Comparison between the artificial neural network, SAFT and PRSV approach in obtaining the solubility of solid aromatic compounds in supercritical carbon dioxide

Developing an accurate empirical correlation for predicting anti-cancer drugs’ dissolution in supercritical carbon dioxide

This study introduces a universal correlation based on the modified version of the Arrhenius equation to estimate the solubility of anti-cancer drugs in supercritical carbon dioxide (CO₂). A combination of an Arrhenius-shape term and a departure function was proposed to estimate the solubility of anti-cancer drugs in supercritical CO₂. This modified Arrhenius correlation predicts the solubility of anti-cancer drugs in supercritical CO₂ from pressure, temperature, and carbon dioxide density. The pre-exponential of the Arrhenius linearly relates to the temperature and carbon dioxide density, and its exponential term is an inverse function of pressure. Moreover, the departure function linearly correlates with the natural logarithm of the ratio of carbon dioxide density to the temperature. The reliability of the proposed correlation is validated using all literature data for solubility of anti-cancer drugs in supercritical CO₂. Furthermore, the predictive performance of the modified Arrhenius correlation is compared with ten available empirical correlations in the literature. Our developed correlation presents the absolute average relative deviation (AARD) of 9.54% for predicting 316 experimental measurements. On the other hand, the most accurate correlation in the literature presents the AARD = 14.90% over the same database. Indeed, 56.2% accuracy improvement in the solubility prediction of the anti-cancer drugs in supercritical CO₂ is the primary outcome of the current study.

Study on the Alteration of Pore Parameters of Shale with Different Natural Fractures under Supercritical Carbon Dioxide Seepage

Supercritical CO2 can reduce formation fracture pressure, form more complex fractures in the near-well zone, and replace methane to complete carbon sequestration, which is an important direction for the efficient development of deep shale gas with carbon sequestration. In this paper, based on the scCO2 fracturing field test parameters and the characteristics of common shale calcite filled natural fractures, we simulated the porosity change in shale with three kinds of fractures (no fracture, named NF; axial natural fracture, named AF; and transversal natural fracture, named TF) under scCO2 seepage, and carried out the experimental verification of shale under supercritical CO2 seepage. It was found that: (1) At the same pressure, when the temperature is greater than the critical temperature, the shale porosity of three kinds of fractures gradually increases with the injection of CO2, and the higher the temperature, the more obvious the increase in porosity. (2) At the same temperature and different pressures, the effect of pressure change on the porosity of shale specimens was more obvious than that of temperature. (3) Multi-field coupling experiments of shale under supercritical CO2 seepage revealed that the porosity of all three shale specimens at the same temperature and pressure increased after CO2 injection, and the relative increase in shale porosity measured experimentally was basically consistent with the numerical simulation results. This paper reveals the mechanism of the effect of different temperatures and pressures of scCO2 and different natural fractures on the change in shale porosity, which can be used to optimize the CO2 injection in supercritical CO2 fracturing and carbon sequestration.

Artificial neural network modelling for biodecolorization of Basic Violet 03 from aqueous solution by biochar derived from agro-bio waste of groundnut hull: Kinetics and thermodynamics

In this study, Levenberg Marquardt back propagation algorithm was used to train the Artificial Neural Network (ANN) and to predict the adsorptive removal of cationic dye Basic Violet 03 (BV03) by biochar derived from biowaste of groundnut hull. The experimental conditions such as solution pH, biochar dose, initial dye concentration, contact time and temperature were used as input variables and BV03 percentage removal as target. The hidden and the output layer of the network was trained by tangent sigmoid and liner transfer functions. The feasibility of the adsorption process is evaluated by the kinetic studies and it exhibited that pseudo-second order kinetic models fit well with experimental data. The adsorbent stability and adsorption mechanism has been discoursed by the thermodynamic characteristics and sorption free energy. The predicted target values were compared with the experiment resulted in a better correlation coefficient of 0.9920. Thus, the results attained from this ANN model was found to be effective in predicting the percentage removal of BV03 dye at any given operating condition.

Using static method to measure tolmetin solubility at different pressures and temperatures in supercritical carbon dioxide

Tolmetin is a non-steroidal anti-inflammatory drug being used to decrease the level of hormones which are the reasons for pain, swelling, tiredness, and stiffness for osteoarthritis and rheumatoid arthritis cases. We evaluated its solubility in supercritical carbon dioxide (SC-CO₂) with the aim of drug nanonization, considering temperature and pressure variations between 120 and 400 bar and 308-338 K, in the experiments. In this way, a PVT solubility cell based on static solubility approach coupled with a simple gravimetric procedure was utilized to evaluate the solubility of tolmetin. The solubility values between 5.00 × 10^-5 and 2.59 × 10^-3 mol fraction were obtained for tolmetin depending on the pressure and temperature of the cell. The measured data demonstrated a direct correlation between pressure and solubility of tolmetin, while the effect of temperature was a dual effect depending on the crossover pressure (160 bar). The calculated solubility data were modeled using several semi-empirical correlations, and the fitting parameters were calculated using the experimental data via appropriate optimization method. The correlated solubility data revealed that the KJ model was the most accurate one with an average absolute relative deviation percent (AARD%) of 6.9. Moreover, the carried out self-consistency analysis utilizing these correlations illustrated great potential of these models to extrapolate the solubility of tolmetin beyond the measured conditions.

Solubility prediction of gases in polymers based on an artificial neural network: a review

Solubility prediction model based on a hybrid artificial neural network.