Estimation of emission of hydrocarbons and filling losses in storage containers using intelligent models

The emission of hydrocarbon plays a key role in the oil and gas production industries and can pose a danger. In the current cooperation, two intelligent simple tools, namely, support vector machine (SVM) and adaptive network based fuzzy inference system (ANFIS), have been developed to predict the amount of filling loss in storage tanks at vapor pressures ranging between 0 and 101 KPa and working pressures ranging between 101.325 and 251.325 KPa. Based on statistical analysis, estimations by the SVM approach show better accuracy than the ANFIS method. The proposed models are easy to apply and would be of great assistance to engineers, particularly those dealing with the design and applications of storage tanks. The efforts in this study will cover the manner for making precise estimations of the filling losses in storage tanks, which can help researchers and engineers control the operational conditions.