An adaptive neuro-fuzzy approach for modeling of water-in-oil emulsion formation

Abstract

Oil composition and properties including density, viscosity, asphaltene, saturate, aromatics and resin contents are responsible factors for the formation of water-in-crude-oil emulsions. These factors can be used to develop an stability index which determines states of water-in-oil emulsion in terms of either an unstable, entrained, mesostable or stable conditions. It is important to note that most of the regression models cannot capture the non-linear relationships involved in the formation of these emulsions. This study deals with the prediction of water-in-oil emulsions stability by an adaptive neuro-fuzzy inference system (ANFIS) with basic compositional factors such as density, viscosity and percentages of SARA (saturates, aromatics, resins, and asphaltenes) components. In the computational method, grid partition and subtractive clustering fuzzy inference systems were tried to generate the optimum fuzzy rule base sets. The stability estimation was conducted by applying hybrid learning algorithm and the model performance was tested by the means of distinct test data set randomly selected from the experimental domain. The ANFIS-based predictions were also compared to the conventional regression approach by means of various descriptive statistical indicators, such as root mean-square error (RMSE), index of agreement (IA), the factor of two (FA2), fractional variance (FV), proportion of systematic error (PSE), etc. With trying various types of fuzzy inference system (FIS) structures and several numbers of training epochs ranging from 1 to 100, the lowest root mean square error (RMSE = 2.0907) and the highest determination coefficient ( R 2 = 0.967) were obtained with subtractive clustering method of a first-order Sugeno type FIS. For the optimum ANFIS structure, input variables were fuzzified with four Gaussian membership functions, and the number of training epochs was computed as 21. In the computational analysis, the predictive performance of the ANFIS model was examined for the following ranges of the clustering parameters: range of influence (ROI) = 0.45–0.60, squash factor (SF) = 1.20–1.35, accept ratio (AR) = 0.40–0.55, and reject ratio (RR) = 0.10–0.20. Results indicated that ROI, SF, AR and RR were obtained to be 0.54, 1.25, 0.50 and 0.15, respectively, for the best FIS structure. It was clearly concluded that the proposed ANFIS model demonstrated a superior predictive performance on forecasting of water-in-oil emulsions stability. Findings of this study clearly indicated that the neuro-fuzzy modeling could be successfully used for predicting the stability of a specific water-in-oil mixture to provide a good discrimination between several visual stability conditions.