Theoretical Foundations of Chemical Engineering最新文献

Arsenic is a harmful element that widely exists in various non-ferrous metal minerals including copper. With the depletion of free and low arsenic copper ores, containing arsenic copper ore has become an important mineral resource for copper smelting and processing. In the smelting and extraction of copper minerals, arsenic poses a serious hazard to the environment. Therefore, the safe and effective removal arsenic plays a crucial role in copper smelting and processing, and it has a great significance in promoting the green and healthy development of the copper industry. This review summarized the resource characteristics of arsenic containing copper minerals, systematically analyzed current smelting progress of containing arsenic copper minerals, and searched for the difficult and key points existed in pyrometallurgical and wet processes for containing arsenic copper mines. The reasons for difficult point formation were explored in treating arsenic containing copper minerals. The pyrometallurgical roasting followed by high-temperature filtration is documented to be an important development direction for smelting and processing of arsenic containing minerals.

A preliminary set of Taguchi-designed experiments utilizing three different fluids (water, 50 : 50 ethylene glycol-water mixture, and 0.6 M sodium chloride solution) were conducted and its experimental results were used to produce an irradiance-based correlation via dimensional analysis. A solar water heating system integrated with its thermal radiation source and flat plate collector as a receiver was used for this experiments. Conducted dimensional analysis, which includes the thermophysical properties of the heat transfer fluid, amount of irradiance received by a collector, and distance between both receiver and source. The resulting dimensionless correlation was a function of the Reynolds number and a newly proposed dimensionless number, referred to as the SRY number. The dimensionless constant and exponents were determined using experimental data. The proposed correlation was found to be in close agreement with the existing correlation.

In this research, the Mn–Ni–Co doped APSO-34 nanostructured materials were studied in the C3H8-assisted reduction of NO_x. The MeAPSO-34 samples were prepared via ultrasound-assisted hydrothermal design upon the isomorphous substitution of transition metal ions into the crystalline lattice. Several characterization techniques such as XRD, FESEM, EDX dot-mapping, BET-BJH, FTIR and TPD-NH3 were used for study the properties of the catalysts. The XRD patterns indicated that the rate of nucleation and crystal growth were different for incorporating the various dopant ions in the CHA framework. The entrance of Mn and Co into the silicoaluminophosphate framework led to the formation of small particles with uniform distribution. Based on the acidity results, the MnCoAPSO-34 molecular sieve favored the increase in the density and strength of acid sites. Furthermore, it was found that the strength and density of acid sites may have a clear effect on catalytic NOx reduction. Therefore, the MnCoAPSO-34 zeolite showed the maximum conversion of NO_x to N₂ (76%) at reaction temperature of 450°C. Moreover, the mechanism of MeAPSO-34 synthesis and also a set of reaction stages for propane-assisted NO_x reduction based on the obtained data were presented.

The effectiveness of fluid mixing in a reactor is crucial for the success of chemical reactions. In this paper, we propose a novel multi-phase flow reactor for continuous flow technology and employ computational fluid dynamics (CFD) to optimize the mixing efficiency for a liquid-liquid system. The uniformity index and phase boundary area per unit volume (custom parameters representing mixing efficiency) are used to characterize the mixing effects of the fluid. We investigate the impact of stirring paddle structure, rotation speed, and feed flow rate on fluid mixing. The numerical simulation results demonstrate that employing multiple stirring paddles enhances the mixing effects of the fluid, but there is an upper limit to this improvement. Increasing the rotation speed improves fluid mixing, but excessively high speeds generate a strong centrifugal effect. Effectively enhancing fluid mixing can be achieved by reducing the feed flow rate to prolong the reaction time. These findings are valuable for the application of multi-phase flow reactor.

The economy based on hydrogen has been proposed as a replacement for the unsustainable economy based on fossil fuels. The development of cost-effective and environmentally friendly hydrogen production technologies, which are crucial for the hydrogen economy, is currently being researched. Utilizing aluminum or its similar metal to convert water as well as hydrocarbons in the form of hydrogen is one of the more promising methods for producing hydrogen. The limitations and difficulties in commercializing various aluminum-based hydrogen production techniques are discussed in this research. Additionally, a fresh idea for the cogeneration of electricity and hydrogen is addressed. The association between the evaluated rate constant being a function of the mass of aluminum and alkali concentration is predicted using a multivariable regression analysis.

In this paper, the local vortex flows for oil transported through main pipelines are analyzed on the basis of von Kármán vortex streets. The vortex flow analysis is based on computational fluid dynamics modeling using the Ansys Fluent software. The simulation of local vortex flows is based on the shear-stress transport (SST) model, representing a combination of the k–ε and k–ω turbulence models. It is proven that, for specified oil-flow parameters and characteristics, it is possible to generate local vortex flows in the center of a pipeline. The estimation of hydraulic drag in the zone of the von Kármán vortex streets indicates that it can be decreased. On the other hand, the analysis of overall pressure losses evidences that form drag losses are predominant in the case of flow around vortex-generating devices. Therefore, it becomes necessary to search for other methods to induce local vortex flows or overcome drag losses by using the resources of multifunctional units in oil-transport processes.

Organic photovoltaic cells are electronic devices that convert sunlight into electricity. To this end, the number of studies on organic photovoltaic cells (OVCs) is growing, and this trend is expected to continue. Computational studies are still needed to verify and prove the capability of CVOs, specifically the nanometer molecule PCBM, based on successful experimental results. In this paper, we present a theoretical and computational investigation of PCBM and PC71BM derivatives using the DFT method. On this basis, we employ independent and time-dependent density theories. HOMO, LUMO and GAPH-L energies, ionization potentials and electronic affinity are determined and found to be in agreement with experiments. Using DFT theory based on B3LYP and M062X methods with bases 6-31G (d,p) and 6-311G (d), calculations show that the most efficient acceptors are presented in the group of PC71BM derivatives and are in substantial agreement with experiments. The geometries of the structures are optimized by Gaussian 09.

In this study, the gasoline yield in the methanol-to-gasoline (MTG) process was modeled using artificial neural network (ANN) and multivariate polynomial regression (MPR) techniques. The ANN trained using the Levenberg–Marquardt (LM) method and having three neurons in the hidden layer was the most accurate at predicting gasoline yield (R² = 0.993 and RMSE = 0.024). Therefore, this network was used to investigate the influence of operational conditions such as pressure, weight hourly space velocity (WHSV), temperature, and the average particle size of the Zeolite Socony Mobil–5 (ZSM-5) catalyst on the gasoline yield. Then, the particle swarm optimization (PSO) and genetic algorithm (GA) were used to approach the best operating parameters and catalyst size to get the most gasoline yield. The mentioned neural network was used as a fitness function in the optimization algorithms. The optimization results showed that at a pressure of 1 bar, a temperature of 400°C, a WHSV equal to 1 h^–1, and a particle size of 1466 nm, the maximum gasoline yield is equivalent to 45.43.

This study aims to optimize curing conditions and delays the curing time by mixing nanoparticles of different sizes and types in commercially available epoxy. To do this, the isothermal curing kinetics of epoxy containing TiO₂, Al₂O₃, and graphene nanoplatelets (GNP) at variable ratios determined in the literature are investigated through differential scanning calorimetry (DSC). DSC measurements are then carried out to examine in detail the curing reactions of epoxy–TiO₂, epoxy–Al₂O₃, and epoxy–GNP systems during isothermal curing. The Kamal–Sourour kinetic model best expresses the curing of the epoxy–nanoparticle systems for DSC. The lowest activation energies during curing for Al₂O₃, TiO₂, and GNP are 21.88, 11.12, and 9 kJ/mol, respectively. The most suitable model for transition to a fully cured structure is observed in GNP.

The article considers the interaction mechanisms of nanocomplexes of various structures with the surface of pore spaces in the filtration flow of polymer solutions, and gives a comparative analysis of the influence of these mechanisms on the behavior of the porosity and permeability properties of an oil-saturated reservoir. It is shown that using polymers with hyperbranched nanoaggregates enhances the efficiency of oil recovery of productive reservoirs.