pouya Es'haghi; Alireza Mohammadi; Keivan Shayesteh; Hassan Seddighi
Abstract
Ethanol (EtOH) purification is a pivotal research pursuit, with liquid-liquid extraction emerging as a significant purification methodology. This study focuses on utilizing benzene solvent for EtOH purification and investigates the liquid-liquid equilibrium (LLE) within three-component systems comprising ...
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Ethanol (EtOH) purification is a pivotal research pursuit, with liquid-liquid extraction emerging as a significant purification methodology. This study focuses on utilizing benzene solvent for EtOH purification and investigates the liquid-liquid equilibrium (LLE) within three-component systems comprising EtOH, water, and benzene. Thermodynamic modeling of EtOH-benzene-water systems at temperatures of 20 °C, 30 °C, 40 °C, and 55 °C was conducted. In this paper, the equations used for predicting mole fraction include Non-Random Two-Liquid (NRTL), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multilayer Perceptron Artificial Neural Network (MLP-ANN). First, the equation parameters were optimized using the particle swarm optimization (PSO) algorithm to employ the NRTL equation Experimental data was used to train the MLP-ANN and ANFIS methods, and the same experimental datasets were used for all models. These models estimated integral components across both phases, revealing effective system control across all methodologies. However, the comparative analysis indicated the superior performance of the MLP-ANN and ANFIS methods over the NRTL model. The Root Mean Square Deviation (RMSD) errors for the NRTL, MLP-ANN, and ANFIS models were 0.0253, 0.0035, and 0.0017, respectively. These results indicate that despite the low prediction error of all three methods, the NRTL equation has the highest error, and the ANFIS method has the lowest mole fraction prediction error.
Pouya Es'haghi; Keivan Shayesteh; Hassan Seddighi
Abstract
In chemical industries, precision in calculations and process simulations is crucial. One of the most influential parameters is the molar density of a fluid under various pressure and temperature conditions. Equations of state (EOS) are common among the methods for determining molar density. Usually, ...
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In chemical industries, precision in calculations and process simulations is crucial. One of the most influential parameters is the molar density of a fluid under various pressure and temperature conditions. Equations of state (EOS) are common among the methods for determining molar density. Usually, the error resulting from predicting molar density using EOS is generally high at high temperatures and pressures due to the increased intermolecular effects. Additionally, due to the form of EOS concerning volume or molar density, calculating molar volume at specified temperature and pressure requires suitable numerical methods for root-finding. This article aims to present an effective method for estimating the molar density of benzene using two crucial machine learning methods, namely Multi-Layer Perceptron-Artificial Neural Network(MLP-ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). This study used 302 sets of experimental data to train these two methods. Additionally, another set of 60 experimental data was used to compare the errors of the methods. The Peng-Robinson (PR) equation was also employed in this article to evaluate the performance of machine learning methods better and calculate molar density. The results showed that the mean relative errors (MRE) for the MLP-ANN, ANFIS, and PR methods for the 362 data points are 0.838%, 1.791%, and 4.834%, respectively. The results demonstrated that using machine learning methods can reduce computational errors, with the error from predicting using the PR equation being almost five times that of MLP-ANN. In this article, the MLP-ANN method outperformed ANFIS due to its computational efficiency and lower error in predicting molar density.
Vahid Vahid Fard; Keivan Shayesteh; Pourya Abbasi; Mohammad Javad Khani
Abstract
Removal of cobalt from zinc electrolyte solution is one of the most important and difficult steps in zinc production using hydrometallurgy method. The impact of initial concentrations of cobalt, manganese, and Fe and amounts of potassium permanganate on the efficiency of cobalt removal by potassium permanganate ...
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Removal of cobalt from zinc electrolyte solution is one of the most important and difficult steps in zinc production using hydrometallurgy method. The impact of initial concentrations of cobalt, manganese, and Fe and amounts of potassium permanganate on the efficiency of cobalt removal by potassium permanganate from zinc electrolyte solution was investigated in this research. The results indicated that the higher the initial concentration of cobalt, manganese, and Fe is, the lower the cobalt removal amount will be; however, as the amount of potassium permanganate increases, the efficiency of cobalt removal will enhance. It was also found that, in order to make a permissible level of the dissolved cobalt, the consumption of potassium permanganate should be increased as the concentration of cobalt, Fe, and manganese increases. If the concentration of manganese is more than 500 mg/L, it can impact the reduction of the efficiency of cobalt removal to a great extent; but when the initial concentration of cobalt is high, the significance of the impact of the initial amounts of manganese would decrease. Additionally, if the manganese concentration is less than 200 mg/L, the optimal removal of cobalt (less than 2 ppm) will not occur under any circumstances. The results also indicate that if the potassium permanganate concentration is 1 g/L or lower, the Fe ions in the solution will drastically reduce the cobalt removal efficiency.
Vahid Vahid Fard; Keivan Shayesteh; Pourya Abbasi; Saeid Salimi
Abstract
One of the effective and important steps of the production of Zn in zinc factories is the purification step to remove the general impurities such as cobalt, nickel, and cadmium that can cause the creation of problems in the electrowinning process. In Iran's zinc factories, firstly, at the hot purification ...
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One of the effective and important steps of the production of Zn in zinc factories is the purification step to remove the general impurities such as cobalt, nickel, and cadmium that can cause the creation of problems in the electrowinning process. In Iran's zinc factories, firstly, at the hot purification step cobalt using the manganometry method, and then at the cold purification step nickel & cadmium using the cementation method with the help of Zn powder removed. In the aim of this study is evaluated the usability of the cementation method instead of the manganometry method. In this paper, important and effective parameters such as temperature, Zn powder dosage, mixing time, amount of trioxide antimony, copper sulfate effect, particle size, zinc ions concentration, mixing speed, and pH of zinc sulfate solution were studied and optimized. The Optimal state is obtained at 85 °C, pH= 4.5, 20 mg/L Sb2O3 concentration, 8 g/L zinc dust, 600 rpm mixing speed in 75 minutes. Decreasing the particle size of zinc dust increased the removal efficiency, but increasing the amounts of copper sulfate and zinc ions caused decreasing in efficiency. Results showed that cobalt, nickel, and cadmium removal efficiencies for the manganomery method were 99%, 0%, and 20% and for the cementation method were 99.5%, 99.7%, and 99.9%, respectively. Also, results indicated that the cementation method due to the increase of soluble zinc concentration; not being removes of manganese ions; simultaneous removal of impurities such as cobalt, nickel, and cadmium in a single step; and saving time and cost, has a higher performance rather than to manganometry method.
Pourya Abbasi; Keyvan Shayesteh; Vahid Vahidfard; Mehdi Hosseini
Abstract
Nickel is one of the metallic impurities that should be removed from the electrolyte solution before the electrowinning of zinc. This study investigated the parameters affecting the process of nickel removal in an Iranian zinc smelter plant by the response surface methodology. According to the results ...
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Nickel is one of the metallic impurities that should be removed from the electrolyte solution before the electrowinning of zinc. This study investigated the parameters affecting the process of nickel removal in an Iranian zinc smelter plant by the response surface methodology. According to the results of experiments, the optimum condition for removal of nickel was obtained at temperature of 85 °C, the residence time of 60 minutes, zinc powder of 2.5 g/l, mixing speed of 500 rpm, and pH of 5. With regards to the resulting model from the Design-Expert software, the significant parameters were concentration, residence time, and temperature, respectively.
