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. 2022 Sep 6;27(18):5762.
doi: 10.3390/molecules27185762.

Application of CO2 Supercritical Fluid to Optimize the Solubility of Oxaprozin: Development of Novel Machine Learning Predictive Models

Saad M Alshahrani  1 , Ahmed Al Saqr  1 , Munerah M Alfadhel  1 , Abdullah S Alshetaili  1 , Bjad K Almutairy  1 , Amal M Alsubaiyel  2 , Ali H Almari  3 , Jawaher Abdullah Alamoudi  4 , Mohammed A S Abourehab  5   6
Affiliations

Application of CO2 Supercritical Fluid to Optimize the Solubility of Oxaprozin: Development of Novel Machine Learning Predictive Models

Saad M Alshahrani et al. Molecules. .

Abstract

Over the last years, extensive motivation has emerged towards the application of supercritical carbon dioxide (SCCO2) for particle engineering. SCCO2 has great potential for application as a green and eco-friendly technique to reach small crystalline particles with narrow particle size distribution. In this paper, an artificial intelligence (AI) method has been used as an efficient and versatile tool to predict and consequently optimize the solubility of oxaprozin in SCCO2 systems. Three learning methods, including multi-layer perceptron (MLP), Kriging or Gaussian process regression (GPR), and k-nearest neighbors (KNN) are selected to make models on the tiny dataset. The dataset includes 32 data points with two input parameters (temperature and pressure) and one output (solubility). The optimized models were tested with standard metrics. MLP, GPR, and KNN have error rates of 2.079 × 10-8, 2.173 × 10-9, and 1.372 × 10-8, respectively, using MSE metrics. Additionally, in terms of R-squared, they have scores of 0.868, 0.997, and 0.999, respectively. The optimal inputs are the same as the maximum possible values and are paired with a solubility of 1.26 × 10-3 as an output.

Keywords: green chemistry; machine learning; mathematical modeling; optimization; solubility.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Pairwise Distribution of variables.
Figure 2
Figure 2
Actual Vs. Predicted Solubility (mole fraction) (MLP).
Figure 3
Figure 3
Real Vs. Forestalled Solubility (mole fraction) (GPR).
Figure 4
Figure 4
Real Vs. Forestalled Solubility (mole fraction) (KNN).
Figure 5
Figure 5
3D projection with GPR Model (pressure, bar/temperature, K/solubility, mole fraction).
Figure 6
Figure 6
Trends for Temperature (K).
Figure 7
Figure 7
Trends for Pressure (bar).
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