Design and synthesis of drug hydrogels containing carboxymethylcellulose with honeycomb structure and pH-sensitivity as drug delivery systems for adriamycin, metformin, and naproxen.

The aim of this research is to prepare and identify functionalized carboxymethylcellulose/mesoporous silica nanohydrogels (CMC/NH2-MCM-41) for obtaining a pH-sensitive system for the controlled release of drugs. The beads of CMC/NH2-MCM-41 nanocomposites were prepared by dispersing NH2-MCM-41 in a CMC polymer matrix and crosslinking with ferric ions (Fe3+). The SEM analysis of samples revealed enhancement in surface porosity of the functionalized nanohydrogel beads compared to the conventional beads. Swelling of the prepared functionalized nanohydrogels was evaluated at various pH values including pH = 7.35-7.45 (simulated body fluid or healthy cells), pH = 6 (simulated intestinal fluid), and pH = 1.5-3.5 (simulated gastric fluid). The swelling of CMC/MCM-41 and CMC/NH2-MCM-41 nanohydrogels at the pH values of simulated body fluid and simulated intestinal fluid is much higher than that of simulated gastric fluid, indicating successful synthesis of pH-sensitive nanohydrogels for drug delivery. The drug loading results showed that drug release in the CMC/NH2-MCM-41 system is much slower than that in the CMC/MCM-41 system. The results of the survival studies for the manufactured systems showed a very good biocompatibility of the designed drug delivery systems for biological applications. By coating the surface of functionalized mesopores with CMC hydrogel, we were able to develop a pH-sensitive intelligent drug delivery system.

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.

A molecularly enhanced proof of concept for targeting cocrystals at molecular scale in continuous pharmaceuticals cocrystallization.

It is impossible to optimize a process for a target drug product with the desired profile without a proper understanding of the interplay among the material attributes, the process parameters, and the attributes of the drug product. There is a particular need to bridge the micro- and mesoscale events that occur during this process. Here, we propose а molecular engineering methodology for the continuous cocrystallization process, based on Raman spectra measured experimentally with a probe and from quantum mechanical calculations. Using molecular dynamics simulations, the theoretical Raman spectra were calculated from first principles for local mixture structures under an external shear force at various temperatures. A proof of concept is developed to build the process design space from the computed data. We show that the determined process design space provides valuable insight for optimizing the cocrystallization process at the nanoscale, where experimental measurements are difficult and/or inapplicable. The results suggest that our method may be used to target cocrystallization processes at the molecular scale for improved pharmaceutical synthesis.

Resource recovery from landfill leachate: An experimental investigation and perspectives.

This work investigates the performances of coconut shell waste-based activated carbon (CSWAC) adsorption in batch studies for removal of ammoniacal nitrogen (NH3-N) and refractory pollutants (as indicated by decreasing COD concentration) from landfill leachate. To valorize unused resources, coconut shell, recovered and recycled from agricultural waste, was converted into activated carbon, which can be used for leachate treatment. The ozonation of the CSWAC was conducted to enhance its removal performance for target pollutants. The adsorption mechanisms of refractory pollutants by the adsorbent are proposed. Perspectives on nutrient recovery technologies from landfill leachate from the view-points of downstream processing are presented. Their removal efficiencies for both recalcitrant compounds and ammoniacal nitrogen were compared to those of other techniques reported in previous work. It is found that the ozonated CSWAC substantially removed COD (i.e. 76%) as well as NH3-N (i.e. 75%), as compared to the CSWAC without pretreatment (i.e. COD: 44%; NH3-N: 51%) with NH3-N and COD concentrations of 2750 and 8500 mg/L, respectively. This reveals the need of ozonation for the adsorbent to improve its performance for the removal of COD and NH3-N at optimized reactions: 30 g/L of CSWAC, pH 8, 200 rpm of shaking speed and 20 min of reaction time. Nevertheless, treatment of the leachate samples using the ozonated CSWAC alone was still unable to result in treated effluents that could meet the COD and NH3-N discharge standards below 200 and 5 mg/L, respectively, set by legislative requirements. This reveals that another treatment is necessary to be undertaken to comply with the requirement of their effluent limit.

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.

Molecular engineering of cocrystallization process in holt melt extrusion based on kinetics of elementary molecular processes.

Continuous co-crystallization in a twin-screw granulator is a promising technology. In order to fundamentally optimize the process flow, it is necessary to investigate the kinetics of molecular interactions within the mixture and the effect of these interactions on co-crystal formation. In this study, the processes governing the co-crystallization of ibuprofen and nicotinamide were considered. Density functional theory calculations employing the Hirshfeld partitioning scheme were used to identify donor-acceptor sites on each molecule. A total of twenty-one different molecular interactions was identified (nine of ibuprofen and nicotinamide (resembling co-crystals), three of ibuprofen and itself (resembling the ibuprofen dimer), and nine of nicotinamide and itself (resembling the nicotinamide dimer)). Each interaction was defined as an artificial reversible reaction and the kinetics were calculated using the transition state theory of chemical reactions, where linear and quadratic synchronous transition methods were utilized to identify transition-state structures; the minimum energy path was determined using the nudged elastic band method. A kinetic Monte Carlo framework was used to study the collective/coupled effect of reactions on the progress of the co-crystallization process. it was found that operating at low temperatures (especially lower or very close to the melting temperature of ibuprofen) for longer residency times creates a safe route for maximizing the presence of ibuprofen and nicotinamide co-crystals. If the proposed route is applied, the purity and properties of the produced co-crystal would be significant, especially its desirable availability within the body.

Resource recovery toward sustainability through nutrient removal from landfill leachate.

This study investigated the feasibility of integrated ammonium stripping and/or coconut shell waste-based activated carbon (CSWAC) adsorption in treating leachate samples. To valorize unused biomass for water treatment application, the adsorbent originated from coconut shell waste. To enhance its performance for target pollutants, the adsorbent was pretreated with ozone and NaOH. The effects of pH, temperature, and airflow rate on the removal of ammoniacal nitrogen (NH3-N) and refractory pollutants were studied during stripping alone. The removal performances of refractory compounds in this study were compared to those of other treatments previously reported. To contribute new knowledge to the field of study, perspectives on nutrients removal and recovery like phosphorus and nitrogen are presented. It was found that the ammonium stripping and adsorption treatment using the ozonated CSWAC attained an almost complete removal (99%) of NH3-N and 90% of COD with initial NH3-N and COD concentrations of 2500 mg/L and 20,000 mg/L, respectively, at optimized conditions. With the COD of treated effluents higher than 200 mg/L, the combined treatments were not satisfactory enough to remove target refractory compounds. Therefore, further biological processes are required to complete their biodegradation to meet the effluent limit set by environmental legislation. As this work has contributed to resource recovery as the driving force of landfill management, it is important to note the investment and operational expenses, engineering applicability of the technologies, and their environmental concerns and benefits. If properly managed, nutrient recovery from waste streams offers environmental and socio-economic benefits that would improve public health and create jobs for the local community.

Understanding solid-state processing of pharmaceutical cocrystals via milling: Role of tablet excipients.

Discovery of novel cocrystal systems and improvement of their physicochemical properties dominates the current literature on cocrystals yet the required end-product formulation is rarely addressed. Drug product manufacturing includes complex API solid state processing steps such as milling, granulation, and tableting. These all require high mechanical stress which can lead to solid-state phase transformations into polymorphs and solvates, or lead to dissociation of cocrystals into their individual components. Here we measured the effect of tablet excipients on solid-state processing of a range of pharmaceutical cocrystal formulations. Our findings were rationalised using Density Functional Theory (DFT) calculations of intermolecular binding energies of cocrystal constituents and co-milling excipients. A 1:1 stoichiometric ratio of API Theophylline (THP) and co-former 4-Aminobenzoic acid (4ABA) was co-milled with five different excipients: hydroxypropylmethylcellulose (HPMC), polyvinylpyrrolidone (PVP), polyethylene glycol (PEG), lactose, and microcrystalline cellulose (MCC). The experiments were carried out in 10 and 25 ml milling jars at 30 Hz for different milling times. Co-milled samples were characterised for formation of cocrystals and phase transformation using powder X-ray diffraction (PXRD) and differential scanning calorimetry (DSC). Our data shows that co-milling in the presence of PEG, HMPC or lactose yields purer cocrystals, supported by the calculated stronger excipient interactions for PVP and MCC. We identify a suitably-prepared THP-4ABA pharmaceutical cocrystal formulation that is stable under extended milling conditions.

A societal transition of MSW management in Xiamen (China) toward a circular economy through integrated waste recycling and technological digitization.

Recently Xiamen (China) has encountered various challenges of municipal solid waste management (MSWM) such as lack of a complete garbage sorting and recycling system, the absence of waste segregation between organic and dry waste at source, and a shortage of complete and clear information about the MSW generated. This article critically analyzes the existing bottlenecks in its waste management system and discusses the way forward for the city to enhance its MSWM by drawing lessons from Hong Kong's effectiveness in dealing with the same problems over the past decades. Solutions to the MSWM problem are not only limited to technological options, but also integrate environmental, legal, and institutional perspectives. The solutions include (1) enhancing source separation and improving recycling system; (2) improving the legislation system of the MSWM; (3) improvement of terminal disposal facilities in the city; (4) incorporating digitization into MSWM; and (5) establishing standards and definitions for recycled products and/or recyclable materials. We also evaluate and compare different aspects of MSWM in Xiamen and Hong Kong SAR (special administrative region) under the framework of 'One Country, Two Systems' concerning environmental policies, generation, composition, characteristics, treatment, and disposal of their MSW. The nexus of society, economics of the MSW, and the environment in the sustainability sphere are established by promoting local recycling industries and the standardization of recycled products and/or recyclable materials. The roles of digitization technologies in the 4th Industrial Revolution for waste reduction in the framework of circular economy (CE) are also elaborated. This technological solution may improve the city's MSWM in terms of public participation in MSW separation through reduction, recycle, reuse, recovery, and repair (5Rs) schemes. To meet top-down policy goals such as a 35% recycling rate for the generated waste by 2030, incorporating digitization into the MSWM provides the city with technology-driven waste solutions.

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.

Pressure and temperature predictions of Al2O3/water nanofluid flow in a porous pipe for different nanoparticles volume fractions: combination of CFD and ACOFIS.

Artificial intelligence (AI) techniques have illustrated significant roles in finding general patterns of CFD (Computational fluid dynamics) results. This study is conducted to develop combination of the ant colony optimization (ACO) algorithm with the fuzzy inference system (ACOFIS) for learning the CFD results of a physical case study. This binary join of the ACOFIS and CFD was used for pressure and temperature predictions of Al2O3/water nanofluid flow in a heated porous pipe. The intelligence of ACOFIS is investigated for different input numbers and pheromone effects, as the ant colony tuning parameter. The results showed that the intelligence of the ACOFIS could be found for three inputs (x and y nodes coordinates and nanoparticles fraction) and the pheromone effect of 0.1. At the system intelligence, the ACOFIS could predict the pressure and temperature of the nanofluid on any values of the nanoparticles fraction between 0.5 and 2%. Comparing the ANFIS and the ACOFIS, it was shown that both methods could reach the same accuracy in predictions of the nanofluid pressure and temperature. The root mean square error (RMSE) of the ACOFIS (~ 1.3) was a little more than that of the ANFIS (~ 0.03), while the total process time of the ANFIS (~ 213 s) was a bit more than that of the ACOFIS (~ 198 s). The AI algorithms process time (less than 4 min) shows their ability in the reduction of CFD modeling calculations and expenses.

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%.

Efficient oxidation/mineralization of pharmaceutical pollutants using a novel Iron (III) oxyhydroxide nanostructure prepared via plasma technology: Experimental, modeling and DFT studies.

High-performance novel iron oxyhydroxide (limonite) nanostructure, with improved surface reactive sites, was prepared via one-pot, eco-friendly, free precursor and cold glow discharge N2-plasma technique. Natural and plasma treated (PTNL/N2) limonite samples were characterized by FESEM, XPS, XRD, FTIR, AAS, EDX, BET/BJH and pHpzc to confirm the successful synthesis. Central composite design (CCD) and artificial neural network (ANN, topology of 4:8:1) methods were utilized to study the oxidation/mineralization of phenazopyridine (PhP) as a hazardous contaminant by heterogeneous catalytic ozonation process (HCOP). The obtained results indicated that PTNL/N2 had the highest catalytic performance in PhP degradation (98.6% in 40 min) and mineralization (80.4% in 120 min). The degradation mechanism in different processes was investigated by dissolved ozone concentration, various organic scavengers (BQ and TBA) and inorganic salts (NaNO3, NaCl, Na2CO3 and NaH2PO4). Moreover, reusability-stability, Fe and nitrogen (NO3- and NH4+) ions release were assessed during different AOPs. Furthermore, toxicity tests indicated that the HCOP using PTNL/N2 was able to detoxify the PhP solutions efficiently. Finally, Density Functional Theory (DFT) studies were employed to introduce the most plausible contaminant degradation pathway, reactive sites and byproducts. This research provided a new insight into the improvement of wastewater treatment studies by a combination of experiment and computer simulation.

Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors.

To understand impact of input and output parameters during optimization and degree of complexity, in the current study we numerically designed a bubble column reactor with a single sparger in the middle of the reactor. After that, some input and output parameters were selected in the post-processing of the numerical method, and then the machine learning observation started to investigate the level of complexity and impact of each input on output parameters. The adaptive neuro-fuzzy inference system (ANFIS) method was exploited as a machine learning approach to analyze the gas-liquid flow in the reactor. The ANFIS method was used as a machine learning approach to simulate the flow of a 3D (three-dimensional) bubble column reactor. This model was also used to analyze the influence of input and output parameters together. More specifically, by analyzing the degree of membership functions as a function of each input, the level of complexity of gas fraction was investigated as a function of computing nodes (X, Y, and Z directions). The results showed that a higher number of membership functions results in a better understanding of the process and higher model accuracy and prediction capability. X and Y computing nodes have a similar impact on the gas fraction, while Z computing points (height of reactor) have a uniform distribution of membership function across the column. Four membership functions (MFs) in each input parameter are insufficient to predict the gas fraction in the 3D bubble column reactor. However, by adding two membership functions, all features of gas fraction in the 3D reactor can be captured by the machine learning algorithm. Indeed, the degree of MFs was considered as a function of each input parameter and the effective parameter was found based on the impact of MFs on the output.

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.

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.

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.

Prediction of gas velocity in two-phase flow using developed fuzzy logic system with differential evolution algorithm.

In this investigation, differential evolution (DE) algorithm with the fuzzy inference system (FIS) are combined and the DE algorithm is employed in FIS training process. Considered data in this study were extracted from simulation of a 2D two-phase reactor in which gas was sparged from bottom of reactor, and the injected gas velocities were between 0.05 to 0.11 m/s. After doing a couple of training by making some changes in DE parameters and FIS parameters, the greatest percentage of FIS capacity was achieved. By applying the optimized model, the gas phase velocity in x direction inside the reactor was predicted when the injected gas velocity was 0.08 m/s.

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.