r. beigzadeh

The optimum design of mixing tank reactors is very important to increase the mixing in industries. This study examined how varying operational factors, such as the type and angle of stirring blades, as well as the rotational speed, affect mixing rates. Four types of mixers (Rushton, Smith Turbine, Reversible Rushton, and Reversible Propeller) were modeled using computational fluid dynamics techniques to assess their mixing performance. The study analyzed the effects of varying rotational speeds (20, 40, and 60 rpm) and inlet flow velocities (0.028, 0.03, 0.04, and 0.05 m/s) on the mixing efficiency. The results showed that, among the designed models, the Reversible Rushton has the best mixing rate and the closest behavior to the ideal state. Where θ is equal to 3, its mixing factor is equal to 0.083879. Additionally, it was noted that as the inlet velocity increased, the rate of change in mixing also became more pronounced. It was observed that the Reversible Rushton shows the least sensitivity to the change of rotational speed compared to the other three blades. The Reversible Rushton model at 20 rpm rotational speed and 0.03 m/s inlet velocity demonstrated the highest mixing rate among all investigated cases.

It is significant to determine the optimal dimensions of the heat exchangers to reduce energy consumption. The helical tube heat exchanger is widely used in industry due to its advantages over other types. Therefore, investigating this type of heat exchanger can be an interesting topic. In this research, conical coil tubes with circular, elliptical, and square cross-sections with 10, 30, and 50° cone angles and 15, 30, and 45 mm pitch were modeled by computational fluid dynamics to evaluate the thermal-hydrodynamic performance. The data relating to the Nusselt number and friction factor for all investigated geometric shapes were compared and analyzed. The results showed that the elliptical cross-section tubes have better heat transfer performance compared to other geometries. The results showed that the elliptical cross-section has a better heat transfer performance compared to the square and circular cross-sections by 34.33% and 0.38%, respectively. Moreover, the lower values of the Nusselt number and the friction factor were obtained for the square cross-section due to the change in the thickness of the boundary layer.

Low-density polyethylene is a subset of polyethylene that is mainly used in the plastics industry. These polymers are formed at high pressures and temperatures using a radical reaction. The fouling phenomenon is the result of the thermodynamic phase separation of polymer and ethylene. This phase separation occurs in the wall area (wall close to the cooling flow) of the tubular reactor, which reduces heat transfer to the reactor jacket, reducing production and in critical cases dangerous decomposition of ethylene. The purpose of this study is to simulate the phenomenon of fouling and its effects on the temperature and pressure of the reactor based on the heat transfer problems. The solved mathematical model is formed based on heat transfer equations to solve the computational fluid dynamics equations which are used to calculate the phase equilibrium data for pressure and temperature: The obtained results show that by increasing the thickness of the fouling, the heat transfer rate decreases. The operating model was used based on thelow-density polyethylene with a grade of 2420H. Based on the model, it was found that with increasing the thickness of fouling from 0.1 to 1.2 mm, the outlet temperature of the reactor increases, but does not have a significant effect on the pressure drop inside the reactor: Moreover,10 °C increase in reaction temperature caused the volume percentage of the second phase (fouling) is reduced from 0.1833 to 0.1818 mm.

In this study, the application of bio-acid leaching method based on the use of lemon juice to extract copper and zinc metals from mobile printed circuit boards has been investigated. Three important factors were investigated include lemon juice concentration, Solid / Liquid (S/L) ratio, and hydrogen peroxide (H2O2) concentration. Response surface methodology (RSM) was used to optimize the effective factors. The results showed that for particles with a size of 150 to 180 μm at a constant temperature of 20 ° C and time 4 h under optimal conditions including 1.41% (w/v) S/L ratio, 12.2% (v/v) H2O2 and 74% (v/v) lemon juice, copper and zinc recovery efficiencies are 89% and 73%, respectively. Moreover, the artificial neural network was used to predict the extraction of copper and zinc metals as a function of the studied factors. To validate the model, laboratory results were considered as evaluation data. The results of neural network modeling showed high accuracy to predict the target variable. The values of MRE, MSE, and R2 were 9.485, 15.254, and 0.9356% for the copper extraction model and 1.854%, 1.094, and 0.9963% for the zinc extraction model, respectively.

Static mixers are applied for increasing the mixing in chemical reactors as well as for increasing the heat transfer coefficient in heat exchangers. In the study, fluid flow characteristics in tubes equipped with modified static mixers with different geometric parameters were investigated by computational fluid dynamics. The classic twisted tape, perforated twisted tape, and V-Cut twisted tape were evaluated for Reynolds numbers between of 3000 to 19000. The fluid flow and pressure drop for the mixers were investigated. The validated simulation results were employed to train the artificial neural network model. Reynolds number and geometric parameters of the mixers were used as input variables of the neural network for predicting the friction factor. The model accuracy for estimating the friction factor was investigated and a relative error of less than 1% was obtained. The main relative errors for all data in classical, V-Cut, and perforated twisted tape were 0.75%, 0.57%, and 0.52%, respectively, and for validation data were 1.1%, 0.92%, and 0.62%. 30% of the data were randomly selected for the neural network to prove the model validity.

The artificial neural network (ANN) approach was applied to develop simple correlations for predicting the thermal conductivity of nitrogen-methane and carbon dioxide-methane mixtures. The genetic algorithm method was used to obtain global optimum parameters (weights and biases) of the ANNs. The methane mole fraction, temperature, pressure, and density as effective parameters on thermal conductivity were network input variables. 171 and 180 data points related to the nitrogen-methane and carbon dioxide-methane gas mixtures, respectively, divided to test and train datasets. Simple correlations were obtained due to the small number of optimal neurons in the ANN structures. The mean relative errors of 0.206% and 0.199% for the testing dataset indicate the high accuracy and validation of the correlations. The work indicates that artificial intelligence approaches are very useful for thermal conductivity modeling in natural gases. A sensitivity analysis was performed on all input variables that indicates that the gas mixture density has the greatest impact on the thermal conductivity.