Modelling of demulsification process of water in crude oil emulsion by new demulsifier

Abstract EN

Castor Oil is a natural raw material, used to prepare Brominated Castor Oil (BCO) and quaternary ammonium salt based castor oil (TEt-CO).

The two products were tested as demulsifiers and compared with a commercial demulsifier (Chimec2439) by using bottle test method.

BCO showed a high ability on water separation efficiency 90 % with a dose of 150ìl at 120min time settling while TEt-CO showed a low water separation efficiency reached to 10 %.

The effect of the demulsifier BCO was tested by varying different variables which have an obvious effect on water separation efficiency such as : dose, temperature, time of mixing emulsion, pH and salinity of aqueous phase of emulsion, and water ratio.

The effect of some additives (i.

e.

methanol, ethanol, xylene and toluene) on the efficiency of the BCO was tested for the purpose of enhancing its effectiveness to break the crude oil emulsion.

The experimental data obtained by using BCO were formulated as a model using the Artificial Neural Networks (ANNs) to evaluating the water separation efficiency.

Multi-layer perceptron artificial neural network was developed based on the collected data of this study.

The results showed that the training algorithm of back propagation (BP) is sufficient enough in predicting BCO efficiency under different operation conditions.

It was found that the correlation coefficient values are 0.9995 and 0.9999 for the testing and training data, respectively and the mean square error (MSE) was 6.18 * 10-5 at 200 epochs.