Selection of Crude Oil Assays for Petroleum Refining

The oily sludge from crude oil contains hazardous BTEX (benzene, toluene, ethylbenzene, xylene) found in the bottom sediment of the crude oil tank in the petroleum refining plant. This study uses microwave treatment of the oily sludge to remove BTEX by utilizing the heat energy generated by the microwave. The results show that when the oily sludge sample was treated for 60 s under microwave power from 200 to 300 W, the electric field energy absorbed by the sample increased from 0.17 to 0.31 V/m and the temperature at the center of the sludge sample increased from 66.5 °C to 96.5 °C. In addition, when the oily sludge was treated for 900 s under microwave power 300 W, the removal rates were 98.5% for benzene, 62.8% for toluene, 51.6% for ethylbenzene, and 29.9% for xylene. Meanwhile, the highest recovery rates of light volatile hydrocarbons in sludge reached 71.9% for C3, 71.3% for C4, 71.0% for C5, and 78.2% for C6.

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Data Size Requirement for Forecasting Daily Crude Oil Price with Neural Networks

Scientific Annals of Economics and Business ◽

10.47743/saeb-2019-0027 ◽

2019 ◽

Vol 66 (3) ◽

pp. 363-388

Author(s):

Serkan Aras ◽

Manel Hamdi

Keyword(s):

Neural Networks ◽

Crude Oil ◽

Significant Interaction ◽

Statistical Tests ◽

Oil Price ◽

Training Data ◽

Forecasting Model ◽

Crude Oil Price ◽

Crude Oil Prices ◽

Selection Of

When the literature regarding applications of neural networks is investigated, it appears that a substantial issue is what size the training data should be when modelling a time series through neural networks. The aim of this paper is to determine the size of training data to be used to construct a forecasting model via a multiple-breakpoint test and compare its performance with two general methods, namely, using all available data and using just two years of data. Furthermore, the importance of the selection of the final neural network model is investigated in detail. The results obtained from daily crude oil prices indicate that the data from the last structural change lead to simpler architectures of neural networks and have an advantage in reaching more accurate forecasts in terms of MAE value. In addition, the statistical tests show that there is a statistically significant interaction between data size and stopping rule.

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The Selection of the Optimum Offers in Crude Oil Market by Linear Programming

Operations Research ’92 ◽

10.1007/978-3-662-12629-5_88 ◽

1993 ◽

pp. 306-309

Author(s):

Gheorghe Barbatu ◽

C. E. Marius ◽

V. Ionescu

Keyword(s):

Linear Programming ◽

Crude Oil ◽

Oil Market ◽

Selection Of

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A Mathematical Model of Evaporation and Dissolution From Oil Spills on Ice, Land, Water and Under Ice

Water Quality Research Journal ◽

10.2166/wqrj.1975.016 ◽

1975 ◽

Vol 10 (1) ◽

pp. 132-141 ◽

Cited By ~ 1

Author(s):

P.J. Leinonen ◽

D. Mackay

Keyword(s):

Mass Transfer ◽

Crude Oil ◽

Transfer Coefficient ◽

Mass Transfer Coefficient ◽

Oil Spills ◽

Aquatic Toxicity ◽

Eddy Diffusivity ◽

Model Parameters ◽

Evaporation Flux ◽

Selection Of

Abstract Mathematical models are presented which quantify the processes of evaporation and dissolution of components of crude oil in three situations: a spill on water, a spill on ice, and a spill under ice cover in which the oil lies between the water and ice phases. Constant spill area is assumed. The evaporation flux is calculated using a mass transfer coefficient based on windspeed and spill dimensions. The dissolution flux can be calculated from two models, a mass transfer coefficient approach and an eddy diffusivity approach involving the integration of a set of partial differential equations in depth and time. The selection of model parameters is discussed. For the three physical situations, using a synthetic crude oil, results are presented giving the relative rates of evaporation and dissolution and the aqueous phase concentration of selected hydrocarbons. The implications of the results for clean-up technology and aquatic toxicity are discussed, particularly with regard to spills under ice.

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