Estimation of surface tension of methyl esters biodiesels using computational intelligence technique

Owolabi, Taoreed O.; Gondal, M.A.

doi:10.1016/j.asoc.2015.08.028

Cited by 12 publications

(3 citation statements)

References 22 publications

(25 reference statements)

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“…[17] It has received great attention and wide applications in material sciences, [18][19][20][21] condensed matter physics, [22,23] etc. [24,25] The unique qualities of SVR include non-convergence to local minimal, excellent performance, and high stability. [26][27][28] Since the predictive and generalization capacity of SVR depends heavily on the proper selection of its hyper-parameters, this proposed model implements the gravitational search algorithm (GSA) developed recently for the optimal selection of SVR hyper-parameters.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Intelligent Modelling of the Viscoelastic Moduli of Coal Fly Ash Based Polymer Gel System for Water Shutoff Treatment in Oil and Gas Wells

et al. 2019

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Qualitative polymer gels exhibiting considerable viscoelastic properties are of immense significance in the petroleum industry as they curtail excessive water production during the tertiary stage of crude oil production. The incorporation of suitable organic or inorganic solid substances into the polymer gel system strengthens its viscoelastic properties. This research work presents hybrid gravitational search algorithm (GSA) and support vector regression (SVR) as novel modelling tools for viscoelastic properties estimation of polymer gel synthesized from polyacrylamide (PAM)/polyethyleneimine (PEI). The coal fly ash (CFA) was incorporated in PAM/PEI gel in order to enhance its viscoelastic characteristics. The hybrid GSA‐SVR models were developed using real‐life experimental data. The results of the models validated using unseen data show a high degree of correlation coefficients (87.3 % for elastic modulus and 97.2 % for viscous modulus), with experimental data generating a small root mean square error (coefficients 669.5Pa for elastic modulus and 38.5Pa for viscous modulus). The developed hybrid models were further used to scrutinize the effect of the addition of various amounts of CFA on the PAM/PEI viscoelastic properties and observed behaviours were presented and discussed. The predictive capability of the developed hybrid model coupled with its strength in modelling the impact of CFA in the PAM/PEI gel are highly meritorious and the outcomes from this investigation would be relevant during the design and formulation of polymer gels employed for water shutoff treatment during hydrocarbon recovery.

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Section: Introductionmentioning

confidence: 99%

“…Support vector regression (SVR) is an efficient computational learning tool, controlled by statistical theory . It has received great attention and wide applications in material sciences, condensed matter physics, etc . The unique qualities of SVR include non‐convergence to local minimal, excellent performance, and high stability .…”

Section: Introductionmentioning

confidence: 99%

Hybrid Intelligent Modelling of the Viscoelastic Moduli of Coal Fly Ash Based Polymer Gel System for Water Shutoff Treatment in Oil and Gas Wells

et al. 2019

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“…It is built on sound mathematical foundation and does not converge to local minima. It has enjoyed a wide range of applications in material sciences [20][21][22][23][24][25], medicine [26,27] and other areas of study [28,29]. Its hybridization proposed in this present work involves combination of two SVR in which one of it is trained and tested using molecular weight and number of carbon to carbon double bound as the descriptors, while the other SVR is developed using the estimated melting points of the first one.…”

Section: Introductionmentioning

confidence: 97%

Estimation of melting points of fatty acids using homogeneously hybridized support vector regression

Owolabi

Zakariya

Olatunji

et al. 2016

Neural Comput & Applic

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This work develops a hybridized support vector regression (HSVR)-based model for accurate estimation of melting points of fatty acids using their molecular weights and the number of carbon-carbon double bond as descriptors. The development of HSVR-based model is characterized with two stages. The first stage involves training and testing SVR using test-set-cross validation technique with molecular weights and the number of carbon-carbon double bond as descriptors, while the second stage utilizes the estimated melting points obtained from the first stage as descriptor for further training and testing of SVR. The proposed hybrid system therefore demonstrates a better predictive and generalization ability than ordinary SVR. Furthermore, the melting points of sixty-two fatty acids estimated using the proposed HSVR-based model show persistence closeness with the experimental values than the results of other existing predictive models for fatty acids melting points estimation such as Guijie et al. model and Guendouzi model. The developed HSVRbased model is also characterized with higher value of coefficient of correlation and lower value of mean absolute error than that of the existing predictive models. Superiority of the developed HSVR-based model over the existing predictive models in terms of the ease of obtaining its descriptors and the accuracy of its estimates is advantageous to unravel estimation challenges associated with determination of fatty acids melting points.

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Latest and Future Prospects of Bismith Oxyhalides

Gondal

Chang

Dastageer

2017

Advanced Structured Materials

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Estimation of surface tension of methyl esters biodiesels using computational intelligence technique

Cited by 12 publications

References 22 publications

Hybrid Intelligent Modelling of the Viscoelastic Moduli of Coal Fly Ash Based Polymer Gel System for Water Shutoff Treatment in Oil and Gas Wells

Hybrid Intelligent Modelling of the Viscoelastic Moduli of Coal Fly Ash Based Polymer Gel System for Water Shutoff Treatment in Oil and Gas Wells

Estimation of melting points of fatty acids using homogeneously hybridized support vector regression

Latest and Future Prospects of Bismith Oxyhalides

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