Chemical Engineering Research & Design最新文献

Sliver powder is the most common and extensively utilized precious metal powder in electronics, primarily for electronic paste. Herein, micron-sized spherical silver powder was synthesized via a liquid phase reduction method employing silver nitrate as the source of silver and ascorbic acid as the reducing agent in a confined impinging jet reactor (CIJR). The impact of the molar ratio between silver nitrate and ascorbic acid, the flow rate, and the temperature on the particle size of silver powder was investigated. The optimal process conditions for silver powder are as follows: maintain a molar ratio of 1:1 and control the feeding rate at 10 ml/min while operating at 50 ° C. The confined impinging jet reactor offers enhanced control over reaction conditions during the synthesis of silver powder, surpassing the capabilities of traditional batch reactors. The aforementioned optimized methodology was employed to successfully synthesize uniform and spherical silver powder (with an aspect ratio approaching 1) in the low Reynolds number jet, resulting in an average particle size of d₅₀ = 0.83 μm and a standard deviation of 0.07, without the addition of dispersant. The synthesis method presented here improves the performance of silver powder, simplifies the production process, reduces energy consumption, and minimizes waste generation. These advances yield significant environmental and economic benefits. In the future, with the continuous development and optimization of microreactor technology, this synthesis method is anticipated to play a more prominent role in the commercial-scale production and application of micrometer-sized silver powder.

In this study, a rotational flow device (rotational blade) is developed and installed in the upstream of the particle inlet with the aim of improving the efficiency and capacity of pneumatic conveying. Firstly, this study analyzed the energy-saving effect of rotational flow based on the pressure drop and power consumption. The results shown that the optimal velocity can be reduced by a maximum of 18.7 % and the power consumption coefficient can be reduced by a maximum of 9.8 %. Furthermore, the distributions of particle concentration, velocity and pulsation velocity are analyzed by using electrical capacitance tomography (ECT) and particle image velocimetry (PIV) system. It is found that the particle velocity and velocity pulsation intensity for rotational flow are higher, and they have the ability to enhance particle suspension. Then, the power spectrum of the particle pulsation velocity shown that the rotational flow exhibited higher peak value at lower frequencies, indicating the particles are not easily deposited at pipe bottom. Finally, the auto-correlation of particle pulsation velocity indicated that the particle motion is more stable and has a longer period at low particle concentrations. The skewness factor and probability density function of particle pulsation velocity indicated that the use of rotational blades makes the particle pulsation velocity to deviate from the Gaussian distribution.

The utilization of decentralized micro gas turbine combined heat and power (MGT-CHP) units is considered as a prospective technique in power generation due to its high levels of fuel utilization rates and low emissions. However, the inherent strong coupling and complex timescale multiplicity make it challenging to realize optimal operation. To this end, this paper first establishes a precise mechanism model to attain a thorough understanding of the system properties. By conducting singular perturbation theory, the complex nonlinear system is decomposed into a fast power subsystem and a slow heat subsystem. Then, a dual-time-scale zone economic model predictive control (D-ZEMPC) algorithm, which is comprised of a fast EMPC and a slow EMPC, is applied to achieve dynamic synergy between heat and power supply by actively coordinating the two sub-controllers. Moreover, a zone tracking method is introduced for room temperature control, thereby yielding increased freedom in balancing the economic profits and thermal comfort. The simulation results in three scenarios along with the qualitative and quantitative discussions show that compared with the other two centralized EMPC algorithms, the proposed D-ZEMPC can significantly alleviate computational loads and reduce the simulation time by over 64.5 % while maintaining required thermal comfort with minimum fuel consumption.

Granular filter media are integral to sustainable drainage systems (SuDS) for their efficiency in removing pollutants from urban runoff. This study focuses on understanding the filtration processes within these media by combining a pilot experimental study with a modeling approach. The experimental study involved characterizing the physical and hydraulic properties of various granular filter media materials, including sand, pea-gravel, gravel, and geotextile membranes. Three laboratory-scale stormwater filtration rigs were tested to evaluate the filter media's pollutant removal capacity and hydraulic performance. This work presents a phenomenological model that predicts the spatial variation in the concentrations of stormwater and urban runoff substances, specifically nitrate ions (NO₃^-), phosphate ions (PO₄^3-), chemical oxygen demand (COD), and suspended solids, by studying their concentration profiles. The stormwater quality model was used to predict the concentration profiles for stormwater with an average inflow consisting of 2.9 mg/L nitrates, 3.4 mg/L phosphate ions, 225 mg/L COD, and 3.3 mg/L of suspended solids. The predicted outlet concentrations matched well with measured experimental data. The results showed that adding geotextile membranes to a granular filter significantly improves its ability to adsorb dissolved species for stormwater applications. This research highlights the importance of understanding the physical and hydraulic properties of granular filter media and their impact on stormwater pollutant removal efficiency. The developed model can assist in the design and optimization of stormwater treatment systems by predicting the performance of different filter media materials, allowing for informed decision-making and improved system functionality.