operate under tight regulation and therefore tools to assist bioprocess design are of significant utility. Conventional approaches for the analysis of variable sensitivities are inadequate. We propose the use of global sensitivity analysis to determine the level of importance of each variable and their interactions. Once key variables have been determined, the designer may focus on the most significant subset. Two case studies are used to demonstrate the applicability of the approach. Each is based on centrifugation and determines the impact of flow-rate, feed viscosity, density difference and particle size, while performance is assessed by supernatant clarification. Significant differences in sensitivities were found between the two studies due to the different feed material properties. Variable sensitivities were found to be system-specific and provide insight for potential operating strategies.

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“…The oil and gas refinery industry in Europe provides 42% of the energy requirements and 95% of the fuels required for transport by the operation of more than 100 refineries that process 700 million tones of crude oil per annum (EC, 2003). Each of these oil refineries is an emission source of various pollutants, mainly volatile organic compounds (VOCs), which have been the subject of various environmental analyses (Bevilacqua and Braglia, 2002;Abdul-Wahab et al, 2002;Cetin et al, 2003;Whitcombe et al, 2003;Mata et al, 2005;Ras et al, 2009;Tompa et al, 2005;Majumdar et al, 2008;Gielen et al, 2002;Ostermark, 1996;Khan and Ghoshal, 2000;Na et al, 2001;Liu et al, 2008;Amaral et al, 1996;Lin et al, 2004;Guo et al, 2007;Cheng et al, 2008;Clements and Cheng, 1982;Snider and Manning, 1982).…”

Section: Introductionmentioning

confidence: 99%

Introduction of cleaner production in the tank farm of the Pancevo Oil Refinery, Serbia

Jovanović

et al. 2010

Journal of Cleaner Production

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Application of sensitivity analysis to oil refinery emissions

Cited by 8 publications

References 5 publications

Modeling and Characterization of Air Emissions from Laboratory and Industrial Fluidized Beds

Modeling and Characterization of Air Emissions from Laboratory and Industrial Fluidized Beds

Ranking bioprocess variables using global sensitivity analysis: a case study in centrifugation

Introduction of cleaner production in the tank farm of the Pancevo Oil Refinery, Serbia

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