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Statistical modeling of aspirin solubility in organic solvents by Response Surface Methodology and Artificial Neural Networks

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  • Rostamian, Hossein
  • Lotfollahi, Mohammad Nader

Abstract

The present work is aiming at statistical modeling and prediction of solubility of aspirin based on two intelligent methods including Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). To develop the models, a data bank including 109 data belonging to the solubility of aspirin in ethanol, acetone, 2-propanol, 1-octanol, ethyl acetate, isobutanol, isobutyl acetate, 1-butanol, MIBK and propylene glycol as organic solvents was extracted from the literature. Temperature, molecular weight of the solvents, critical pressure and temperature and acentric factor were chosen as independent variables for the modeling. Both RSM and ANN models were statistically compared using coefficient of determination (R2), Root Mean Square Error (RMSE), Average Absolute Deviation (AAD%) and Sum of Absolute Residual (SAR) obtained for the data set. R2 and A.A.D% were determined as 0.9992 and 2.598% for ANN, and 0.997 and 3.884% for RSM model, respectively. It was identified that both developed model can accurately predict the solubility of aspirin in different organic solvents, however, ANN was more accurate due to its topology and structure, which promotes the accuracy of the model. The correlation was also verified with seven more experiments. It was found that the proposed statistical RSM model is able to obtain the solubility of aspirin in various organic solvents using extrapolation and/or interpolation feature.

Suggested Citation

  • Rostamian, Hossein & Lotfollahi, Mohammad Nader, 2020. "Statistical modeling of aspirin solubility in organic solvents by Response Surface Methodology and Artificial Neural Networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    • Handle: RePEc:eee:phsmap:v:540:y:2020:i:c:s0378437119318266
    DOI: 10.1016/j.physa.2019.123253
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    References listed on IDEAS

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    1. Phillips, J.C., 2014. "Fractals and self-organized criticality in anti-inflammatory drugs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 538-543.
    2. Hemmat Esfe, Mohammad & Kamyab, Mohammad Hassan & Afrand, Masoud & Amiri, Mahmoud Kiannejad, 2018. "Using artificial neural network for investigating of concurrent effects of multi-walled carbon nanotubes and alumina nanoparticles on the viscosity of 10W-40 engine oil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 610-624.
    3. Rostamian, Hossein & Lotfollahi, Mohammad Nader, 2019. "A novel statistical approach for prediction of thermal conductivity of CO2 by Response Surface Methodology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    4. Hemmat Esfe, Mohammad & Reiszadeh, Mahdi & Esfandeh, Saeed & Afrand, Masoud, 2018. "Optimization of MWCNTs (10%) – Al2O3 (90%)/5W50 nanofluid viscosity using experimental data and artificial neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 731-744.
    5. Zahedi, Javad & Rounaghi, Mohammad Mahdi, 2015. "Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 178-187.
    6. Hemmat Esfe, Mohammad & Rostamian, Hossein & Esfandeh, Saeed & Afrand, Masoud, 2018. "Modeling and prediction of rheological behavior of Al2O3-MWCNT/5W50 hybrid nano-lubricant by artificial neural network using experimental data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 625-634.
    7. Guo, Tongxiao & Bian, Xiufang & Yang, Chuncheng, 2015. "A new method to prepare water based Fe3O4 ferrofluid with high stabilization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 560-567.
    8. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
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