Utilization of support vector machine to calculate gas compressibility factor

Chamkalani, Ali; Zendehboudi, Sohrab; Chamkalani, Reza; Lohi, Ali; Elkamel, Ali; Chatzis, Ioannis

doi:10.1016/j.fluid.2013.08.018

Cited by 51 publications

(13 citation statements)

References 47 publications

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“…[10][11][12][13][14][15][16][17][18], or molecular simulations, for modeling the fluid's viscosity. The later models are out of the scope of the present study.…”

Section: Introductionmentioning

confidence: 99%

Modeling viscosity of alcohols based on the CPA-EoS + ƒ-theory

Habibi

Hekmat-Nazemi

Kamran-Pirzaman

et al. 2016

Journal of Molecular Liquids

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“…[10][11][12][13][14][15][16][17][18], or molecular simulations, for modeling the fluid's viscosity. The later models are out of the scope of the present study.…”

Section: Introductionmentioning

confidence: 99%

Modeling viscosity of alcohols based on the CPA-EoS + ƒ-theory

Habibi

Hekmat-Nazemi

Kamran-Pirzaman

et al. 2016

Journal of Molecular Liquids

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“…Compressibility factor ( -factor) [1][2][3] is an important parameter of thermal physical properties and is often used to calculate other physical parameters. -factor changes with temperature and pressure.…”

Section: Introductionmentioning

confidence: 99%

Measurement and Prediction Method of Compressibility Factor at High Temperature and High Pressure

Zhu

Han

2016

Mathematical Problems in Engineering

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In order to get the compressibility factorZof working fluid under different conditions, experimental measurement method ofZunder high pressure and high temperature and data mining method were studied in this paper. Experimental measurement method based on real gas state equation and prediction method based on Least Squares Support Vector Machine were proposed. First, an experimental method for measuringZat high temperature and high pressure was designed; in this method the temperature, pressure, and density (mass and volume) of corresponding state were measured and substituted into the actual gas equation of state, and thenZcan be calculated. Meanwhile, in order to obtain continuous value inT-pplane, Squares Support Vector Machines are introduced to establish the prediction model ofZ. Take Hexamethyldisiloxane, for example; the experimental data ofZwas obtained using the experimental method. Meanwhile the prediction model ofZ, which can be used as calculation function ofZ, was established based on those experimental data, and theZ(T: 500 K~800 K,p: 1.3 MPa~2.25 MPa) was calculated by using this calculation function. By comparison with this published data, it was found that the average relative error was 2.14%.

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“…The artificial neural network (ANN) was determined to be a successful tool to predict the interfacial tension at the crystal/solution interface [1]. Chamkalani et al [2] showed the superiority of the least square support vector machine in predicting the compressibility factor in the petroleum industry. ANN optimized with Particle Swarm Optimization algorithm was found to be the best model in predicting Recovery Factor and Cumulative Steam to Oil Ration during steamflooding [3].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Hoabinh Reservoir’s Incoming Flow: An Application of Neural Networks with the Cuckoo Search Algorithm

2014

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The accuracy of reservoir flow forecasting has the most significant influence on the assurance of stability and annual operations of hydro-constructions. For instance, accurate forecasting on the ebb and flow of Vietnam's Hoabinh Reservoir can aid in the preparation and prevention of lowland flooding and drought, as well as regulating electric energy. This raises the need to propose a model that accurately forecasts the incoming flow of the Hoabinh Reservoir. In this study, a solution to this problem based on neural network with the Cuckoo Search (CS) algorithm is presented. In particular, we used hydrographic data and predicted total incoming flows of the Hoabinh Reservoir over a period of 10 days. The Cuckoo Search algorithm was utilized to train the feedforward neural network (FNN) for prediction. The algorithm optimized the weights between layers and biases of the neuron network. Different forecasting models for the three scenarios were developed. The constructed models have shown high forecasting performance based on the performance indices calculated. These results were also compared with those obtained from the neural networks trained by the particle swarm optimization (PSO) and back-propagation (BP), indicating that the proposed approach performed more effectively. Based on the experimental results, the scenario using the rainfall and the flow as input yielded the highest forecasting accuracy when compared with other scenarios. The performance

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Utilization of support vector machine to calculate gas compressibility factor

Cited by 51 publications

References 47 publications

Modeling viscosity of alcohols based on the CPA-EoS + ƒ-theory

Modeling viscosity of alcohols based on the CPA-EoS + ƒ-theory

Measurement and Prediction Method of Compressibility Factor at High Temperature and High Pressure

Forecasting Hoabinh Reservoir’s Incoming Flow: An Application of Neural Networks with the Cuckoo Search Algorithm

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