Chemical Engineering & Technology最新文献

Here, a microchannel reactor based on polytetrafluoroethylene (PTFE) capillary was successfully constructed and catalyzed 4-nitrophenol (4-NP) stably and efficiently. The reactor is based on inexpensive PTFE capillary tubes, and polydopamine is adsorbed on the inner wall of the capillary tubes by utilizing its super adhesive property. Then, the uniform and stable loading of palladium nanoparticles on the multilayer hybridized membrane is achieved in the microchannel by continuous flow using layer-by-layer self-assembly, ion exchange, and in situ reduction. Under the condition of low catalyst loading, the conversion rate of high concentration 4-NP (0.5 mM) wastewater was maintained above 98 % for 360 h, which demonstrated the excellent catalytic efficiency and good stability of the microchannel reactor. In addition, the microreactor has excellent universality. This study provides a new way to treat organic wastewater efficiently and finely.

The impact of including different mass balance equations for HDT reactions on sulfur compound removal was evaluated through reactor modeling using a previously proposed kinetic model. the model considers major HDT reactions (HDS, HDN, HDA, HGO, and HCR), with HDS divided into three lumps based on compound refractiveness. Liquid and gas phase balance equations were used to predict partial pressure and concentration profiles. Simulations with and without each mass balance equation assessed their effect on sulfur concentration predictions and hydrogen consumption. Including mass balance equation for nitrogen compounds showed the strongest effect on the prediction of product sulfur compounds.

Green ammonia as a potential clean energy source has attracted significant attention, only contributing to the effective utilization of new energy but also boosting the green transformation of the chemical industry. Effective and accurate modeling is crucial for the green ammonia process. This study innovatively introduces a machine learning method combining the adaptive immune genetic algorithm (AIGA) and random forest (RF) for the optimal modeling of the green ammonia production process. AIGA is responsible for intelligently screening key production parameters, whereas RF constructs a prediction model to predict green ammonia production yields under different feed loads. An example analysis on the Unisim software platform verifies the AIGA-RF model, which maintains high accuracy even under varying production loads, significantly outperforming other algorithm combinations. The method opens new pathways for precise control and efficiency enhancement in green ammonia production.

Wave rotor boosting technology offers significant advantages in improving the thermal cycle efficiency of propulsion systems. This article provides a comprehensive review of the recently proposed radial-flow wave rotor technology, focusing on the research progress of radial-flow wave rotor combustors and top-cycle radial-flow wave rotors for ultra-micro gas turbines. The thermal cycle characteristics, structural features, operational principles, and research achievements of these technologies are discussed. The analysis suggests that this technology is still in its developmental phase, characterized by underdeveloped foundational theories and a lack of valuable experimental data and engineering application cases. The summarized technological challenges contribute to guiding the direction of research in radial-flow wave rotor technology.

This study develops a coupled model using MATLAB and Aspen Plus to simulate the cooling-antisolvent crystallization of azelaic acid. The solubility was measured using the gravimetric method, and a crystallization kinetics model was built with the population balance equation. Kinetic parameters were obtained through experiments and used as inputs for the model. A key feature was the segmental addition of antisolvent in Aspen Plus. To address Aspen Plus limitations in dynamic solubility changes, a MATLAB-based solubility calculator was developed, feeding solubility data back to Aspen Plus. The simulation results were validated by comparing crystal size distribution data, confirming the model's accuracy. This approach provides a reliable tool for optimizing antisolvent crystallization processes.

This research provides a new structured methodology for improving diesel fuel properties through solvent extraction. Diesel fuel is analyzed using an experimental approach, including solvent selection, extraction, phase separation, and characterization. Four solvents—furfural, N-methylpyrrolidone (NMP), propylene carbonate, and acetonitrile (AN)—are tested at diesel-to-solvent ratios. Several tests are conducted. The research process involved optimizing extraction conditions and evaluating fuel quality using Fourier transform infrared (FT-IR) and gas chromatography (GC) techniques. Extraction ratio significantly improved combustion properties, reducing residual carbon, sulfur content, and viscosity. NMP is identified as the most effective solvent, with an NMP + AN mixture providing optimal performance. This research serves as a guide for systematically designing and executing research on fuel enhancement, demonstrating solvent extraction as a viable method for refining diesel fuel properties.

The research discusses the droplet formation mechanism in vibrating mesh atomizers. These atomizers, commonly used for particle generation in spray dryers or in inhalation devices, are characterized by resonance frequency, volume flow rate, droplet size distribution, and atomization efficiency. It was found out that the droplet size distribution was independent from the volume flow rate. Moreover, the atomization efficiency of this type of atomizer is comparable to that of pneumatic atomizers. The volume flow rate is modeled using the Bernoulli equation and the Hagen–Poiseuille law. Theoretical and experimental investigations concerning the droplet formation mechanism have demonstrated that the Rayleigh jet break-up can be assumed to be the mechanism responsible for droplet formation with a metal mesh atomizer.

Perovskite oxide due to their structural versatility and compositional flexibility shows potential catalytic performance for redox reactions. So, they can be one of most suitable catalysts to study their effect and mechanism of thermal decomposition of ammonium perchlorate (AP). Here, nano-dimensional perovskite oxide of LaCo_xMn_1−xO₃ (x = 0.2, 0.4, 0.6, 0.8) was synthesized by citric acid sol–gel method and applied as catalyst for thermal decomposition of AP. Structural characterization was studied using various techniques like powder X-ray diffraction (XRD), SEM-EDX, FTIR, UV–VIS spectroscopy, Raman spectroscopy, and Brunauer–Emmett–Teller (BET) analysis. DSC, DTA, TG, and DTG thermal analysis results confirm that LaCo_0.8Mn_0.2O₃ shows best catalyst performance among all. Co metal facilitates the electron transition from CB to VB to create a greater number of reactive sites on the catalyst surface to absorb NH₃ and accelerate the thermal decomposition of AP. So, it can conclude that a higher content of Co metal facilitates the better catalytic performance.

This work investigates the process of distillation purification of perfluoro(butylcyclohexane) (BCH) and concentration of perfluorodecalin (PFD) from industrial samples of BCH–PFD mixtures. According to experimental data, the main difficulty is the separation of BCH from the cis-isomer of PFD, for which distillation is ineffective. To intensify this process, a heteroazeotropic distillation method in the presence of water was proposed and implemented on a pilot-scale distillation column. This approach allows BCH to be purified from PFD up to more than 0.99 mol.fr. The advantage of the proposed method is the use of a safe separating agent—water, which is especially important due to the widespread use of BCH and PFD in medicine. Based on experimental data, the work also determined the characteristics of heteroazeotropes in the systems BCH–water, cis-PFD–water, and trans-PFD–water and relative volatility between BCH and cis-PFD in the presence of water.

Current synthesis methods for copper oxide nanoparticles (CuO NPs) use toxic chemicals, limiting their environmental and biomedical use. We used green synthesis with potato starch as a reducing and stabilizing agent, hypothesizing it would yield CuO NPs with enhanced photocatalytic and antimicrobial properties due to unique structures. X-ray diffraction (XRD) confirmed a 13.92 nm crystallite size, scanning electron microscopy (SEM) showed flake-like aggregates, and ultraviolet (UV)–Vis revealed a 2.95 eV bandgap, indicating strong optical properties. CuO NPs achieved 99.2 % degradation of Evans Blue dye in 140 min and full atrazine degradation in 160 min, showing excellent photocatalysis. Antimicrobial assays gave inhibition zones up to 26 mm for bacteria and 14 mm for fungi at 20 mg mL⁻¹. These results show potato starch–synthesized CuO NPs’ potential for environmental remediation and antimicrobial applications, offering a sustainable option for nanomaterial synthesis.