| Abstract: | Deposition of the wax is one of the thorny issues in the petroleum industry, invoking costly problems during the transportation and production of crude oil. Owing to its devastating impacts on oil companies' economy, it is essential to develop a simple and robust strategy for the quantitative estimation of wax deposition. In this paper, support vector regression (SVR) is first proposed to estimate the amount of wax deposition. Subsequently, an artificial neural network (ANN) is developed for wax deposition prediction. Eventually, a sophisticated committee machine (CM) is constructed for combining the results of the SVR and ANN models. Optimal contribution of each model in final prediction of the wax deposit is determined through genetic algorithm in CM. Statistical error analysis shows that the CM model performs better than the individual models performing alone. |
