The Prediction of Hydrate Formation Rate in the Presence of Inhibitors

Abstract

The main objective of this study was to present a novel approach to access more accurate hydrate formation rate predicting models based on the combination of flow-loop experimental data with learning power of artificial neural networks. Therefore, more than 2,300 data of C₁, C₃, i-C₄, and CO₂ hydrate formation rate in the presence of two kinetic inhibitors (PVP and L-Tyrosine) and two inhibitor intensifying additives (PPO and PEO) was used. It was found that such models can be used as powerful tools, with total errors less than 2% for the developed models, in predicting hydrate formation rate in these cases.

Keywords::

• artificial neural network
• gas hydrate formation
• kinetic inhibitor
• polymeric additives
• rate model