فهرست مطالب

Chemical Engineering - Volume:21 Issue: 4, Autumn 2024

Iranian journal of chemical engineering
Volume:21 Issue: 4, Autumn 2024

  • تاریخ انتشار: 1403/09/11
  • تعداد عناوین: 6
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  • Arsalan Parvareh *, Mohammad Ghanbarnezhad, Mostafa Keshavarz Moraveji, Sahand Jorfi Pages 3-19

    The Fe3O4/MW-CNT composite was prepared for a hybrid photo-catalyst-assisted electrochemical process for the removal of BTX contamination from wastewater. Oxidation of multi-walled carbon nanotube was conducted by different treatments including acid treatment and hydrogen peroxide. The XRD, FTIR, SEM, TEM, and BET analyses were performed to characterize both the MW-CNT and the synthesized composite. Simultaneous photo-catalyst and electrochemical processes were conducted to evaluate the performance of a new hybrid process for wastewater treatment. The effect of current density, photo-catalyst loading, and BTX initial concentration was investigated experimentally. The characterization results of the synthesized composite show that a mixture of strong nitric acid and sulfuric acid treatment at a high exposure time and low temperature is the best route for MW-CNT oxidation. The removal efficiency of BTX compounds from wastewater using the hybrid photo-electrochemical process was found to be in the range of 28 to 43% for different conditions. The optimum condition for maximum removal of BTX was found by mathematical modeling of experimental data. The results indicate that a combination of photo-catalyst and the electrochemical process can enhance the BTX removal efficiency.

    Keywords: Multi-Walled Carbon Nano-Tube, Photo-Catalyst, Electrochemical, Hydrothermal Treatment, Fe3o4, MWCNT Composite
  • Maryam Dinarvand, Mahdieh Abolhasani * Pages 20-36

    In this study, the effect of the presence of a magnetic field (MF) on the thermal conductivity of the nanofluid (NF) ( ) containing spinel ferrite nanoparticles (NPs) (MFe2O4, M=Fe, Co) was investigated. CoFe2O4 NPs were concentrated by the coprecipitation method. Both NPs were characterized by SEM, EDX, XRD, and VSM. The thermal conductivity was investigated and compared in the presence and absence of an MF. In addition to the intensity of MF (100, 200, 300, and 400 G), the effect of the concentration of NPs (from 0.25 to 2 Vol%) on  at a constant temperature of 25 °C was investigated. According to the results, in the absence of MF, the  of CoFe2O4/water ferrofluid (FF) was higher than that of Fe3O4/water FF in different concentrations. Furthermore, as the intensity of the MF increased, the  of both Fe3O4/water and CoFe2O4/water FFs increased. This increase was more observed for the FFs containing Fe3O4 NPs. At the highest concentration (2 Vol%), with the increase of MF up to 400 G, the  of Fe3O4/water has increased by about 3.2%, while this increase was about 1.8% for CoFe2O4/water. Increasing the volume percentage of NPs also had a positive effect on the thermal conductivity coefficient. Finally, according to the obtained results, correlations were presented to predict the  of both FFs according to the intensity of the MF and the concentration of NPs. The proposed correlations had a satisfactory accuracy with R2 values of 0.98 for both FFs.

    Keywords: Thermal Conductivity, Ferrofluid, Magnetic Field, Spinel Ferrite Nanoparticles
  • Mohammad Shareei *, Reza Mosayebi Behbahani, Fatemeh Rashedi Pages 37-47

    This study was conducted to decrease the concentration of acetic acid in wastewater of acetic acid plants through the photocatalytic oxidation method. This process employed commercial Titanium dioxide powder (TiO2) as a photocatalyst, utilizing a UV lamp as the light source within a batch reactor system for advanced oxidation. Various experimental parameters were modified, including the concentration of acetic acid, the amount of catalyst, the volume of waste, temperature, and reaction time. The residual acid concentration and COD values were recorded as results of the process. The percentage of acetic acid remaining in the solution was determined by using a gas chromatography (G.C) device. Experiments were conducted with different volumes, from 200 ml to 35 ml, and utilized varying amounts of photocatalyst: 0.01 g, 0.005 g, 0.0025 g, and 0.001 g. Additionally, the experiments were carried out over two-time intervals of 2 hours and 5 hours. The wastewater concentration contained 3% by mass of acetic acid, and the average COD value was 13300. After experiments, it was found that the optimal conditions for removing acetic acid were a volume of 35 ml and 0.0025 g of catalyst used for 2 hours. In this condition, the percentage of acetic acid decreased from 3% to 0.2%, which is a 93% decrease, and the COD decreased from 13,300 to 2,800, which is a 79% decrease.

    Keywords: Acetic Acid, Photocatalyst, UV Irradiation, Tio2, Photoreactor
  • Hassan Tavakoli *, Moslem Abrofarakh, Rasool Amirkhani Pages 48-61

    This study investigated sarin gas dispersion in an indoor environment using transient three-dimensional Computational Fluid Dynamics (CFD) and Artificial Neural Network (ANN) approaches. To achieve this, the CFD model was first verified and validated. Then, random locations in the indoor environment were considered as inlets of airflow with sarin gas, and the dangerous times were calculated using the CFD model. Finally, these results of the CFD model were used as inputs to train the ANN model. The results of this study demonstrated that the present model exhibited strong agreement with experimental data. Also, the results of training the ANN showed that for all sections, the training, validation, and testing data and model results were consistent with a high R-squared value. Moreover, the results of different air inlet locations showed that if the air inlet was placed in the corner sections of the indoor environment, the danger time increased. Additionally, if the air inlet was placed near the open region, the danger time also increased, which is an important result for designing indoor environments.

    Keywords: Sarin Gas, Danger Time, CFD, ANN
  • Seyed Mohammad Razavi, Rahmat Sotudeh Gharebagh *, Navid Mostoufi, Jamal Chaouki, K.D.P. Nigam Pages 62-77

    This paper presents a machine learning-based approach for accurately predicting pyrolysis product yields. Methods such as Linear Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Support Vector Regression (SVR), Random Forest (RF), and Neural Networks (NN) leverage operating conditions and/or ultimate/proximate analysis data, eliminating the need for reaction kinetics. This innovative approach offers a broader range and higher accuracy of feedstock compared to traditional kinetics-based methods. The KNN model demonstrated superior performance, achieving a correlation coefficient greater than 0.998 and an RMSE of 0.64. These findings provide valuable insights for engineers and practitioners, facilitating the efficient design and operation of pyrolysis units.The selectivity exhibited a notable increase from 2.46 to 5.27. This improvement in selectivity can primarily be attributed to the significantly higher increase in the solubility coefficient of CO2 compared to that of CH4.

    Keywords: Machine Learning, Prediction, Pyrolysis, Yield, Reaction Kinetics, Biomass
  • Arsalan Parvareh *, Zahra Bazazzadeh Pages 78-93

    In the current research, heat transfer within a pilot scale shell and tube heat exchanger is investigated. The heat exchanger consist of a shell and five copper tubes. Water as the cold stream and Boehmite-water nanofluid as the hot stream passes through the shell and tube side, respectively. The effect of nanofluid concentration (0.35, 0.7, and 1.5 %wt.), volume flowrate of the cold stream (0.6, 3, and 6 L/min), and the inlet temperature of the hot stream (40, 50, 60  were investigated on the overall heat transfer coefficient. Moreover, the computational fluid dynamics (CFD) modeling of heat transfer within the pilot scale was performed to study the hydrodynamics of flow inside the heat exchanger. The experimental results and CFD predictions indicates that as the concentration of the nanofluid increases, the overall heat transfer coefficient will increase. This can be attributed to higher thermal conductivity of nanoparticles and the Brownian motion of the particles in the base fluid. Moreover, when the volume flowrate of the fluid increases, Reynolds number will increase, which cause the convection heat transfer coefficient and consequently the overall heat transfer coefficient to be enhanced. Also, at higher inlet temperature of the hot fluid, higher overall heat transfer coefficient was resulted. The maximum deviation between the overall heat transfer coefficients evaluated base on the CFD predictions and its value based on experimental measurements was 16.7%. This proves the ability of CFD technique in pursuing the experimental data. CFD simulation provide a meaningful knowledge about the hydrodynamics of each stream in the heat exchanger, which help us to optimize the performance of heat exchanger.

    Keywords: Boehmite-Water Nanofluid, Shell, Tube Heat Exchanger, CFD Modeling, Heat Transfer Coefficient