This work presents the investigations of the application of synthesized ZnO nanoparticles to methane gas hydrate formation. The ZnO nanoparticles were synthesized in one-pot synthesis with an application of binding chemicals to control the size of the nanoparticles. The addition of surfactant imparts better gas hydrate formation properties to the ZnO particles apart from mass transfer enhancement. The characterization of nanoparticles was performed using complementary analytical facilities. The characterization confirms the presence of organic molecules as a binding component of nanoparticles. The comparative study of gas hydrate formation using ZnO nanoparticles was performed with pure water. The experimental results proved that the impact of nanoparticles could enhance or inhibit the gas hydrate formation based on the additives used during the synthesis. The experimental results were further confirmed with the help of artificial intelligence in deep learning based artificial neural network modeling. The model predicted results mimic the experimental results very well and can further be used to develop the nanoparticle-based gas hydrate formation study. The experimental and modeling study signifies the impact of ZnO nanoparticles on methane gas uptake in the form of gas hydrate and opens up new opportunities to develop sustainable, efficient, and inexpensive processes.
Keywords gas hydrates, methane hydrate formation kinetics, promotor, nanoparticles, artificial intelligence, artificial neural network