Building inferential estimators for modeling product quality in a crude oil desalting and dehydration process

Desalting/dehydration plants (DDP) are often installed in crude oil production units in order to remove water-soluble salts from an oil stream. This paper describes the development of simple inferential estimators for product quality of the desalting/dehydration process. The inferential estimators were constructed to capture the relationship between the product quality of the plant and the process input variables. Five input process variables that are known to influence product quality were considered. These include temperature, settling time, mixing time, chemical dosage, and dilution rate. The product quality of the desalting/dehydration process was identified by the salt removal and water cut efficiencies. Hence, inferential estimators were used to infer the salt removal and water cut efficiencies from the five input process variables. These inferential estimators were constructed based on the application of both multiple linear and principal component analysis as well as non-linear regression. The results indicate that the settling time and dilution water were the common variables in estimating both the salt removal and water cut efficiencies. On the other hand, temperature contributed insignificantly in predicting the two efficiencies. Furthermore, the inferential model predictions were compared with the experimental readings. It was found that the actual dependence of the performance of the desalting/dehydration process on process parameters could not be described only by linear relationships. Addressing the non-linearity of the process variables overcame the problem of inaccurate predictions. Future studies based on the use of computational intelligence techniques and design of experiments to get better models are suggested as well as the use of response surface methodologies to determine the set of parameters that will optimize the process efficiencies. (c) 2006 Elsevier B.V. All rights reserved.