Renovating Scaling Equation Through Hybrid Genetic Algorithm-Pattern Search Tool for Asphaltene Precipitation Modeling

The scaling equation is the most popular mathematical modeling of asphaltene precipitation as a problematic issue in petroleum industry. There are eight adjustable coefficients in the scaling equation that govern the quality of the fit between titration data and the scaling equation model. In this study, a hybrid genetic algorithm-pattern search (GA-PS) tool was employed to extract optimal values of the involved coefficients in the scaling equation through the stochastic search. For better performance of the GA-PS tool, dimensionality of the problem was broken into two simpler parts using the divide-and-conquer principle by introducing two fitness functions. The renovated scaling equation was compared with previous works; it was shown that the proposed method outperforms previous works.