Modeling of true vapor pressure of petroleum products using ANFIS algorithm

The aim of this contribution was to develop a simple tool based on fuzzy logic concepts to predict true vapor pressure of volatile petroleum products. In this regard, the adaptive neuro fuzzy inference system was evolved to estimate the true vapor pressure of volatile petroleum products as function of temperature and Reid vapor pressure. In addition, to determine optimal membership function parameters, the particle swarm optimization as an amazing evolutionary algorithm was applied. This predictive tool is suggested as a precise technique to measure the true vapor pressures of typical liquefied petroleum gases, natural gasoline, and motor fuel components at broad ranges of temperatures. This technique was trained and tested by 156 set of data points collected from the reference. The temperature range is 253–373 K and the range of Reid vapor pressure is 35–250 KPa. Results obtained from the present tool found to be in acceptable agreement with the actual reported data in the literature. The values of root mean square error and regression coefficient obtained 5.34 and 0.9975, respectively.