Abstract Mixing efficiency is critical for mass and heat transfer, microbial activity, and process stability in anaerobic reactors. In this study, a three-dimensional CFD model was developed to investigate the coupled effects of impeller configuration, rotational speed, feeding position, and fluid viscosity on hydrodynamics and mixing performance in an anaerobic reactor. The results showed that increasing rotational speed enhanced momentum transfer from the impeller to the bulk fluid, producing a more uniform velocity field and a larger effective mixing region. Tracer simulations indicated that feeding position strongly influenced mixing performance because the initial tracer pathway depended on its hydrodynamic connectivity with the impeller-driven circulation loop. In addition, higher viscosity increased flow resistance, weakened bulk circulation, and prolonged mixing time. Overall, mixing behavior was governed by the combined effects of impeller design, operating conditions, feeding location, and fluid rheology. Quantitative comparison further showed that mixing performance was not determined by local velocity magnitude alone, but by the coordinated distribution of radial and axial transport within the reactor. These findings provide mechanistic insight into flow–mixing interactions and offer guidance for reactor design, feeding position optimization, and energy-efficient operation of anaerobic digestion systems.
Abstract The esterification reaction is essential for producing esters widely used in chemical, pharmaceutical, and biofuel applications. This study develops and evaluates a computational methodology for modeling heterogeneous esterification kinetics in an isothermal batch reactor, implemented independently in Scilab, Python, and R. The kinetic formulation was based on a simplified Eley–Rideal-type approach, considering a reversible reaction and water inhibition through an adsorption term. The resulting system of ordinary differential equations was used to estimate the apparent kinetic coefficient ( k d ), water adsorption constant ( K W ), and equilibrium constant ( K eq ) from 20 experimental reaction profiles obtained from independent literature studies. Parameter estimation was performed using nonlinear estimation procedures, while the ODE systems were solved using LSODA in R and Scilab and adaptive RK45 in Python. The calculated profiles reproduced the experimental data with average coefficients of determination of 0.9908 ± 0.00399 for Scilab, 0.9905 ± 0.00399 for Python, and 0.9851 ± 0.00960 for R, indicating high consistency among the three computational approaches. Differences in estimated parameters were mainly attributed to the distinct parameter-estimation and numerical-integration procedures adopted in each implementation. Independently calculated equilibrium constants agreed well with the values incorporated into the kinetic formulation, supporting the proposed simplification. Temperature dependence was consistent with the Arrhenius relationship, while confidence ellipse analysis indicated correlation between the forward and reverse reaction parameters. Overall, the proposed methodology provides a reproducible framework for kinetic parameter estimation and simulation of heterogeneous esterification systems across different computational environments.
Hibiscus extract's active phenolic components were identified through a chemical assessment utilizing "high-performance liquid chromatography (HPLC)". The analysis demonstrated that there were seven primary phenol compounds in varying concentrations. The highest concentration was found in rutin (145.6 ppm), while the lowest was found in chlorogenic. By utilizing the antioxidant activity was measured using the "2,2-diphenyl-1-picrylhydrazyl (DPPH)" assay; the extract showed more antioxidant activity than vitamin C. Antioxidant capability and concentration are directly correlated has been established by the results, which showed that the extract's capacity decreases hydrogen peroxide concentration. Hibiscus extract was used to create a hybrid nanocomposite (ZnO/CuO) that demonstrated promising results in enhancing the bioactivity of phenolic chemicals. The DPPH assay additionally served in evaluating the nanocomposite extracts' antioxidant capacities, and the findings indicate that they were significantly more effective than vitamin C at scavenging free radicals. The ZnO/CuO nanocomposite enhanced the extract's optical and structural characteristics, according to the data, which indicate that it might have value in environmental and medical applications.
Fluorinated polymers are strategic materials with high added value; however, their industrial production is constrained by conventional screw-rod and batch autoclave reactors that suffer from extreme temperatures (>300 degrees C), severe equipment corrosion, and limited product quality. To address these limitations, this study proposes a circulating vibrated fluidized bed microwave fluorination (CVFB-MF) reactor that integrates mechanical vibration, microwave volumetric heating, and fluorine gas recycling into a single continuous unit. A coupled computational fluid dynamics (CFD) model was developed by integrating the Euler-Euler two-fluid approach with surface reaction kinetics via user-defined functions (UDFs). The kinetic theory of granular flow (KTGF) and the Gidaspow drag model were employed to close the solid-phase governing equations and characterize interphase momentum exchange, respectively. The model was rigorously validated against experimental pressure drop, bed expansion ratio, and radial solid volume fraction data, with relative deviations within +/- 15 %. Parametric investigations reveal that mechanical vibration effectively suppresses bubble coalescence and enhances particle mixing, while microwave heating ensures rapid and uniform temperature elevation across the bed. The Damk & ouml;hler number analysis indicates a transition from mass-transfer-controlled behavior in the lower dense-phase region to reaction-controlled behavior in the upper dilute-phase region along the bed height, and an optimal operating window was identified through multi-parameter response surface analysis (conversion similar to 88 % at f = 50-60 Hz and U p = 1.0-1.3 m s-1). The established coupled model provides a scalable theoretical framework for the design, optimization, and scale-up of next-generation fluorination reactors.
Metal gas arc welding (GMAW) is a widely used welding process known for its efficiency and versatility in joining metals. The quality and efficiency of GMAW depends on the selection and optimization of process parameters. This research aims to select suitable process parameters for welding AISI 1006 steel by metal gas arc welding based on tensile test. For this, voltage (V), current (A), and weld speed (inch/min) welding process parameters have been taken into account. Also, modern decision-making software spice logic AHP software has been used. At the end of this research, 29 V, 80 A, and 17 inch/min has been found as suitable process parameters for welding AISI 1006 steel. Additionally, challenges and future trends in GMAW process parameter optimization are highlighted.
