Application of Wavelet Neural Network to Prediction of Water Content in Crude Oil

2010 ◽  
pp. 21-27 ◽  
Author(s):  
Huifen Niu ◽  
Cuiling Liu ◽  
Jinqi Wang ◽  
Xiaowen Sun
Keyword(s):  
Neural Network ◽  
Crude Oil ◽  
Water Content ◽  
Wavelet Neural Network

FORECASTING THE CRUDE OIL SPOT PRICE BY WAVELET NEURAL NETWORKS USING OECD PETROLEUM INVENTORY LEVELS

2011 ◽  
Vol 07 (02) ◽  
pp. 281-297 ◽  
Author(s):  
YE PANG ◽  
WEI XU ◽  
LEAN YU ◽  
JIAN MA ◽  
KIN KEUNG LAI ◽  
...  
Keyword(s):  
Neural Network ◽  
Crude Oil ◽  
Linear Models ◽  
Wavelet Neural Network ◽  
Spot Price ◽  
Inventory Level ◽  
Forecasting Model ◽  
Nonlinear Relationship ◽  
Forecast Horizon ◽  
Petroleum Inventory

In this study, a novel forecasting model based on the Wavelet Neural Network (WNN) is proposed to predict the monthly crude oil spot price. In the proposed model, the OECD industrial petroleum inventory level is used as an independent variable, and the Wavelet Neural Network (WNN) is used to explore the nonlinear relationship between inventories and the price. For verification purposes, the West Texas Intermediate (WTI) crude oil spot price is used for the tested target. Experimental results reveal that the WNN can model the nonlinear relationship between inventories and the price very well. Furthermore, the in-sample and out-of-sample prediction performance also demonstrates that the WNN-based forecasting model can produce more accurate prediction results than other nonlinear and linear models, even when the lengths of the forecast horizon are relatively short or long.

A Local Linear Wavelet Neural Network Based on a Bayesian Design Method

2009 ◽  
Vol 129 (7) ◽  
pp. 1356-1362
Author(s):  
Kunikazu Kobayashi ◽  
Masanao Obayashi ◽  
Takashi Kuremoto
Keyword(s):  
Neural Network ◽  
Design Method ◽  
Wavelet Neural Network ◽  
Bayesian Design ◽  
Local Linear

Demulsification of a Water-in-crude Oil Emulsion with a Corn Oil BasedDemulsifier using the Response Surface Methodology: Modelling and Optimization

2021 ◽  
Vol 14 ◽  
Author(s):  
Abed Saad ◽  
Nour Abdurahman ◽  
Rosli Mohd Yunus
Keyword(s):  
Response Surface Methodology ◽  
Crude Oil ◽  
Water Content ◽  
Response Surface ◽  
Separation Efficiency ◽  
Corn Oil ◽  
Oil Emulsion ◽  
Optimal Values ◽  
Input Variables ◽  
Oil Emulsions

: In this study, the Sany-glass test was used to evaluate the performance of a new surfactant prepared from corn oil as a demulsifier for crude oil emulsions. Central composite design (CCD), based on the response surface methodology (RSM), was used to investigate the effect of four variables, including demulsifier dosage, water content, temperature, and pH, on the efficiency of water removal from the emulsion. As well, analysis of variance was applied to examine the precision of the CCD mathematical model. The results indicate that demulsifier dose and emulsion pH are two significant parameters determining demulsification. The maximum separation efficiency of 96% was attained at an alkaline pH and with 3500 ppm demulsifier. According to the RSM analysis, the optimal values for the input variables are 40% water content, 3500 ppm demulsifier, 60 °C, and pH 8.

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