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The current study aims at modeling the solubility of anti-cancer agents in supercritical carbon dioxide (SC-CO2). An extensive databank, including 893 measured samples for 33 anti-cancer agents were collected from the literature, covering extensive ranges of operating conditions. Eight density-based empirical models were firstly employed to correlate the collected data. After adjusting their constant coefficients, four of them provided satisfactory estimations, with total average absolute relative errors (AAREs) below 10 %. A novel six-parameter empirical correlation was also proposed, with input factors optimized based on the Pearson coefficient analysis. This correlation produced satisfactory results for the analyzed drugs, achieving a total AARE of 7.71 %. Afterward, a generalized and unified model was built using the intelligent method of gaussian process regression (GPR). For the testing data, this model showed excellent results with AARE and R2 values of 2.90 % and 99.87 %, respectively. Furthermore, its estimations for all anti-cancer agents outperformed the empirical correlations significantly. Both empirical and intelligent models accurately described the physical behavior of anti-cancer agents’ solubility in SC-CO2 under various conditions. Subsequently, the most effective factors on the performances of the models were recognized through a sensitivity analysis.

In this study, for the first time a magnetically recoverable zeolitic imidazolate framework-8 (ZIF-8) / graphite carbon nitride (g-C3N4)/ cooper ferrite (CuFe2O4) nanocomposite was prepared through a fast and simple wet impregnation procedure. The magnetic behavior of the ZIF-8/g-C3N4/CuFe2O4 heterostructure was used for the easy recovery of the nanocomposite by an external magnet. After studying the structural, crystallinity, magnetic, and photophysical properties of the as-syntesized sample, its application was investigated for the enhanced photodegradation of tetracycline under visible light illumination. The band gap energies for ZIF-8, g-C3N4, and CuFe2O4 were obtained 5.1 eV, 2.8 eV, and 1.3 eV, respectively. After combination of ZIF-8, g-C3N4, and CuFe2O4, the band gap energy was obtained to 2.4 eV, which indicate the improvement of photocatalytic activity of ZIF-8/g-C3N4/CuFe2O4 nanocomposite. Among the as-synthesized photocatalyst samples, the ZIF-8/g-C3N4/CuFe2O4 showed an excellent photocatalytic activity (99.15 %) for degradation of tetracycline within 65 min irradiation of visible light, solution pH of 5 and photocatalyst dosage of 0.75 g/L. The rate constant of the ZIF-8/g-C3N4/CuFe2O4 is almost 13.1, 9.9, and 15.6 times higher than that of ZIF-8, g-C3N4, and CuFe2O4, respectively. The ZIF-8/g-C3N4/CuFe2O4 heterojunction showed the effective degradation activity for four consecutive cycles, which exhibited its excellent reusability and stability. Moreover, the free radical quenching tests confirmed that holes, and hydroxyl radicals were the main reaction species for degradation of tetracycline. Altogether the preparation of novel ZIF-8/g-C3N4/CuFe2O4 provides an promise avenue for the development of high potential magtenticaly photocatalytic systems for sustainable tetracycline production with remarkable efficiencies.

The performance of a packed bed reactor for CO2 conversion to methanol was investigated. Because of the exothermic reaction, temperature and concentration gradients happen along the reactor. A 2D packed-bed reactor model was applied for numerical investigations, coupled with detailed reaction kinetics and transport phenomena. The effect of feed inlet pressure and temperature on CO2 conversion and temperature profiles along the reactor was evaluated. The results showed that firstly increase in the temperature in the reactor because of the presence of exothermic reactions and then slight decrease in the temperature at top section of the reactor. Pressure was decrease from 55 bar to around 54 bar along the reactor. It was found that the CO2 conversion at outlet of reactor was 18.18 % at T = 475 K and it was increased to 23 % at temperature of 498 K and 555 K. The CO2 conversion was reached its maximum at short distance from the reactor entrance at T = 555 K. Also, the outlet temperature of gas mixture was increased from 522.53 K to 534.56 K with increasing feed inlet pressure from 35 bar to 55 bar. The CO2 conversion was 18.12 %, 20.77 %, and 22.96 % at pressures equal to 35, 45, and 55 bar respectively.

In this research, the magnesium ferrite/zinc oxide (MgFe2O4/ZnO) nanocomposite has been utilized as an effective adsorbent in the ultrasound-assisted dispersive solid-phase extraction (USA-DSPE) coupled to a spectrophotometric method for preconcentration of methylene blue (MB) and safranin O (SO) dyes. X-Ray diffraction (XRD), vibrating sample magnetometer (VSM), transmission electron microscopy (TEM), and field emission scanning electron microscopy (FESEM) analyses were performed to characterize and confirm the properties of the synthesized MgFe2O4/ZnO nanocomposite. Box-Behnken design (BBD) based on response surface methodology (RSM) was utilized to optimize the variables. The results of this research showed that the maximum extraction efficiency was obtained at pH = 7, eluent volume of 140 µL, adsorbent amount of 1.53 mg, and ultrasonic time of 8 min. After optimization by RSM and using the Quadratic model, the extraction efficiency for MB and SO was 98.60% and 97.23%, respectively. The analysis of variance (ANOVA) showed that the data obtained from the experiment were consistent with the predicted model. Under optimal conditions, the calibration curve was linear in the range of 5–1000 µg/L (coefficient of determination of 0.9884) for MB and 10–1100 µg/L (coefficient of determination of 0.9973) for SO. The limit of detections (LOD) of the proposed method for MB and SO were determined as 1.5 µg/L and 3 µg/L, respectively. The relative standard deviations (RSD, n = 5) for MB and SO were obtained as 1.1% and 2.1%, respectively. The results of adsorbent recovery revealed that MgFe2O4/ZnO nanocomposite could be used several times without a significant adsorbent efficiency loss. Furthermore, the findings of interference studies revealed that the presence of different ions does not cause substantial interference with the extraction of dyes. The environmental water samples analysis showed that the extraction efficiency for dyes was in the range of 93.74–99.11%.

Membrane-based separation processes has been recently of significant global interest compared to other conventional separation approaches due to possessing undeniable advantages like superior performance, environmentally-benign nature and simplicity of application. Computational simulation of fluids has shown its undeniable role in modeling and simulation of numerous physical/chemical phenomena including chemical engineering, chemical reaction, aerodynamics, drug delivery and plasma physics. Definition of fluids can be occurred using the Navier–Stokes equations, but solving the equations remains an important challenge. In membrane-based separation processes, true perception of fluid’s manner through disparate membrane modules is an important concern, which has been significantly limited applying numerical/computational procedures such s computational fluid dynamics (CFD). Despite this noteworthy advantage, the optimization of membrane processes using CFD is time-consuming and expensive. Therefore, combination of artificial intelligence (AI) and CFD can result in the creation of a promising hybrid model to accurately predict the model results and appropriately optimize membrane processes and phase separation. This paper aims to provide a comprehensive overview about the advantages of commonly-employed ML-based techniques in combination with the CFD to intelligently increase the optimization accuracy and predict mass transfer and the unfavorable events (i.e., fouling) in various membrane processes. To reach this objective, four principal strategies of AI including SL, USL, SSL and ANN were explained and their advantages/disadvantages were discussed. Then after, prevalent ML-based algorithm for membrane-based separation processes. Finally, the application potential of AI techniques in different membrane processes (i.e., fouling control, desalination and wastewater treatment) were presented.

In this study, an eco-friendly procedure was established by vortex-assisted liquid-phase microextraction based on deep eutectic solvent (VA−LPME−DES) combined with graphite furnace atomic absorption spectroscopy (GFAAS). The performance of this method was demonstrated by the extraction and analysis of lead (Pb), cadmium (Cd), and mercury (Hg) in fish samples. The hydrophobic DES is considered as a green extractant (environmentally friendly and less toxic than common organic solvents) and is a suitable alternative to common toxic organic solvents and is made of l-menthol and ethylene glycol (EG) with a molar ratio of 1:1. Under optimized conditions, the method linearity was in the ranges of 0.15–150 µg kg−1 with the coefficient of determinations (r2) higher than 0.996. Accordingly, the detection limits for Pb, Cd, and Hg were 0.05, 0.05, and 0.10 µg kg−1, respectively. The analysis of fish samples showed that the concentration of toxic elements in fish caught from the Tigris and Euphrates Rivers is much higher than the concentration of these elements in locally farmed trout fish. Also, the analysis of fish-certified reference materials with presented procedure produced results that were in good agreement with the certified values. The results showed that VA−LPME–DES is a very cheap, fast, and environmental-friendly procedure for the analysis of toxic elements in different types of fish species.