Modeling Water-in-Oil Emulsion Formation Using Fuzzy Logic
Kaan Yetilmezsoy, Merv Fingas and Ben Fieldhouse
Several compositional factors including density, viscosity, asphaltene, aromatic, saturate and resin contents play an important role to compute a class index (Stability C) which yields either an unstable or entrained water-in-oil state or a meso-stable or stable emulsion. Considering the complex structure and tedious determination procedures of water-in-oil emulsions-based problems, old regression models are not able to capture the nonlinear relationships existing between variables in a complex water-in-oil emulsion system. To undertake these tasks, derivation of a motivation for developing a robust and reliable model has become a particular field of investigation to predict a proper stability index due the involved uncertainties and their poor generalization performance. Recently, it has become apparent that alternative artificial intelligence-based methods, such as fuzzy logic methodology, have been successfully used to deal with subjects having ambiguities and uncertainties. In this study, a MISO (multiple inputs and single output) fuzzy-logic-based model was proposed as a new numerical modeling scheme for the prediction of water-in-oil emulsions formation. The fuzzy-logic predictions were compared to the actual data from some common oils and against a 15-term old regression model. Statistical results clearly indicated that, compared to the regression approach, the proposed MISO fuzzy-logic-based model showed a superior predictive performance on forecasting of water-in-oil emulsions stability with a satisfactory determination coefficient over 0.98.
Keywords: Water-in-oil emulsions; oil spill emulsions; emulsion stability; fuzzy logic; regression model.