Mohammad Salehi Maleh
@modares.ac.ir
Department of Process Engineering/Faculty of Chemical Engineering
Tarbiat Modares University
RESEARCH, TEACHING, or OTHER INTERESTS
Chemical Engineering, Filtration and Separation, Environmental Engineering, Water Science and Technology
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Mohsen Shayanmehr, Sepehr Aarabi, Ahad Ghaemi, and Alireza Hemmati
Springer Science and Business Media LLC
Maryam Helmi, Bentolhoda Chenarani, Ahad Ghaemi, and Alireza Hemmati
Elsevier BV
Fatemeh Aminian and Alireza Hemmati
Elsevier BV
Samira Ahmadzadeh and Alireza Hemmati
Elsevier BV
Sepideh Moradi Haghighi, Alireza Hemmati, Hamidreza Moghadamzadeh, Ahad Ghaemi, and Nahid Raoofi
Springer Science and Business Media LLC
AbstractBurning fossil fuels causes toxic gas emissions to increase, therefore, scientists are trying to find alternative green fuels. One of the important alternative fuels is biodiesel. However, using eco-friendly primary materials is a main factor. Sustainable catalysts should have high performance, good activity, easy separation from reaction cells, and regenerability. In this study, to solve the mentioned problem NaOH@Graphene oxide-Fe3O4 as a magnetic catalyst was used for the first time to generate biodiesel from waste cooking oil. The crystal structure, functional groups, surface area and morphology of catalyst were studied by XRD, FTIR, BET, and FESEM techniques. The response surface methodology based central composite design (RSM-CCD) was used for biodiesel production via ultrasonic technique. The maximum biodiesel yield was 95.88% in the following operation: 10.52:1 molar ratio of methanol to oil, a catalyst weight of 3.76 wt%, a voltage of 49.58 kHz, and a time of 33.29 min. The physiochemical characterization of biodiesel was based to ASTM standard. The magnetic catalyst was high standstill to free fatty acid due to the five cycle’s regeneration. The kinetic study results possess good agreement with first-order kinetics as well as the activation energy and Arrhenius constant are 49.2 kJ/min and 16.47 * 1010 min−1, respectively.
Neshat Rahimpour, Hossein Bahmanyar, Alireza Hemmati, and Mehdi Asadollahzadeh
Springer Science and Business Media LLC
AbstractA new type of Tenova pulsed extraction column was introduced in 2017. It is the newest generation of pulsed columns. Due to the internal equipment of this column and the lack of moving parts and the simplicity and speed of repairs and maintenance, it has been the focus of researchers in recent years. No correlations for predicting the mean drop size and drop size distribution of the Tenova column have been reported. The Sauter mean drop diameter and drop size distribution are investigated for a Tenova pulsed column with a diameter and an active height of 7.4 and 73 cm, respectively. Three standard chemical systems of isobutyl acetate-water, isobutanol-water, and toluene-water have been used. The effects of pulse intensity, dispersed and continuous phase flow rates have been taken into account. In each experiment, 200–300 drops have been analyzed in a total of 10,000 drops. The investigation covered a spectrum of physical properties, notably surface tension (within a range of 1.75–36 mN/m). Operating conditions including pulse intensity (in the range of 0.2–2 cm/s) and the flow rate of continuous and dispersed phases (in the range of 8–30 L/h) have been investigated. Methods based on artificial intelligence (AI) such as multilayer perceptron neural networks and gene expression programming were combined with a dimensional analysis approach to provide a new approach to estimating the mean drop diameter (d32). Experimental results have been compared with the equations found by other researchers in similar columns. The variation of drop size distribution has also been experimentally obtained.Methods based on artificial intelligence (AI) such as multilayer perceptron neural networks and gene expression programming were combined with a dimensional analysis approach to provide a new approach to estimating the mean drop diameter (d32). Experimental results have been compared with the equations found by other researchers in similar columns. The variation of drop size distribution has also been experimentally obtained.
Maryam Helmi, Zahra Khoshdouni Farahani, Alireza Hemmati, and Ahad Ghaemi
Springer Science and Business Media LLC
AbstractBurning fossil fuels releases toxic gases into the environment and has negative effects on it. In this study, Persian gum@Graphene oxide (Pg@GO) was synthesized and used as a novel adsorbent for CO2 capture. The characterization of materials was determined through XRD, FTIR, FE-SEM, and TGA analysis. The operating parameters including temperature, Pressure, and adsorbent weight were studied and optimized by response surface methodology via Box–Behnken design (RSM-BBD). The highest amount of CO2 adsorption capacity was 4.80 mmol/g, achieved at 300 K and 7.8 bar and 0.4 g of adsorbent weight. To identify the behavior and performance of the Pg@GO, various isotherm and kinetic models were used to fit with the highest correlation coefficient (R2) amounts of 0.955 and 0.986, respectively. The results proved that the adsorption of CO2 molecules on the adsorbent surface is heterogeneous. Based on thermodynamic results, as the value of ΔG° is − 8.169 at 300 K, the CO2 adsorption process is exothermic, and spontaneous.
Zohreh Khoshraftar, Ahad Ghaemi, and Alireza Hemmati
Springer Science and Business Media LLC
AbstractChemical vapor deposition was used to produce multi-walled carbon nanotubes (MWCNTs), which were modified by Fe–Ni/AC catalysts to enhance CO2 adsorption. In this study, a new realm of possibilities and potential advancements in CO2 capture technology is unveiled through the unique combination of cutting-edge modeling techniques and utilization of the recently synthesized Fe–Ni/AC catalyst adsorbent. SEM, BET, and FTIR were used to analyze their structure and morphology. The surface area of MWCNT was found to be 240 m2/g, but after modification, it was reduced to 11 m2/g. The modified MWCNT showed increased adsorption capacity with higher pressure and lower temperature, due to the introduction of new adsorption sites and favorable interactions at lower temperatures. At 25 °C and 10 bar, it reached a maximum adsorption capacity of 424.08 mg/g. The optimal values of the pressure, time, and temperature parameters were achieved at 7 bar, 2646 S and 313 K. The Freundlich and Hill models had the highest correlation with the experimental data. The Second-Order and Fractional Order kinetic models fit the adsorption results well. The adsorption process was found to be exothermic and spontaneous. The modified MWCNT has the potential for efficient gas adsorption in fields like gas storage or separation. The regenerated M-MWCNT adsorbent demonstrated the ability to be reused multiple times for the CO2 adsorption process, as evidenced by the study. In this study, a feed-forward MLP artificial neural network model was created using a back-propagation training approach to predict CO2 adsorption. The most suitable and efficient MLP network structure, selected for optimization, consisted of two hidden layers with 25 and 10 neurons, respectively. This network was trained using the Levenberg–Marquardt backpropagation algorithm. An MLP artificial neural network model was created, with a minimum MSE performance of 0.0004247 and an R2 value of 0.99904, indicating its accuracy. The experiment also utilized the blank spreadsheet design within the framework of response surface methodology to predict CO2 adsorption. The proximity between the Predicted R2 value of 0.8899 and the Adjusted R2 value of 0.9016, with a difference of less than 0.2, indicates a high level of similarity. This suggests that the model is exceptionally reliable in its ability to predict future observations, highlighting its robustness.
Alireza Hemmati, Mehdi Asadollahzadeh, and Rezvan Torkaman
Springer Science and Business Media LLC
AbstractRecently, efficient techniques to remove indium ions from e-waste have been described due to their critical application. This paper illustrates the recovery of indium ions from an aqueous solution using a liquid membrane. CyphosIL 104 described the excellent potential for the extraction of indium ions. Evaluation of the five process parameters, such as indium concentration (10–100 mg/L), carrier concentration (0.05–0.2 mol/L), feed phase acidity (0.01–3 mol/L), chloride ion concentration (0.5–4 mol/L) and the stripping agent concentration (0.1–5 mol/L) were conducted. The interactive impacts of the various parameters on the extraction efficiency were investigated. The response surface methodology (RSM) and artificial neural network (ANN) were employed to model and compare the FS-SLM process results. RSM model with a quadratic equation (R2 = 0.9589) was the most suitable model for describing the efficiency. ANN model with six neurons showed a prediction of extraction efficiency with R2 = 0.9860. The best-optimized data were: 73.92 mg/L, 0.157 mol/L, 1.386 mol/L, 2.99 mol/L, and 3.06 mol/L for indium concentration, carrier concentration, feed phase acidity, chloride ion concentration, and stripping agent concentration. The results achieved by RSM and ANN led to an experimentally determined extraction efficiency of 93.91%, and 94.85%, respectively. It was close to the experimental data in the optimization condition (95.77%). Also, the evaluation shows that the ANN model has a better prediction and fitting ability to reach outcomes than the RSM model.
Farnoush Fathalian, Hamidreza Moghadamzadeh, Alireza Hemmati, and Ahad Ghaemi
Springer Science and Business Media LLC
AbstractThis study was deeply focused on developing a novel CTS/GO/ZnO composite as an efficient adsorbent for CO2 adsorption process. To do so, design of experiment (DOE) was done based on RSM-BBD technique and according to the DOE runs, various CTS/GO/ZnO samples were synthesized with different GO loading (in the range of 0 wt% to 20 wt%) and different ZnO nanoparticle’s loading (in the range of 0 wt% to 20 wt%). A volumetric adsorption setup was used to investigate the effect of temperature (in the range of 25–65 °C) and pressure (in the range of 1–9 bar) on the obtained samples CO2 uptake capability. A quadratic model was developed based on the RSM-BBD method to predict the CO2 adsorption capacity of the composite sample within design space. In addition, CO2 adsorption process optimization was conducted and the optimum values of the GO, ZnO, temperature, and pressure were obtained around 23.8 wt%, 18.2 wt%, 30.1 °C, and 8.6 bar, respectively, with the highest CO2 uptake capacity of 470.43 mg/g. Moreover, isotherm and kinetic modeling of the CO2 uptake process were conducted and the Freundlich model (R2 = 0.99) and fractional order model (R2 = 0.99) were obtained as the most appropriate isotherm and kinetic models, respectively. Also, thermodynamic analysis of the adsorption was done and the ∆H°, ∆S°, and ∆G° values were obtained around − 19.121 kJ/mol, − 0.032 kJ/mol K, and − 9.608 kJ/mol, respectively, indicating exothermic, spontaneously, and physically adsorption of the CO2 molecules on the CTS/GO/ZnO composite’s surface. Finally, a renewability study was conducted and a minor loss in the CO2 adsorption efficiency of about 4.35% was obtained after ten cycles, demonstrating the resulting adsorbent has good performance and robustness for industrial CO2 capture purposes.
Forough Bahmei, Alireza Hemmati, Ahad Ghaemi, and Maryam Bahreini
Elsevier BV
Maryam Helmi, Alireza Hemmati, and Ahad Ghaemi
Elsevier BV
Parnian Masuodi, Fatemeh Bahmanzadegan, Alireza Hemmati, and Ahad Ghaemi
Elsevier BV
Shanli Nezami, Farzad Moazami, Maryam Helmi, Alireza Hemmati, and Ahad Ghaemi
Springer Nature Singapore
Shanli Nezami, Farzad Moazami, Ahad Ghaemi, and Alireza Hemmati
Springer Nature Singapore
Armin Ghobadi Moghadam and Alireza Hemmati
Springer Science and Business Media LLC
AbstractThis work presents a modified polyvinylidene fluoride (PVDF) ultrafiltration membrane blended with graphene oxide-polyvinyl alcohol-sodium alginate (GO-PVA-NaAlg) hydrogel (HG) and polyvinylpyrrolidone (PVP) prepared by the immersion precipitation induced phase inversion approach. Characteristics of the membranes with different HG and PVP concentrations were analyzed by field emission scanning electron microscopy (FESEM), Atomic force microscopy (AFM), contact angle measurement (CA), and Attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR). The FESEM images showed an asymmetric structure of the fabricated membranes, and possessing a thin dense layer over the top and a layer finger-like. With increasing HG content, membrane surface roughness increases so that highest surface roughness for the membrane containing 1wt% HG is with a Ra value of 281.4 nm. Also, the contact angle of the membrane reaches from 82.5° in bare PVDF membrane to 65.1° in the membrane containing 1wt% HG. The influences of adding HG and PVP to the casting solution on pure water flux (PWF), hydrophilicity, anti-fouling ability, and dye rejection efficiency were evaluated. The highest water flux reached 103.2 L/m2 h at 3 bar for the modified PVDF membranes containing 0.3 wt% HG and 1.0wt% PVP. This membrane exhibited a rejection efficiency of higher than 92%, 95%, and 98% for Methyl Orange (MO), Conge Red (CR), and Bovine Serum Albumin (BSA), respectively. All nanocomposite membranes possessed a flux recovery ratio (FRR) higher than bare PVDF membranes, and the best anti-fouling performance of 90.1% was relevant to the membrane containing 0.3 wt% HG. The improved filtration performance of the HG-modified membranes was due to the enhanced hydrophilicity, porosity, mean pore size, and surface roughness after introducing HG.
Shaparak Mirzaei, Fatemeh Ardestani, Ahad Ghaemi, Alireza Hemmati, and Mansour Shirvani
Elsevier BV
Neshat Rahimpour, Hossein Bahmanyar, Alireza Hemmati, and Mehdi Asadollahzadeh
Elsevier BV
Maryam Helmi, Mohammad Amin Sobati, and Alireza Hemmati
Springer Science and Business Media LLC
Maryam Helmi, Farzad Moazami, Alireza Hemmati, and Ahad Ghaemi
Elsevier BV
Fatemeh Ardestani, Ahad Ghaemi, Alireza Hemmati, Jaber Safdari, and Vahid Rafiei
Elsevier BV
Maryam Helmi, Farzad Moazami, Ahad Ghaemi, and Alireza Hemmati
Elsevier BV
Alireza Hemmati, Mehdi Asadollahzadeh, Mehdi Derafshi, Mohammad Salimi, MohammadHossein Mahabadi Mahabad, and Rezvan Torkaman
Elsevier BV
Fatemeh Ardestani, Ahad Ghaemi, Jaber Safdari, and Alireza Hemmati
Elsevier BV