Enameled steel chemical reactor equipment. © gen_A@AdobeStock
Enameled steel chemical reactor equipment. © gen_A@AdobeStock
This study examines the antimicrobial properties and drying kinetics of chitosan–silver nanoparticle membranes. Chitosan exhibits limited antimicrobial properties, whereas its incorporation with silver nanoparticles (37.08 nm) enhances its efficacy by mixing chitosan and silver nitrate, followed by microwave heating at 300 W for 1 min. The solution was then microwave-dried at 100–450 W, with the highest drying rate observed at 450 W. Effective diffusivities increased with microwave power, and activation energy for chitosan–silver nanoparticle membranes was determined at 49.28 W g−1, respectively. Fourier transform infrared (FTIR) spectra showed reduced peak steepness at lower microwave power. Field emission scanning electron microscopy (FESEM) images revealed nanoparticle agglomeration and surface rupture at 300 W. Studies showed that chitosan–silver nanoparticle membranes exhibited stronger antimicrobial properties, highlighting microwave drying's potential in antimicrobial wound-healing materials.
Magnetic Fe3O4 nanoparticles (Fe3O4 NPs) were synthesized via a green method using Camellia sinensis leaf extract as a reducing and stabilizing agent. The obtained superparamagnetic nanoparticles exhibited a saturation magnetization of ∼50 emu/g and an average crystallite size of ∼20 nm, formed at 180°C over 8 h. These nanoparticles were applied as adsorbents for polyacrylamide (PAM) removal from aqueous solutions. Adsorption followed a pseudo-second-order kinetic model and fitted the Langmuir isotherm, with optimal conditions at pH 8, 50 min contact time, and 1.5 g/L dosage, achieving 99% removal efficiency. The Fe3O4 NPs were easily recovered using an external magnetic field, highlighting their potential as low-cost, eco-friendly, and reusable adsorbents for water treatment applications.
In this paper, the computational fluid dynamics–DPM method is used to simulate the gas–solid two-phase flow in cyclone separation, and the Reynolds stress turbulence model is applied to gas turbulence. The distributions of gas velocity, pressure drop, and particle flow characteristics pattern in the cyclone at different inlet angles are obtained. The simulation results show that the tangential velocity increases first and then decreases with the inlet angle from negative to positive, whereas both axial velocity and radial velocity increase with the increase of the inlet angle; in turn, the pressure drop in the cyclone decreases accordingly, and the negative inlet angle brings less pressure drop than the positive inlet angle. Meanwhile, the separation efficiency increases with the increase in particle size. More specifically, small-sized particles are not easy to be separated, medium-sized particles need a suitable inlet angle to be well separated, whereas large particles have a wider choice of inlet angles.
Traditional data-driven optimization methods using case-based reasoning (CBR) rely on heuristic similarity matching and lack probabilistic rigor, especially in complex processes like fluid catalytic cracking (FCC) with high dimensionality and uncertainty. To address these challenges, a novel data-driven framework that integrates compact posterior estimation with CBR is proposed. The method first identifies key variables affecting product yields through information-theoretic dimensionality reduction. Optimal operating parameters are then inferred using a combination of K-nearest neighbors for similarity matching and Markov Chain Monte Carlo sampling for probabilistic estimation. Industrial validation showed gasoline and total liquid yields increased by 7.31% and 6.94%, respectively, with coke yield reduced by 5.83%. This approach successfully improves computational efficiency and optimization accuracy in practical applications.
This study presents the hydrodynamic behavior of a dual-stage stirred tank, which is typically used in bioprocesses and is equipped with a Rushton turbine (RT) below and a pitched blade turbine (PBT) above. The investigation of the turbulent flow is based on numerical simulations (computational fluid dynamics, CFD) and experiments (particle image velocimetry, 2D–2C PIV). Direct CFD–PIV comparisons in dual-impeller tanks are rare. The velocity and turbulent kinetic energy vertical profiles show a high degree of agreement between the CFD and PIV data across the entire tank at 250 rpm. The power consumption calculated by CFD for the RT accounts for 78 % of the global power number. CFD post-processing confirms the presence of a pair of trailing vortices behind the RT only, with local velocities exceeding the tip speed.
The Rectisol process plays a pivotal role in syngas purification for clean coal utilization. Feedstock variations during gasification induce syngas flow and composition fluctuations, necessitating adaptive process control. This study develops both proportional–integral–derivative (PID) and model predictive control (MPC) strategies for the CO2 absorption column. The MPC controller was developed in MATLAB Simulink and innovatively integrated with Aspen Plus Dynamics through the AMSimulation interface module for co-simulation. The control strategies were evaluated under ±10 % flow and ±2 % CO2 concentration disturbances, with purified gas quality assessed through response time (RT), overshoot (OS), and the integral of absolute error multiplied by time (ITAE). The data-driven MPC strategy exhibits enhanced robustness against flow and composition variations compared to conventional PID control. The study offers actionable guidance for control system design in process industries.
Enameled steel chemical reactor equipment. © gen_A@AdobeStock
To understand the influences of heterogeneous wettability on the microchannel walls on droplet generation, this article proposes a T-junction microchannel with two distinct contact angles on the channel walls (θ1 on three walls and θ2 on the fourth wall) to investigate their effects on the droplet generation modes and sizes. A phase diagram illustrating the droplet generation mode across a wide range of θ1 and θ2 reveals that increase either contact angle induces a transition in droplet generation modes from no droplet generation to jetting, squeezing, and finally dripping. Notably, a larger θ1 is required for the stable droplet generation. Furthermore, compared to uniform wettability configuration, reducing θ2 facilitates the emergence of squeezing mode and jetting mode and yields larger droplets at fixed θ1. This study provides insights into the design and optimization of microfluidic systems for tailored droplet generation.
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