Search

In this study, the gas−particle flow field in a real-size industrial-scale micron air classifier manufactured by Phenikaa Group using 3D transient simulations with the FWC-RSM–DPM (Four-Way Coupling-Reynold Stress Model-Discrete Phase Model) in ANSYS Fluent 2022 R2 and with the assistance of High-Performance Computing (HPC) systems is explored. A comparison among three coupling models is carried out, highlighting the significant influence of the interactions between solid and gas phases on the flow field. The complex two-phase flow, characterized by the formation of multiple vortices with different sizes, positions, and rotation directions, is successfully captured on the real-size model of the classifier. Additionally, analyzing the effects of the vortices on the flow field provides a comprehensive understanding of the gas–solid flow field and the classification mechanism. The effect of the outlet mass airflow rate is also investigated. The classifier's Key Performance Indicators (KPIs: d50, K, η, ΔP) and the constrained condition of the particle size distribution curve of the final product are used to evaluate the classification efficiency. The contributions of this work are as follows: (i) a simulation analysis of a real-size industrial-scale classifier is conducted that highlights its advantages over a lab-scale one; (ii) a comparison is conducted among three coupling models, showing the advancement of four-way coupling in providing accurate results for simulations of interactions between the gas phase and particles; and (iii) the particle size distribution curve performances of a classified product under different simulation models and outlet airflow rates are addressed, from which optimal parameters can be selected in the design and operation processes to achieve the required efficiency of an air classifier. [ABSTRACT FROM AUTHOR]

In this work, electrodeposition and electronucleation of cobalt on a glassy carbon electrode from CoCl2·6H2O dissolved in ethylene glycol-based solutions (with and without NaCl as supporting electrolyte) were studied. Cyclic voltammetry and chronoamperometry have shown that cobalt can be easily electrodeposited from these nonaqueous solvents through one-step of two electrons with high cathodic efficiencies and insignificant effect of the side reaction. The analyses of current density transients were performed using the three-dimensional nucleation and diffusion-controlled growth model of Co metallic nuclei, to derive kinetic parameters such as nucleation frequency, A, density number of active sites, N0, and diffusion coefficient, D. Characterization methods, such as scanning electron microscopy, energy-dispersive X-ray spectroscopy, Raman spectroscopy, and X-ray diffraction, verified the presence of high-purity (electrodeposited) Co nanoparticles with the hexagonal close-packed structure on the electrode surfaces. It was found that the supporting electrolyte influenced the morphology and size distribution of Co nanoparticles and their preferred growth along the electrodeposition plane. Finally, linear sweep voltammetric curves indicated a strong electrocatalytic activity of cobalt electrodeposited from ethylene glycol-based solutions towards hydrogen evolution reaction in acidic and alkaline solutions, even better than platinum for the case of cobalt directly electrodeposited from ethylene glycol. [ABSTRACT FROM AUTHOR]

In this paper, an Artificial Intelligence (AI) model is constructed for the behavior prediction, i.e. Young's modulus, of polymer/carbon-nanotube (CNTs) composites. The AI is proposed to overcome the difficulties when studying the properties of novel composite materials, for example the time-consuming of experimental studies of resource-consuming of other numerical methods. Artificial Neural Network (ANN) model was chosen and optimized in architecture based on a parametric study. The main objective of this study is to firstly confirm that the proposed AI method performs well for nanocomposites and it can then be optimized in terms of computational time and resources in further studies. The obtained results have shown that the proposed model exhibits great performance in both training and testing phases, where the correlation coefficient is 0.986 for training part and 0.978 for the testing part. [ABSTRACT FROM AUTHOR]

In this study, oil was converted to biodiesel via transesterification reaction under the presence of CaO at the laboratory. This aims to evaluate and compare the influence of Jatropha oil with commercial diesel (CD) fuel on the engine operating characteristics. Secondly, this study is to compare engine performance and temperature characteristics of cooling water and lubricant oil under various engine operating conditions of the test engine fueled by Jatropha oil and CD. The results indicated that the engine torque of the engine running with Jatropha oil dropped from 0.2 kW to 0.3 kW at all speeds and its brake specific fuel consumption (BSFC) increased at almost every speed due to the low heating value and high viscosity of the Jatropha fuel. The BTEs of the engines fuelled with Jatropha oil was 0.3%, 1.0%, 0.6%, 0.7%, 1.1%, 1.6%, and 1.0% smaller than the BTEs of the engine when varied speed from 1000 rpm to 2200 rpm. However, the difference was trivial. Also, Jatropha oil contributed to reducing the oil temperature of lubricant and cooling water. [ABSTRACT FROM AUTHOR]