Accurate gas viscosity determination is an important issue in the oil and gas industries.Experimental approaches for gas viscosity measurement are timeconsuming,expensive and hardly possible at high pressures and high...Accurate gas viscosity determination is an important issue in the oil and gas industries.Experimental approaches for gas viscosity measurement are timeconsuming,expensive and hardly possible at high pressures and high temperatures(HPHT).In this study,a number of correlations were developed to estimate gas viscosity by the use of group method of data handling(GMDH)type neural network and gene expression programming(GEP)techniques using a large data set containing more than 3000 experimental data points for methane,nitrogen,and hydrocarbon gas mixtures.It is worth mentioning that unlike many of viscosity correlations,the proposed ones in this study could compute gas viscosity at pressures ranging between 34 and 172 MPa and temperatures between 310 and 1300 K.Also,a comparison was performed between the results of these established models and the results of ten wellknown models reported in the literature.Average absolute relative errors of GMDH models were obtained 4.23%,0.64%,and 0.61%for hydrocarbon gas mixtures,methane,and nitrogen,respectively.In addition,graphical analyses indicate that the GMDH can predict gas viscosity with higher accuracy than GEP at HPHT conditions.Also,using leverage technique,valid,suspected and outlier data points were determined.Finally,trends of gas viscosity models at different conditions were evaluated.展开更多
A major goal of coastal engineering is to develop models for the reliable prediction of short-and longterm near shore evolution.The most successful coastal models are numerical models,which allow flexibility in the ch...A major goal of coastal engineering is to develop models for the reliable prediction of short-and longterm near shore evolution.The most successful coastal models are numerical models,which allow flexibility in the choice of initial and boundary conditions.In the present study,evolutionary algorithms(EAs)are employed for multi-objective Pareto optimum design of group method data handling(GMDH)-type neural networks that have been used for bed evolution modeling in the surf zone for reflective beaches,based on the irregular wave experiments performed at the Hydraulic Laboratory of Imperial College(London,UK).The input parameters used for such modeling are significant wave height,wave period,wave action duration,reflection coefficient,distance from shoreline and sand size.In this way,EAs with an encoding scheme are presented for evolutionary design of the generalized GMDH-type neural networks,in which the connectivity configurations in such networks are not limited to adjacent layers.Also,multi-objective EAs with a diversity preserving mechanism are used for Pareto optimization of such GMDH-type neural networks.The most important objectives of GMDH-type neural networks that are considered in this study are training error(TE),prediction error(PE),and number of neurons(N).Different pairs of these objective functions are selected for two-objective optimization processes.Therefore,optimal Pareto fronts of such models are obtained in each case,which exhibit the trade-offs between the corresponding pair of the objectives and,thus,provide different non-dominated optimal choices of GMDH-type neural network model for beach profile evolution.The results showed that the present model has been successfully used to optimally prediction of beach profile evolution on beaches with seawalls.展开更多
There are numerous correlations and thermodynamic models for predicting the natural gas hydrate formation condition but still the lack of a simple and unifying general model that addresses a broad ranges of gas mixtur...There are numerous correlations and thermodynamic models for predicting the natural gas hydrate formation condition but still the lack of a simple and unifying general model that addresses a broad ranges of gas mixture.This study was aimed to develop a user-friendly universal correlation based on hybrid group method of data handling(GMDH)for prediction of hydrate formation temperature of a wide range of natural gas mixtures including sweet and sour gas.To establish the hybrid GMDH,the total experimental data of 343 were obtained from open articles.The selection of input variables was based on the hydrate structure formed by each gas species.The modeling resulted in a strong algorithm since the squared correlation coefficient(R2)and root mean square error(RMSE)were 0.9721 and 1.2152,respectively.In comparison to some conventional correlation,this model represented not only the outstanding statistical parameters but also its absolute superiority over others.In particular,the result was encouraging for sour gases concentrated at H2S to the extent that the model outstrips all available thermodynamic models and correlations.Leverage statistical approach was applied on datasets to the discovery of the defected and doubtful experimental data and suitability of the model.According to this algorithm,approximately all the data points were in the proper range of the model and the proposed hybrid GMDH model was statistically reliable.展开更多
文摘Accurate gas viscosity determination is an important issue in the oil and gas industries.Experimental approaches for gas viscosity measurement are timeconsuming,expensive and hardly possible at high pressures and high temperatures(HPHT).In this study,a number of correlations were developed to estimate gas viscosity by the use of group method of data handling(GMDH)type neural network and gene expression programming(GEP)techniques using a large data set containing more than 3000 experimental data points for methane,nitrogen,and hydrocarbon gas mixtures.It is worth mentioning that unlike many of viscosity correlations,the proposed ones in this study could compute gas viscosity at pressures ranging between 34 and 172 MPa and temperatures between 310 and 1300 K.Also,a comparison was performed between the results of these established models and the results of ten wellknown models reported in the literature.Average absolute relative errors of GMDH models were obtained 4.23%,0.64%,and 0.61%for hydrocarbon gas mixtures,methane,and nitrogen,respectively.In addition,graphical analyses indicate that the GMDH can predict gas viscosity with higher accuracy than GEP at HPHT conditions.Also,using leverage technique,valid,suspected and outlier data points were determined.Finally,trends of gas viscosity models at different conditions were evaluated.
文摘A major goal of coastal engineering is to develop models for the reliable prediction of short-and longterm near shore evolution.The most successful coastal models are numerical models,which allow flexibility in the choice of initial and boundary conditions.In the present study,evolutionary algorithms(EAs)are employed for multi-objective Pareto optimum design of group method data handling(GMDH)-type neural networks that have been used for bed evolution modeling in the surf zone for reflective beaches,based on the irregular wave experiments performed at the Hydraulic Laboratory of Imperial College(London,UK).The input parameters used for such modeling are significant wave height,wave period,wave action duration,reflection coefficient,distance from shoreline and sand size.In this way,EAs with an encoding scheme are presented for evolutionary design of the generalized GMDH-type neural networks,in which the connectivity configurations in such networks are not limited to adjacent layers.Also,multi-objective EAs with a diversity preserving mechanism are used for Pareto optimization of such GMDH-type neural networks.The most important objectives of GMDH-type neural networks that are considered in this study are training error(TE),prediction error(PE),and number of neurons(N).Different pairs of these objective functions are selected for two-objective optimization processes.Therefore,optimal Pareto fronts of such models are obtained in each case,which exhibit the trade-offs between the corresponding pair of the objectives and,thus,provide different non-dominated optimal choices of GMDH-type neural network model for beach profile evolution.The results showed that the present model has been successfully used to optimally prediction of beach profile evolution on beaches with seawalls.
文摘There are numerous correlations and thermodynamic models for predicting the natural gas hydrate formation condition but still the lack of a simple and unifying general model that addresses a broad ranges of gas mixture.This study was aimed to develop a user-friendly universal correlation based on hybrid group method of data handling(GMDH)for prediction of hydrate formation temperature of a wide range of natural gas mixtures including sweet and sour gas.To establish the hybrid GMDH,the total experimental data of 343 were obtained from open articles.The selection of input variables was based on the hydrate structure formed by each gas species.The modeling resulted in a strong algorithm since the squared correlation coefficient(R2)and root mean square error(RMSE)were 0.9721 and 1.2152,respectively.In comparison to some conventional correlation,this model represented not only the outstanding statistical parameters but also its absolute superiority over others.In particular,the result was encouraging for sour gases concentrated at H2S to the extent that the model outstrips all available thermodynamic models and correlations.Leverage statistical approach was applied on datasets to the discovery of the defected and doubtful experimental data and suitability of the model.According to this algorithm,approximately all the data points were in the proper range of the model and the proposed hybrid GMDH model was statistically reliable.