Investigation of Parameters Affecting the Performance of Electrostatic Desalting Process Using Neural Network

Dispersed water-in-oil as a stable emulsion causes numerous problems in extraction, transportation and refining of the crude oil. In the most desalting units, high voltage electrical field is utilized to separate water and ionic components from the crude oil. The efficiency of desalting units depends on operational conditions and hence in this study the result of several parameters on salt content of output crude oil in a desalting unit was considered for both theoretical and experimental studies. For this goal, optimized artificial neural network (ANN) using cuckoo optimization algorithm was applied to simulate the process. The optimum temperature, water  injection rate, retention time, differential pressure of mixing valves and injection rate of demulsifier were predicted by the consequences of simulation as the optimum value for each of the parameters was respectively equal to 79 ppm, 3.25%, 8.5 bar and 90 ppm. Then, because of the significant effect of the demulsifiers, the variation of each parameter was evaluated in the presence of four types of demulsifier separately. The results showed that an increase in the basic sediment and water content (BS&W) and specific gravity of crude oil has adverse effects on desalting process efficiency.