The Experts below are selected from a list of 11385 Experts worldwide ranked by ideXlab platform
Marjan Adib - One of the best experts on this subject based on the ideXlab platform.
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Toward an intelligent approach for H2S content and vapor pressure of sour condensate of south pars Natural Gas Processing plant
Journal of Natural Gas Science and Engineering, 2016Co-Authors: Nooshin Moradi Kazerooni, Hooman Adib, Askar Sabet, Mohammad Amin Adhami, Marjan AdibAbstract:In this study, artificial neural network is employed to develop a model to predict process output variables of an industrial condensate stabilization plant. The developed model is evaluated by process operating data of south pars Natural Gas Processing plant located Asaluyeh/Iran. A large dataset of 4 variables consisting of temperature and pressure of the stabilization column in addition to Ried Vapor Pressure (RVP) and H2S content of the processed condensate is utilized to train the network. In order to determine the optimized topology and decision parameters of the network, the values of Mean Square Error (MSE), Mean Absolute Error (MAE) and the coefficient of determination (R2) are minimized by the method of trial and error. Since precision of ANN model is dependent on the amount of training data used, the extensive set of samples applied in this work can offer accurate reliable predictions. Model output is compared to actual data of the plant and the values of Average Absolute Deviation percent (ADD%) are reported as 1.6 for RVP and 3.8 for H2S concentration.
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Evolving a prediction model based on machine learning approach for hydrogen sulfide removal from sour condensate of south pars Natural Gas Processing plant
Journal of Natural Gas Science and Engineering, 2015Co-Authors: Hooman Adib, Askar Sabet, Marjan Adib, Abbas Naderifar, Massoud EbrahimzadehAbstract:In present study Support Vector Machine (SVM) is employed to develop a model to estimate process output variables of stabilizer column of an industrial Natural Gas sweetening plant. The developed model is evaluated by process operating data of south pars Natural Gas Processing plant in Asalouyeh/Iran. A set of 6 input/output plant data each consisting of 660 data has been used to train, optimize, and test the model. Model development that consists of training, optimization and test was performed using randomly selected 80%, 10%, and 10% of available data respectively. Test results from the SVM based model showed to be in better agreement with operating plant data. The minimum calculated squared correlation coefficient for estimated process variables are 0.97 for H2S concentration and 0.94 for Reid vapor pressure (RVP). Based on the results of this case study SVM proved that it can be a reliable accurate estimation method.
Hooman Adib - One of the best experts on this subject based on the ideXlab platform.
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Toward an intelligent approach for H2S content and vapor pressure of sour condensate of south pars Natural Gas Processing plant
Journal of Natural Gas Science and Engineering, 2016Co-Authors: Nooshin Moradi Kazerooni, Hooman Adib, Askar Sabet, Mohammad Amin Adhami, Marjan AdibAbstract:In this study, artificial neural network is employed to develop a model to predict process output variables of an industrial condensate stabilization plant. The developed model is evaluated by process operating data of south pars Natural Gas Processing plant located Asaluyeh/Iran. A large dataset of 4 variables consisting of temperature and pressure of the stabilization column in addition to Ried Vapor Pressure (RVP) and H2S content of the processed condensate is utilized to train the network. In order to determine the optimized topology and decision parameters of the network, the values of Mean Square Error (MSE), Mean Absolute Error (MAE) and the coefficient of determination (R2) are minimized by the method of trial and error. Since precision of ANN model is dependent on the amount of training data used, the extensive set of samples applied in this work can offer accurate reliable predictions. Model output is compared to actual data of the plant and the values of Average Absolute Deviation percent (ADD%) are reported as 1.6 for RVP and 3.8 for H2S concentration.
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Evolving a prediction model based on machine learning approach for hydrogen sulfide removal from sour condensate of south pars Natural Gas Processing plant
Journal of Natural Gas Science and Engineering, 2015Co-Authors: Hooman Adib, Askar Sabet, Marjan Adib, Abbas Naderifar, Massoud EbrahimzadehAbstract:In present study Support Vector Machine (SVM) is employed to develop a model to estimate process output variables of stabilizer column of an industrial Natural Gas sweetening plant. The developed model is evaluated by process operating data of south pars Natural Gas Processing plant in Asalouyeh/Iran. A set of 6 input/output plant data each consisting of 660 data has been used to train, optimize, and test the model. Model development that consists of training, optimization and test was performed using randomly selected 80%, 10%, and 10% of available data respectively. Test results from the SVM based model showed to be in better agreement with operating plant data. The minimum calculated squared correlation coefficient for estimated process variables are 0.97 for H2S concentration and 0.94 for Reid vapor pressure (RVP). Based on the results of this case study SVM proved that it can be a reliable accurate estimation method.
Askar Sabet - One of the best experts on this subject based on the ideXlab platform.
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Toward an intelligent approach for H2S content and vapor pressure of sour condensate of south pars Natural Gas Processing plant
Journal of Natural Gas Science and Engineering, 2016Co-Authors: Nooshin Moradi Kazerooni, Hooman Adib, Askar Sabet, Mohammad Amin Adhami, Marjan AdibAbstract:In this study, artificial neural network is employed to develop a model to predict process output variables of an industrial condensate stabilization plant. The developed model is evaluated by process operating data of south pars Natural Gas Processing plant located Asaluyeh/Iran. A large dataset of 4 variables consisting of temperature and pressure of the stabilization column in addition to Ried Vapor Pressure (RVP) and H2S content of the processed condensate is utilized to train the network. In order to determine the optimized topology and decision parameters of the network, the values of Mean Square Error (MSE), Mean Absolute Error (MAE) and the coefficient of determination (R2) are minimized by the method of trial and error. Since precision of ANN model is dependent on the amount of training data used, the extensive set of samples applied in this work can offer accurate reliable predictions. Model output is compared to actual data of the plant and the values of Average Absolute Deviation percent (ADD%) are reported as 1.6 for RVP and 3.8 for H2S concentration.
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Evolving a prediction model based on machine learning approach for hydrogen sulfide removal from sour condensate of south pars Natural Gas Processing plant
Journal of Natural Gas Science and Engineering, 2015Co-Authors: Hooman Adib, Askar Sabet, Marjan Adib, Abbas Naderifar, Massoud EbrahimzadehAbstract:In present study Support Vector Machine (SVM) is employed to develop a model to estimate process output variables of stabilizer column of an industrial Natural Gas sweetening plant. The developed model is evaluated by process operating data of south pars Natural Gas Processing plant in Asalouyeh/Iran. A set of 6 input/output plant data each consisting of 660 data has been used to train, optimize, and test the model. Model development that consists of training, optimization and test was performed using randomly selected 80%, 10%, and 10% of available data respectively. Test results from the SVM based model showed to be in better agreement with operating plant data. The minimum calculated squared correlation coefficient for estimated process variables are 0.97 for H2S concentration and 0.94 for Reid vapor pressure (RVP). Based on the results of this case study SVM proved that it can be a reliable accurate estimation method.
Massoud Ebrahimzadeh - One of the best experts on this subject based on the ideXlab platform.
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Evolving a prediction model based on machine learning approach for hydrogen sulfide removal from sour condensate of south pars Natural Gas Processing plant
Journal of Natural Gas Science and Engineering, 2015Co-Authors: Hooman Adib, Askar Sabet, Marjan Adib, Abbas Naderifar, Massoud EbrahimzadehAbstract:In present study Support Vector Machine (SVM) is employed to develop a model to estimate process output variables of stabilizer column of an industrial Natural Gas sweetening plant. The developed model is evaluated by process operating data of south pars Natural Gas Processing plant in Asalouyeh/Iran. A set of 6 input/output plant data each consisting of 660 data has been used to train, optimize, and test the model. Model development that consists of training, optimization and test was performed using randomly selected 80%, 10%, and 10% of available data respectively. Test results from the SVM based model showed to be in better agreement with operating plant data. The minimum calculated squared correlation coefficient for estimated process variables are 0.97 for H2S concentration and 0.94 for Reid vapor pressure (RVP). Based on the results of this case study SVM proved that it can be a reliable accurate estimation method.
Jiang Bian - One of the best experts on this subject based on the ideXlab platform.
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supersonic separation technology for Natural Gas Processing a review
Chemical Engineering and Processing, 2019Co-Authors: Xuewen Cao, Jiang BianAbstract:Abstract Supersonic separation technology is a new approach to condense and separate water, heavy hydrocarbons and other impurities from Natural Gas. This paper reviews the research advances in condensation characteristics of the Laval nozzle and separation mechanism of the supersonic separator in detail from the perspectives of theoretical analysis, experiments and numerical simulation, and summarizes the new application of this technology including Natural Gas liquefaction and removal of the acid Gases. This review points out that while several aspects of this technology have been well studied, there still exist several issues in the practice. The further research topics are clarified to promote future applications such as the model modification of Gas spontaneous nucleation rate to improve the prediction accuracy, and the combination the molecular dynamics technology with interface mechanics theory to study the collision and coalescence of the droplets and the interaction between Gas and droplets under supersonic conditions.
