Efficient green Cr(VI) adsorbent from sorghum waste: Eco-designed functionalized mesoporous silica FDU-12.

This paper presents an eco-design approach to the synthesis of a highly efficient Cr(VI) adsorbent, utilizing a positively charged surface mesoporous FDU-12 material (designated as MI-Cl-FDU-12) for the first time. The MI-Cl-FDU-12 anion-exchange adsorbent was synthesized via a facile one-pot synthesis approach using sodium silicate extracted from sorghum waste as a green silica source, 1-methyl-3-(triethoxysilylpropyl) imidazolium chloride as a functionalization agent, triblock copolymer F127 as a templating or pore-directing agent, trimethyl benzene as a swelling agent, KCl as an additive, and water as a solvent. The synthesis method offers a sustainable and environmentally friendly approach to the production of a so-called "green" adsorbent with a bimodal micro-/mesoporous structure and a high surface area comparable with the previous reports regarding FDU-12 synthesis. MI-Cl-FDU-12 was applied as an anion exchanger for the adsorption of toxic Cr(VI) oxyanions from aqueous media and various kinetic and isotherm models were fitted to experimental data to propose the adsorption behavior of Cr(VI) on the adsorbent. Langmuir model revealed the best fit to the experimental data at four different temperatures, indicating a homogeneous surface site affinity. The theoretical maximum adsorption capacities of the adsorbent were found to be 363.5, 385.5, 409.0, and 416.9 mg g-1 at 298, 303, 308, and 313 K, respectively; at optimal conditions (pH=2, adsorbent dose=3.0 mg, and contact time of 30 min), surpassing that of most previously reported Cr(VI) adsorbents in the literature. A regeneration study revealed that this adsorbent possesses outstanding performance even after six consecutive recycling.

Spectrophotometric determination of phenol impurity in phenoxyethanol and phenol index of drinking water and municipal wastewater effluent after salting-out assisted liquid phase microextraction (SA-LPME).

In this study, a novel and convenient analytical method based on salting-out-assisted liquid phase microextraction (SA-LPME) has been developed. A spectrophotometric technique was employed to quantify the concentration of phenol in drinking water and treated wastewater, as well as the phenol impurity in 2-phenoxyethanol (PE). To accomplish this, a solution containing dissolved PE was supplemented with 4-aminoantipyrine (4-AAP) and hexacyanoferrate. Subsequently, NaCl was added to induce the formation of a two-phase system, consisting of fine droplets of PE as an extractant phase in the aqueous phase. The resulting red derivative was then extracted into the extractant phase and separated through centrifugation. Finally, the absorbance of the extracted derivative was measured at 520 nm. The Response Surface Methodology (RSM) based on the Box-Behnken Design (BBD) was employed to optimize the influential factors, namely 4-Aminoantipyrine (4-AAP), buffer (pH = 10), hexacyanoferrate, and NaCl. By utilizing the optimal conditions (buffer: 50 µL, 4-AAP (1% w/v): 80 µL, hexacyanoferrate (10% w/v): 65 µL, and NaCl: 0.7 g per 10 mL of the sample), the limit of detection was determined to be 0.7 ng mL-1 and 0.22 µg g-1 for water and PE samples, respectively. The relative standard deviation (RSD) and correlation of determination (r2) obtained fell within the range of 2.4-6.8% and 0.9983-0.9994, respectively. Moreover, an enrichment factor of 65 was achieved for a sample volume of 10 mL. The phenol concentration in two PE samples (PE-1, PE-2), provided by a pharmaceutical company (Pars Sadra Fanavar, Iran), were determined to be 0.83 ± 0.05 µg g-1 and 2.70 ± 0.14 µg g-1, respectively. Additionally, the phenol index in drinking water and treated municipal wastewater was found to be 3.60 ± 1.06 ng mL-1 and 4.60 ± 1.17 ng mL-1, respectively. These mentioned samples were spiked in order to evaluate the potential influence of the matrix. The relative recoveries from PE-1, PE-2 samples, drinking water, and treated municipal wastewater samples were measured as 104.5%, 97.5%, 101.6%, and 107.8%, respectively, indicating no matrix effect.

Molecular separation of ions from aqueous solutions using modified nanocomposites.

Herein, two novel porous polymer matrix nanocomposites were synthesized and used as adsorbents for heavy metal uptake. Methacrylate-modified large mesoporous silica FDU-12 was incorporated in poly(methyl methacrylate) matrix through an in-situ polymerization approach. For another, amine-modified FDU-12 was composited with Nylon 6,6 via a facile solution blending protocol. Various characterization techniques including small-angle X-ray scattering, FTIR spectroscopy, field emission-scanning electron microscopy, transmission electron microscopy, porosimetry, and thermogravimetric analysis have been applied to investigate the physical and chemical properties of the prepared materials. The adsorption of Pb(II) onto the synthesized nanocomposites was studied in a batch system. After study the effect of solution pH, adsorbent amount, contact time, and initial concentration of metal ion on the adsorption process, kinetic studies were also conducted. For both adsorbents, the Langmuir and pseudo-second-order models were found to be the best fit to predict isotherm and kinetics of adsorption. Based on the Langmuir model, maximum adsorption capacities of 105.3 and 109.9 mg g-1 were obtained for methacrylate-modified FDU-12/poly(methyl methacrylate) and amine-modified FDU-12/Nylon 6,6, respectively.

Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm.

The heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

Ability of neural network cells in learning teacher motivation scale and prediction of motivation with fuzzy logic system.

We employed a new approach in the field of social sciences or psychological aspects of teaching besides using a very common software package that is Statistical Package for the Social Sciences (SPSS). Artificial intelligence (AI) is a new domain that the methods of its data analysis could provide the researchers with new insights for their research studies and more innovative ways to analyze their data or verify the data with this method. Also, a very significant element in teaching is teacher motivation that is the trigger that pushes the teachers forward, depending on some internal and external factors. In the current study, seven research questions were designed to explore different aspects of teacher motivation, and they were analyzed via SPSS. The current study also compared the results by using an adaptive neuro-fuzzy inference system (ANFIS). Due to the similarity of ANFIS to humans' brain intelligence, the results of the current study could be similar to humans regarding what happens in reality. To do so, the researchers used the validated teacher motivation scale (TMS) and asked participants to fill the questionnaire, and analyzed the results. When the inputs were added to the ANFIS system, the model indicated a high accuracy and prediction capability. The findings also illustrated the importance of the tuning model parameters for the ANFIS method to build up the AI model with a high repeatability level. The differences between the results and conclusions are discussed in detail in the article.

Synthesis of novel adsorbent based on tetrasulfide-functionalized fibrous silica KCC-1 for removal of Hg(II) cations.

Hg(II) has been identified to be one of the extremely toxic heavy metals because of its hazardous effects and this fact that it is even more hazardous to animals than other pollutants such as Ag, Au, Cd, Ni, Pb, Co, Cu, and Zn. Accordingly, for the first time, tetrasulfide-functionalized fibrous silica KCC-1 (TS-KCC-1) spheres were synthesized by a facile, conventional ultrasonic-assisted, sol-gel-hydrothermal preparation approach to adsorb Hg(II) from aqueous solution. Tetrasulfide groups (-S-S-S-S-) were chosen as binding sites due to the strong and effective interaction of mercury ions (Hg(II)) with sulfur atoms. Hg(II) uptake onto TS-KCC-1 in a batch system has been carried out. Isotherm and kinetic results showed a very agreed agreement with Langmuir and pseudo-first-order models, respectively, with a Langmuir maximum uptake capacity of 132.55 mg g-1 (volume of the solution = 20.0 mL; adsorbent dose = 5.0 mg; pH = 5.0; temperature: 198 K; contact time = 40 min; shaking speed = 180 rpm). TS-KCC-1was shown to be a promising functional nanoporous material for the uptake of Hg(II) cations from aqueous media. To the best of our knowledge, there has been no report on the uptake of toxic Hg(II) cations by tetrasulfide-functionalized KCC-1 prepared by a conventional ultrasonic-assisted sol-gel-hydrothermal synthesis method.

Extraction of ingredients from tea leaves using oxidative enzymatic reaction and optimization of extraction conditions.

Peroxidase (POD) and polyphenol oxidase (PPO) are used as biocatalyst in many processes such as oxidization reactions, wastewater treatment, phenol synthesis and so on. The purpose of current study is enzymes extraction from biomass (tea leaves) as well as evaluation of their activation. Different parameters including temperature, buffer concentration, buffer type, buffer/tea leaves ratio, addition of high molecular weight polymers and emulsifiers, and pH were optimized in order to obtain the highest enzymes activity. Response Surface Methodology (RSM) procedure is employed for statistical analysis of enzymes extraction. It is understood from the result that PPO and POD possess the highest activity at temperatures of 25 °C and 50 °C, pH 7 and 5, buffer molarity of 0.1, and 0.05, buffer/tea leaves ratio = 5 for both, contact time = 20 min and 10 min, and presence of 6% and 3% PVP, 5% and 0% Tween 80 for PPO and POD, respectively. Amounts of highest activity for PPO and POD biocatalysts were calculated 0.42 U/mL and 0.025493 U/mL, respectively. Moreover, the entire inactivation of PPO took place after 30 min at 40 °C and 60 °C and 20 min at 80 °C. However, POD lost 35% of its activity after 30 min at 40 °C and 60 °C. The amount of 6% POD activity was kept after 45 min at 80 °C. Generally, it was indicated that POD was more resistant to thermal treatment than PPO.

Synthesis of multi-organo-functionalized fibrous silica KCC-1 for highly efficient adsorption of acid fuchsine and acid orange II from aqueous solution.

Multi-functionalized fibrous silica KCC-1 (MF-KCC-1) bearing amine, tetrasulfide, and thiol groups was synthesized via a post-functionalization method and fully characterized by several methods such as FTIR, FESEM, EDX-Mapping, TEM, and N2 adsorption-desorption techniques. Due to abundant surface functional groups, accessible active adsorption sites, high surface area (572 m2 g-1), large pore volume (0.98 cm3 g-1), and unique fibrous structure, mesoporous MF-KCC-1 was used as a potential adsorbent for the uptake of acid fuchsine (AF) and acid orange II (AO) from water. Different adsorption factors such as pH of the dye solution, the amount of adsorbent, initial dye concentration, and contact time, affecting the uptake process were optimized and isotherm and kinetic studies were conducted to find the possible mechanism involved in the process. For both AF and AO dyes, the Langmuir isotherm model and the PFO kinetic model show the most agreement with the experimental data. According to the Langmuir isotherm, the calculated maximum adsorption capacity for AF and AO were found to be 574.5 mg g-1 and 605.9 mg g-1, respectively, surpassing most adsorption capacities reported until now which is indicative of the high potential of mesoporous MF-KCC-1 as an adsorbent for removal applications.

Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system.

Computational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.

Investigation on performance of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) in a combination of CFD modeling for prediction of fluid flow.

Herein, a reactor of bubble column type with non-equilibrium thermal condition between air and water is mechanistically modeled and simulated by the CFD technique. Moreover, the combination of the adaptive network (AN) trainer with the fuzzy inference system (FIS) as the artificial intelligence method calling ANFIS has already shown potential in the optimization of CFD approach. Although the artificial intelligence method of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) has a good background for optimizing the other fields of research, there are not any investigations on the cooperation of this method with the CFD. The PSOFIS can reduce all the difficulties and simplify the investigation by elimination of the additional CFD simulations. In fact, after achieving the best intelligence, all the predictions can be done by the PSOFIS instead of the massive computational efforts needed for CFD modeling. The first aim of this study is to develop the PSOFIS for use in the CFD approach application. The second one is to make a comparison between the PSOFIS and ANFIS for the accurate prediction of the CFD results. In the present study, the CFD data are learned by the PSOFIS for prediction of the water velocity inside the bubble column. The values of input numbers, swarm sizes, and inertia weights are investigated for the best intelligence. Once the best intelligence is achieved, there is no need to mesh refinement in the CFD domain. The mesh density can be increased, and the newer predictions can be done in an easier way by the PSOFIS with much less computational efforts. For a strong verification, the results of the PSOFIS in the prediction of the liquid velocity are compared with those of the ANFIS. It was shown that for the same fuzzy set parameters, the PSOFIS predictions are closer to the CFD in comparison with the ANFIS. The regression number (R) of the PSOFIS (0.98) was a little more than that of the ANFIS (0.97). The PSOFIS showed a powerful potential in mesh density increment from 9477 to 774,468 and accurate predictions for the new nodes independent of the CFD modeling.

A novel and facile green synthesis method to prepare LDH/MOF nanocomposite for removal of Cd(II) and Pb(II).

To date, many nanoadsorbents have been developed and used to eliminate heavy metal contamination, however, one of the challenges ahead is the preparation of adsorbents from processes in which toxic organic solvents are used in the least possible amount. Herein, we have developed a new carboxylic acid-functionalized layered double hydroxide/metal-organic framework nanocomposite (LDH/MOF NC) using a simple, effective, and green in situ method. UiO-66-(Zr)-(COOH)2 MOF nanocrystals were grown uniformly over the whole surface of COOH-functionalized Ni50Co50-LDH ultrathin nanosheets in a green water system under a normal solvothermal condition at 100 °C. The synthesized LDH/MOF NC was used as a potential adsorbent for removal of toxic Cd(II) and Pb(II) from water and the influence of important factors on the adsorption process was monitored. Various non-linear isotherm and kinetic models were used to find plausible mechanisms involved in the adsorption, and it was found that the Langmuir and pseudo-first-order models show the best agreement with isotherm and kinetic data, respectively. The calculated maximum adsorption capacities of Cd(II) and Pb(II) by the LDH/MOF NC were found to be 415.3 and 301.4 mg g-1, respectively, based on the Langmuir model (pH = 5.0, adsorbent dose = 0.02 g, solution volume = 20 mL, contact time = 120 min, temperature = 25 â„ƒ, shaking speed 200 rpm).

Controlled release evaluation of paracetamol loaded amine functionalized mesoporous silica KCC1 compared to microcrystalline cellulose based tablets.

In the pharmaceutical manufacturing, drug release behavior development is remained as one of the main challenges to improve the drug effectiveness. Recently, more focus has been done on using mesoporous silica materials as drug carriers for prolonged and superior control of drug release in human body. In this study, release behavior of paracetamol is developed using drug-loaded KCC-1-NH2 mesoporous silica, based on direct compaction method for preparation of tablets. The purpose of this study is to investigate the utilizing of pure KCC-1 mesoporous silica (KCC-1) and amino functionalized KCC-1 (KCC-1-NH2) as drug carriers in oral solid dosage formulations compared to common excipient, microcrystalline cellulose (MCC), to improve the control of drug release rate by manipulating surface chemistry of the carrier. Different formulations of KCC-1 and KCC-NH2 are designed to investigate the effect of functionalized mesoporous silica as carrier on drug controlled-release rate. The results displayed the remarkable effect of KCC-1-NH2 on drug controlled-release in comparison with the formulation containing pure KCC-1 and formulation including MCC as reference materials. The pure KCC-1 and KCC-1-NH2 are characterized using different evaluation methods such as FTIR, SEM, TEM and N2 adsorption analysis.

Alkali metal doping of black phosphorus monolayer for ultrasensitive capture and detection of nitrogen dioxide.

Black phosphorus nanostructures have recently sparked substantial research interest for the rational development of novel chemosensors and nanodevices. For the first time, the influence of alkali metal doping of black phosphorus monolayer (BP) on its capabilities for nitrogen dioxide (NO2) capture and monitoring is discussed. Four different nanostructures including BP, Li-BP, Na-BP, and K-BP were evaluated; it was found that the adsorption configuration on Li-BP was different from others such that the NO2 molecule preferred a vertical stabilization rather than a parallel configuration with respect to the surface. The efficiency for the detection increased in the sequence of Na-BP < BP < K-BP < Li-BP, with the most significant improvement of + 95.2% in the case of Li doping. The Na-BP demonstrated the most compelling capacity (54 times higher than BP) for NO2 capture and catalysis (- 24.36 kcal/mol at HSE06/TZVP). Furthermore, the K-doped device was appropriate for both nitrogen dioxide adsorption and sensing while also providing the highest work function sensitivity (55.4%), which was much higher than that of BP (10.4%).

Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results.

Utilizing artificial intelligence algorithm of adaptive network-based fuzzy inference system (ANFIS) in combination with the computational lfuid dynamics (CFD) has recently revealed great potential as an auxiliary method for simulating challenging fluid mechnics problems. This research area is at the beginning, and needs sophisticated algorithms to be developed. No studies are available to consider the efficiency of the other trainers like differential evolution (DE) integrating with the FIS for capturing the pattern of the simulation results generated by CFD technique. Besides, the adjustment of the tuning parameters of the artificial intelligence (AI) algorithm for finding the highest level of intelligence is unavailable. The performance of AI algorithms in the meshing process has not been considered yet. Therfore, herein the Al2O3/water nanofluid flow in a porous pipe is simulated by a sophisticated hybrid approach combining mechnsitic model (CFD) and AI. The finite volume method (FVM) is employed as the CFD approach. Also, the differential evolution-based fuzzy inference system (DEFIS) is used for learning the CFD results. The DEFIS learns the nanofluid velocity in the y-direction, as output, and the nodes coordinates (i.e., x, y, and z), as inputs. The intelligence of the DEFIS is assessed by adjusting the methd's variables including input number, population number, and crossover. It was found that the DEFIS intelligence is related to the input number of 3, the crossover of 0.8, and the population number of 120. In addition, the nodes increment from 4833 to 774,468 was done by the DEFIS. The DEFIS predicted the velocity for the new dense mesh without using the CFD data. Finally, all CFD results were covered with the new predictions of the DEFIS.

Performance and application analysis of ANFIS artificial intelligence for pressure prediction of nanofluid convective flow in a heated pipe.

Heat transfer augmentation of the nanofluids is still an attractive concept for researchers due to rising demands for designing efficient heat transfer fluids. However, the pressure loss arisen from the suspension of nanoparticles in liquid is known as a drawback for developing such novel fluids. Therefore, prediction of the nanofluid pressure, especially in internal flows, has been focused on studies. Computational fluid dynamics (CFD) is a commonly used approach for such a prediction of fluid flow. The CFD tools are perfect and precise in prediction of the fluid flow parameters. But they might be time-consuming and expensive, especially for complex models such as 3-dimension modeling and turbulent flow. In addition, the CFD could just predict the pressure, and it is disabled for finding the relationship of such variables. This study is intended to show the performance of the artificial intelligence (AI) algorithm as an auxiliary method for cooperation with the CFD. The turbulent flow of Cu/water nanofluid warming up in a pipe is considered as a sample of a physical phenomenon. The AI algorithm learns the CFD results. Then, the relation between the CFD results is discovered by the AI algorithm. For this purpose, the adaptive network-based fuzzy inference system (ANFIS) is adopted as AI tool. The intelligence condition of the ANFIS is checked by benchmarking the CFD results. The paper outcomes indicated that the ANFIS intelligence is met by employing gauss2mf in the model as the membership function and x, y, and z coordinates, the nanoparticle volume fraction, and the temperature as the inputs. The pressure predicted by the ANFIS at this condition is the same as that predicted by the CFD. The artificial intelligence of ANFIS could find the relation of the nanofluid pressure to the nanoparticle fraction and the temperature. The CFD simulation took much more time (90-110 min) than the total time of the learning and the prediction of the ANFIS (369 s). The CFD modeling was done on a workstation computer, while the ANFIS method was run on a normal desktop.

High performance ozone based advanced oxidation processes catalyzed with novel argon plasma treated iron oxyhydroxide hydrate for phenazopyridine degradation.

The present study has focused on the degradation of phenazopyridine (PhP) as an emerging contaminant through catalytic ozonation by novel plasma treated natural limonite (FeOOH·xH2O, NL) under argon atmosphere (PTL/Ar). The physical and chemical characteristics of samples were evaluated with different analyses. The obtained results demonstrated higher surface area for PTL/Ar and negligible change in crystal structure, compared to NL. It was found that the synergistic effect between ozone and PTL/Ar nanocatalyst was led to highest PhP degradation efficiency. The kinetic study confirmed the pseudo-first-order reaction for the PhP degradation processes included adsorption, peroxone and ozonation, catalytic ozonation with NL and PTL/Ar. Long term application (6 cycles) confirmed the high stability of the PTL/Ar. Moreover, different organic and inorganic salts as well as the dissolved ozone concentration demonstrated the predominant role of hydroxyl radicals and superoxide radicals in PhP degradation by catalytic Ozonation using PTL/Ar. The main produced intermediates during PhP oxidation by PTL/Ar catalytic ozonation were identified using LC-(+ESI)-MS technique. Finally, the negligible iron leaching, higher mineralization rate, lower electrical energy consumption and excellent catalytic activity of PTL/Ar samples demonstrate the superior application of non-thermal plasma for treatment of NL.

Experimental and thermodynamic modeling decitabine anti cancer drug solubility in supercritical carbon dioxide.

Design and development of efficient processes for continuous manufacturing of solid dosage oral formulations is of crucial importance for pharmaceutical industry in order to implement the Quality-by-Design paradigm. Supercritical solvent-based manufacturing can be utilized in pharmaceutical processing owing to its inherent operational advantages. However, in order to evaluate the possibility of supercritical processing for a particular medicine, solubility measurement needs to be carried out prior to process design. The current work reports a systematic solubility analysis on decitabine as an anti-cancer medicine. The solvent is supercritical carbon dioxide at different conditions (temperatures and pressures), while gravimetric technique is used to obtain the solubility data for decitabine. The results indicated that the solubility of decitabine varies between 2.84 × 10-05 and 1.07 × 10-03 mol fraction depending on the temperature and pressure. In the experiments, temperature and pressure varied between 308-338 K and 12-40 MPa, respectively. The solubility of decitabine was plotted against temperature and pressure, and it turned out that the solubility had direct relation with the pressure due to the effect of pressure on solvating power of solvent. The effect of temperature on solubility was shown to be dependent on the cross-over pressure. Below the cross-over pressure, there is a reverse relation between temperature and solubility, while a direct relation was observed above the cross-over pressure (16 MPa). Theoretical study was carried out to correlate the solubility data using several thermodynamic-based models. The fitting and model calibration indicated that the examined models were of linear nature and capable to predict the measured decitabine solubilities with the highest average absolute relative deviation percent (AARD %) of 8.9%.

Intensification of CO2 absorption using MDEA-based nanofluid in a hollow fibre membrane contactor.

Porous hollow fibres made of polyvinylidene fluoride were employed as membrane contactor for carbon dioxide (CO2) absorption in a gas-liquid mode with methyldiethanolamine (MDEA) based nanofluid absorbent. Both theoretical and experimental works were carried out in which a mechanistic model was developed that considers the mass transfer of components in all subdomains of the contactor module. Also, the model considers convectional mass transfer in shell and tube subdomains with the chemical reaction as well as Grazing and Brownian motion of nanoparticles effects. The predicted outputs of the developed model and simulations showed that the dispersion of CNT nanoparticles to MDEA-based solvent improves CO2 capture percentage compared to the pure solvent. In addition, the efficiency of CO2 capture for MDEA-based nanofluid was increased with rising MDEA content, liquid flow rate and membrane porosity. On the other hand, the enhancement of gas velocity and the membrane tortuosity led to reduced CO2 capture efficiency in the module. Moreover, it was revealed that the CNT nanoparticles effect on CO2 removal is higher in the presence of lower MDEA concentration (5%) in the solvent. The model was validated by comparing with the experimental data, and great agreement was obtained.

Predicting Air Superficial Velocity of Two-Phase Reactors Using ANFIS and CFD.

In predicting the turbulence property of gas (bubble) flow in the domain of continuous fluid and liquid, the integration of machine learning and computational fluid dynamics (CFD) methods reduces the overall computational time. This combination enables us to see the effective input parameters in the engineering process and the impact of operating conditions on final outputs, such as gas hold-up, heat and mass transfer, and the flow regime (uniform bubble distribution or nonuniform bubble properties). This paper uses the combination of machine learning and single-size calculation of the Eulerian method to estimate the gas flow distribution in the continuous liquid fluid. To present the machine-learning method besides the Eulerian method, an adaptive neuro-fuzzy inference system (ANFIS) is used to train the CFD finding and then estimate the flow based on the machine-learning method. The gas velocity and turbulent eddy dissipation rate are trained throughout the bubble column reactor (BCR) for each CFD node, and the artificial BCR is predicted by the ANFIS method. This smart reactor can represent the artificial CFD of the BCR, resulting in the reduction of expensive numerical simulations. The results showed that the number of inputs could significantly change this method's accuracy, representing the intelligence of method in the learning data set. Additionally, the membership function specifications can impact the accuracy, particularly, when the process is trained with different inputs. The turbulent eddy dissipation rate can also be predicted by the ANFIS method with a similar model pattern for air superficial gas velocity.