Abstract
ABSTRACTThe formation of gas hydrates in industries and chemical plants, especially in natural gas production and transmission, is an important factor that can lead to operational and economic risks. Hence, if the hydrate conditions are well addressed, it is possible overcome hydrate-related problems. To that end, evolving an accurate and simple-to-apply approach for estimating gas hydrate formation is vitally important. In this contribution, the least square support vector machine (LSSVM) approach has been developed based on Katz chart data points to estimate natural gas hydrate formation temperature as function of the pressure and gas gravity. In addition, a genetic algorithm has been employed to optimize hyper parameters of the LSSVM. Moreover, the present model has been compared with five popular correlations and was concluded that the LSSVM approach has fewer deviations than these correlations so to estimate hydrate formation temperature. According to statistical analyses, the obtained values of MSE and R2 were 0.278634 and 0.9973, respectively. This predictive tool is simple to apply and has great potential for estimating natural gas hydrate formation temperature and can be of immense value for engineers who deal with the natural gas utilities.
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