Prediction of methanol loss by hydrocarbon gas phase in hydrate inhibition unit by back propagation neural networks

Abstract

Gas hydrate often occurs in natural gas pipelines and process equipment at high pressure and low temperature. Methanol as a hydrate inhibitor injects to the potential hydrate systems and then recovers from the gas phase and re-injects to the system. Since methanol loss imposes an extra cost on the gas processing plants, designing a process for its reduction is necessary. In this study, an accurate back propagation neural network (BPNN) is designed for the prediction of methanol loss by the gas phase as a function of temperature, pressure, and methanol composition in the aqueous phase. Different configurations of BPNN were trained, tested, and a configuration providing the smallest absolute average relative deviation (AARD%) was chosen as an optimum structure. Finally, comparisons made among the accuracy of the developed BPNN model, process simulators, and probabilistic neural network (PNN). Results confirm that the designed BPNN model is more accurate than the other considered predictive tools. The BPNN provided an AARD=5.75% for prediction of experimental data, while Aspen-HYSYS, Aspen-Plus, and PNN presented an AARD% of 9.71, 12.57, and 13.27, respectively.