A new correlation for predicting hydrate formation conditions for ...

( )A.A. Elgibaly, A.M. ElkamelrFluid Phase Equilibria 152 1998 23–42 39Table 6Relative importance of the various inputs in the general compositional modelInput Relative importance Ž %.T 14.1C111.2C 7.852C 8.963i-C 4.964n-C 4.194C55.51qC 3.926CO 6.302N 6.362H 2S 4.90MeOH 8.48Nacl 6.21EG 3.71CaCl22.20EtOH 1.45results showed that temperature has a relative importance of 42% while gravity has a bigger share of58%.3.6. Integration with a dosing control systemHydrate formation control should be considered to be an on-line service activity. The formation ofhydrates can be avoided by introducing computerized systems along with monitoring instruments andon-line data collection. The objective is to formulate prompt and reliable remedial actions by injectingthe appropriate amount of inhibitor for the measured temperature and pressure of the system. Thistype of ‘modern strategy’ for inhibition will minimize the cost of chemicals used for inhibition ofhydrate formation, and maximize systems effectiveness.In order to determine the required amount of an inhibitor to avoid the formation of hydrates withina system, the comprehensive compositional neural network model Ž Model-D.presented in this papercan be used. This can be done by invoking a valuable property of a neural network: the ability tosolve the inverse problem of determining the amount of an inhibitor Ž for instance.that will lead to nohydrate formation at the current temperature, pressure and composition of other components. Thus,one can envision the proposed neural network model-D to be integrated within a generalized inhibitorcontrol system consisting of monitoring instrumentation and on-line data collections. The study ofsuch integration will be the subject of further research.4. Concluding remarksUtilization of the neural network technique for predicting the hydrate formation conditions hasbeen investigated in the present study. The technique has been applied on a total of 2387 experimental