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The main objective of this study was to present a novel approach for predication of gas hydrate formation rate based on the Intelligent Systems . Using a data set including about 470 data obtained from flow tests in a mini-loop apparatus, different predictive models were developed. From the results predicted by these models, it can be pointed out that the developed models can be used as powerful tools for prediction of gas hydrate formation rate with total errors of less than 4%.

Comparison of intelligent systems, artificial neural networks and neural fuzzy model for prediction of gas hydrate formation rate