Prediction of natural gas hydrate inhibitor vaporization rate using particle swarm optimization approach

Natural gas is a very important source of energy. In natural gas processing, accurate prediction of methanol loss to the vapor phase during natural gas hydrate inhibition is necessary to compute the total methanol injection rate required to effectively prevent the formation of natural gas hydrate. A reliable prediction tool that has the capability to accurately predict methanol losses to the vapor phase is thus needed. In order to address this matter, the current research was aimed at assessing the ability and feasibility of a robust computational intelligence paradigm. Based on a total of 326 dataset collected from the reliable literature, methanol loss to the vapor phase was predicted using artificial neural network (ANN) linked with particle swarm optimization (PSO) which is employed to determine the optimal values of the ANN weights. Success of the introduced hybrid intelligence model (or PSO-ANN) was confirmed with overall mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R2) values of 0.16421, 0.33210, and 0.99696, respectively.

Artificial neural network, gas hydrate, hydrate inhibition, methanol loss, natural gas, prediction, particle swarm optimization