Statistical design and process optimization for using chemical demulsifiers for the dehydration of the crude oil

Abstract

This paper uses a simple and efficiently executed response surface method (RSM) technique to develop statistically designed experiments and determine the optimum values of the influencing variables to maximize crude oil dehydration efficiency using two different demulsifiers. The bottle test method is performed to study the effect of some main influencing parameters such as demulsifier concentration, settling time, temperature, and oil content for demulsification. Experiments were designed through Central Composite Design (CCD) based on the Response Surface Method (RSM). The analysis of variance (ANOVA) is utilized to find the accuracy of the prepared model and factors, and it has been optimized using RSM. The result shows that all factors have a significant impact on demulsification efficiency and presented a p-value less than 0.05, among which oil content is the most dominant factor, and the optimized conditions for the input factors were successfully achieved using RSM. The maximum demulsification efficiency of 99.35% and 98.2% was achieved at the optimum conditions for triton x-100 and commercial demulsifiers. The results of the CCD-based technique were compared with the Box-Behnken design (BBD) based method, and it shows that the CCD-based model delivers more accuracy than the BBD-based model for predicting the demulsification efficiency.