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Erschienen in: Neural Computing and Applications 3-4/2014

01.09.2014 | Original Article

Rigorous modeling of gypsum solubility in Na–Ca–Mg–Fe–Al–H–Cl–H2O system at elevated temperatures

verfasst von: Hossein Safari, Farhad Gharagheizi, Alireza Samadi Lemraski, Mohammad Jamialahmadi, Amir H. Mohammadi, Milad Ebrahimi

Erschienen in: Neural Computing and Applications | Ausgabe 3-4/2014

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Abstract

Precipitation and scaling of calcium sulfate have been known as major problems facing process industries and oilfield operations. Most scale prediction models are based on aqueous thermodynamics and solubility behavior of salts in aqueous electrolyte solutions. There is yet a huge interest in developing reliable, simple, and accurate solubility prediction models. In this study, a comprehensive model based on least-squares support vector machine (LS-SVM) is presented, which is mainly devoted to calcium sulfate dihydrate (or gypsum) solubility in aqueous solutions of mixed electrolytes covering wide temperature ranges. In this respect, an aggregate of 880 experimental data were gathered from the open literature in order to construct and evaluate the reliability of presented model. Solubility values predicted by LS-SVM model are in well accordance with the observed values yielding a squared correlation coefficient (R 2) of 0.994. Sensitivity of the model for some important parameters is also checked to ascertain whether the learning process has succeeded. At the end, outlier diagnosis was performed using the method of leverage value statistics to find and eliminate the falsely recorded measurements from assembled dataset. Results obtained from this study indicate that LS-SVM model can successfully be applied in predicting accurate solubility of calcium sulfate dihydrate in Na–Ca–Mg–Fe–Al–H–Cl–H2O system over temperatures ranging from 283.15 to 371.15 K.

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Metadaten
Titel
Rigorous modeling of gypsum solubility in Na–Ca–Mg–Fe–Al–H–Cl–H2O system at elevated temperatures
verfasst von
Hossein Safari
Farhad Gharagheizi
Alireza Samadi Lemraski
Mohammad Jamialahmadi
Amir H. Mohammadi
Milad Ebrahimi
Publikationsdatum
01.09.2014
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 3-4/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-014-1587-z

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