An investigation of industrial desalting process based on artificial neural network

crude oil contains various amounts of water and inorganic salts that cause lots of penalties during its processing like corrosion , plugging and fouling of equipments , as well as poisoning the catalysts in processing units. Therefore , a desalting plan is often installed in crude oil production units to remove salts and oil -water emulsion from the feed stream. the desalting process includes crude oil washing with fresh water in an AC or AC/DC electrostatic field to remove water and salts from crude oil. the performance of the desalting process depends on various process parameters having simultaneous synergetic effects on each other. in this study the performance of refinery desalters is evaluated by calculating of the salinity and water cut efficiencies using artificial neural network ANN technique. ANN is selected due to its potential for modeling of highly nonlinear phenomena involving in the desalting process.

Keywords:

crude oil , desalting process , neural network , modeling , salinity , water cut