Prediction of the properties of brines using least squares support vector machine (LS-SVM) computational strategy

Arabloo, Milad; Ziaee, Hossein; Lee, Moonyong; Bahadori, Alireza

doi:10.1016/j.jtice.2014.12.005

Cited by 40 publications

(9 citation statements)

References 29 publications

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“…The SVM is a supervised learning technique from the field of machine learning that is applicable to both classification and regression [4,5]. The SVM is a relatively recent approach introduced by Vapnik [6] and Burges [7] to solve supervised classification and regression problems, or more colloquially, learning from examples.…”

Section: Greek Letters α Imentioning

confidence: 99%

Support vector machines for modelling phosphocalcic hydroxyapatite by precipitation from a calcium carbonate solution and phosphoric acid solution

Labjar¹,

Cherif

Digua³

et al. 2016

Journal of Taibah University for Science

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The aim of this work was to develop an effective quantitative relationship model using support vector machines for the synthesis of phosphocalcic hydroxyapatite by precipitation from a calcium carbonate solution and a phosphoric acid solution. The model was created using a set of factors consisting of the pH of the reaction solution, the Ca/P molar ratio of the reagents, the reaction time and the initial concentration of calcium. Previous studies modelled these relationships using different classification techniques. In this paper, a novel approach based on support vector machines (SVMs) is introduced. This latter approach yields the best result compared to polynomial regression (PR), linear regression (LR) and artificial neural networks (ANN). In addition, the contribution of each descriptor is evaluated. Thus, the proposed method can be successfully used to predict the Ca/P analysis directly from the considered factors.

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Section: Greek Letters α Imentioning

confidence: 99%

Support vector machines for modelling phosphocalcic hydroxyapatite by precipitation from a calcium carbonate solution and phosphoric acid solution

Labjar¹,

Cherif

Digua³

et al. 2016

Journal of Taibah University for Science

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“…The attraction of soft computing techniques is that they are best suited to solve various difficult engineering challenges that are hard to solve by traditional computational approaches . During the past decades some efforts have been made to the applications of artificial intelligence in academic study or industry . In this article first, two accurate and simple correlations based on statistical analysis will be presented so as to suitably calculate the CO 2 adsorption isotherms for molecular sieves and activated carbon under wide ranges of temperatures and pressure.…”

Section: Introductionmentioning

confidence: 99%

Estimation of carbon dioxide equilibrium adsorption isotherms using adaptive neuro‐fuzzy inference systems (ANFIS) and regression models

Saghafi

Arabloo

2017

Env Prog and Sustain Energy

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Removal of CO 2 from industrial facilities such as refineries and power plants for emission reduction has attracted considerable interest. Among currently used CO 2 capturing processes, the use of microporous solids is considered to be one of the most promising approaches. A description of the CO 2 adsorption on microporous material focuses on some captivating problems of present adsorption studies. In present study, robust and accurate methods are designed for the estimation of CO 2 adsorption onto microporous solids as a function of CO 2 partial pressure and temperature. The performance and accuracy of the developed models were tested and validated by their ability to predict the literature data. It was concluded that the designed adaptive neurofuzzy inference systems (ANFIS) models and mathematical equations demonstrated a superior predictive performance on estimation of CO 2 adsorption data over well-known classic adsorption isotherms. Findings of present study indicate that the neuro-fuzzy modeling approach is a practicable method for analysis and design of CO 2 separation and purification technology.

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“…It can be used for determining the degree of linear correlation of parameters in a regression calculation and a higher value implies more reliable prediction of the model. 30,55,56 AAD is the average absolute deviation from a middle point and is considered a direct way to measure deviations between predicted and obtained results and, in contrast with R 2 , a smaller value is better. 57 Mean square error (MSE) and RMSE are other statistics to check the quality of a model which are positive values and are preferred to be smaller and closer to zero.…”

Section: Comparison Brt and Rsmmentioning

confidence: 99%

Ultrasound-assisted sorption of Pb(ii) on multi-walled carbon nanotube in presence of natural organic matter: an insight into main and interaction effects using modelling approaches of RSM and BRT

et al. 2019

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Prediction of the properties of brines using least squares support vector machine (LS-SVM) computational strategy

Cited by 40 publications

References 29 publications

Support vector machines for modelling phosphocalcic hydroxyapatite by precipitation from a calcium carbonate solution and phosphoric acid solution

Support vector machines for modelling phosphocalcic hydroxyapatite by precipitation from a calcium carbonate solution and phosphoric acid solution

Estimation of carbon dioxide equilibrium adsorption isotherms using adaptive neuro‐fuzzy inference systems (ANFIS) and regression models

Ultrasound-assisted sorption of Pb(ii) on multi-walled carbon nanotube in presence of natural organic matter: an insight into main and interaction effects using modelling approaches of RSM and BRT

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