Development of robust model to estimate gas–oil interfacial tension using least square support vector machine: Experimental and modeling study

Ahmadi, Mohammad Ali; Mahmoudi, Behnam

doi:10.1016/j.supflu.2015.08.012

Cited by 55 publications

(27 citation statements)

References 31 publications

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“…Rew and GDP were measured as million tons oil equivalent and in billion 2005 U.S. dollars, respectively. Overall, the LSSVM nonlinear function can be demonstrated as follows [54][55][56][57][58][59]:…”

Section: Least Squares Support Vector Machinementioning

confidence: 99%

“…Input variables were oil (million tons), gas (million tons oil equivalent), coal (million tons oil equivalent), R ew (million tons oil equivalent) and GDP (billion 2005 U.S. dollars). w represents the weight vector (m-dimensional); φ maps x into the characteristic vector (m-dimensional); and b states the bias [54][55][56].…”

Section: Least Squares Support Vector Machinementioning

confidence: 99%

“…Employing the Lagrange multipliers α i in order to change the previous limited problem to an unlimited problem is a strong and efficient tool to determine the optimization problems that are stated in Equation 2 [55][56][57][58][59]:…”

Section: Least Squares Support Vector Machinementioning

confidence: 99%

“…As stated in the investigation of Karush-Kuhn-Tucker (KKT), considering the stated variables including w, b, e and α and also performing the partial derivatives of Equation (4), the optimum condition can be expressed as follows [55][56][57][58][59]:…”

Section: Least Squares Support Vector Machinementioning

confidence: 99%

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Current Status Investigation and Predicting Carbon Dioxide Emission in Latin American Countries by Connectionist Models

et al. 2019

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Currently, one of the biggest concerns of human beings is greenhouse gas emissions, especially carbon dioxide emissions in developed and under-developed countries. In this study, connectionist models including LSSVM (Least Square Support Vector Machine) and evolutionary methods are employed for predicting the amount of CO 2 emission in six Latin American countries, i.e., Brazil, Mexico, Argentina, Peru, Chile, Venezuela and Uruguay. The studied region is modelled based on the available input data in terms of million tons including oil (million tons), gas (million tons oil equivalent), coal (million tons oil equivalent), R e w (million tons oil equivalent) and Gross Domestic Product (GDP) in terms of billion U.S. dollars. Moreover, the available patents in the field of climate change mitigation in six Latin American countries, namely Brazil, Mexico, Argentina, Peru, Chile, Venezuela and Uruguay, have been reviewed and analysed. The results show that except Venezuela, all other mentioned countries have invested in renewable energy R&D activities. Brazil and Argentina have the highest share of renewable energies, which account for 60% and 72%, respectively.

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Section: Least Squares Support Vector Machinementioning

confidence: 99%

Section: Least Squares Support Vector Machinementioning

confidence: 99%

Section: Least Squares Support Vector Machinementioning

confidence: 99%

Section: Least Squares Support Vector Machinementioning

confidence: 99%

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Current Status Investigation and Predicting Carbon Dioxide Emission in Latin American Countries by Connectionist Models

et al. 2019

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“…The defects of these models are that prediction accuracy is not good enough and prediction time is too long. While least squares support vector machine (LSSVM) overcomes these disadvantages [5,6] . In this paper, least squares support vector machine is used to establish the temperature prediction model of cement rotary kiln, with multi population genetic algorithm (MPGA) optimizing its parameters.…”

Section: Introductionmentioning

confidence: 99%

Temperature Prediction Model of Cement Rotary Kiln Based on MPGA-LSSVM

Zhao¹,

Wang²,

Wang³

et al. 2016

Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)

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Abstract. The burning zone temperature (BZT) in cement rotary kiln is a mostly important index which affects the quality of produced cement. In rotary kiln, the bilateral movements of air and complex chemical reactions take place. Thus, traditional modeling methods can not predict the BZT effectively. In order to improve the prediction accuracy, the temperature prediction model was established by least square support vector machine (LSSVM). The input variables of this model were selected by the gray correlation analysis method and mechanism analysis. The parameters of the LSSVM were optimized by multi population genetic algorithm (MPGA). The final simulation results show that the prediction accuracy is improved obviously. The effectiveness of the proposed method is verified.

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Novel machine learning approach for classification of high-dimensional microarray data

2019

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Development of robust model to estimate gas–oil interfacial tension using least square support vector machine: Experimental and modeling study

Cited by 55 publications

References 31 publications

Current Status Investigation and Predicting Carbon Dioxide Emission in Latin American Countries by Connectionist Models

Current Status Investigation and Predicting Carbon Dioxide Emission in Latin American Countries by Connectionist Models

Temperature Prediction Model of Cement Rotary Kiln Based on MPGA-LSSVM

Novel machine learning approach for classification of high-dimensional microarray data

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