Asia-Pacific Journal of Chemical Engineering最新文献

Pulse-jet cleaning technology is commonly used during the cleaning process of cartridge dust collectors. Understanding the changes in the airflow field and the transport laws of dust particles during the pulse-jet cleaning process is important to improve the cleaning efficiency. In the present study, a single cartridge dust collector was selected as the research object. The numerical simulations on the pulse-jet cleaning process of the cartridge were carried out by the CFD-DPM method. The distributions and variations of the airflow and the dust particles were investigated during the pulse-jet cleaning process for a filter cartridge. In order to optimize the flow field and improve the cleaning efficiency, new types of diffusers were designed, and simulations on the pulse-jet cleaning process were conducted. The distribution of the jet airflow, the pressure changes on the side walls of the filter cartridge, and the dust dispersion under the four conditions for the filter cartridge were obtained. It was found that the existence of the diffuser is helpful to broaden the coverage area of the pulse jet airflow, which is conducive to enhancing the effectiveness of the cleaning process. For the two-layer perforated diffuser, the dust concentration on the filter cartridge diminishes at the most rapid rate, from about 0.25 to 0.01 kg/m³, which is twice that of the condition without a diffuser. The present study provides a theoretical basis for the application of diffusers in the cleaning process and offers certain guidance for enhancing the cleaning efficiency of the filter cartridge dust collectors.

The escalating need for transient monitoring solutions within the body is driving significant advancements in bioresorbable electronics. Traditional diagnostic and monitoring methods are frequently costly, invasive, and time consuming. Conventional implantable sensors present further challenges due to their long-term presence. This can lead to complications and necessitate a second surgery for removal. Such removal ameliorates costs and patient distress and risks injury to the brain. These sensors can also act as a nidus for infection or immune-mediated inflammatory responses. The use of nonbiodegradable materials in these devices confers mainly to electronic waste and accelerates environmental pollution. Biodegradable sensors offer a compelling and key alternative. They are explicitly constructed to dissolve harmlessly in biofluids at well-defined programmable rates to biologically benign end products. This innovation ends the need for secondary surgical extraction, reduces the risk of infection, and alleviates environmental problems associated with electronic waste. This review gives a comprehensive overview of the current state of biodegradable sensors in biomedical applications. It emphasizes biomimetic composites, advanced materials such as polymers, metals, and novel substances, and distinct sensor designs including pressure, strain, electrochemical, optical, and temperature sensing. It also highlights key in vivo applications such as intracranial, cardiovascular, and general health monitoring. These devices fulfill the major clinical requirement for minimally invasive, temporary, and environmentally sustainable physiological monitoring.

Co-pyrolysis is potentially an effective treatment of oily sludge (OS) for oil recovery. The co-pyrolysis behavior and product distribution of OS and walnut shell (WS) were systematically investigated. Thermogravimetric analysis coupled with Fourier transform infrared spectroscopy (TG-FTIR) and pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) were employed to examine the effects of varying mixing ratios and temperatures on pyrolysis characteristics. The results revealed a clear synergistic interaction between OS and WS during co-pyrolysis. FTIR results showed that the addition of WS altered the reaction path, reduced CO₂ emissions, and changed the distribution of volatile products, resulting in improved pyrolysis efficiency and environmental performance. Kinetic analysis demonstrated that the co-pyrolysis was predominantly diffusion-controlled, with the strongest synergy and best model fit observed at a 1:1 OS-to-WS ratio at an OS-to-WS ratio of 1:1. In terms of product distribution, the incorporation of WS significantly reduced the yield of heavy components, thereby enhancing the quality and conversion efficiency of the pyrolysis oil. Pyrolysis temperature had a notable impact on the composition of volatile products, where acid compounds were markedly suppressed due to synergistic effects at temperatures of 600°C and below.

This study aims to investigate the research status and latest advancements in the application of magnetic resonance imaging (MRI) contrast agents for glioma diagnosis. A comprehensive search was conducted in the Web of Science Core Collection for literature published between 2015 and 2024 related to MRI, contrast agents, and glioma. A total of 454 relevant publications were included and analyzed using bibliometric tools, including the Online Analysis Platform and CiteSpace software. The number of publications peaked in 2017, followed by a gradual decline from 2021 to 2024. The United States, China, and Germany emerged as the leading countries in publication output. Prominent institutions included Johns Hopkins University, the University of California, Los Angeles (UCLA), and Oregon Health & Science University. The most frequently cited journals were Radiology, Neuro-oncology, and Journal of Magnetic Resonance Imaging. Among authors, Patrick Y. Wen was most cited, followed by Roger Stupp and Jerrold L. Boxerman. Influential references included “Bevacizumab plus radiotherapy-temozolomide for newly diagnosed glioblastoma” and “Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials.” Research hotspots focus on Nuclear Overhauser Enhancement (NOE), while emerging themes suggested increasing interest in radiotherapy, the blood–brain barrier (BBB), clinical recommendations, radiation necrosis, size, and deep learning applications. This bibliometric and visual analysis highlights the evolving research focus on MRI contrast agents in glioma diagnosis over the past decade. Future directions are likely to include advanced imaging techniques, nanotechnology-enhanced contrast agents, AI, and deep learning, clinical diagnostics, optimization, biomarker discovery, and personalized medicine approaches.

The resource utilization of food waste poses a pressing problem during the urbanization in China. In this paper, hydrothermal carbon (HC) was prepared from food waste by hydrothermal carbonization (HTC), and the combustion performance of HC was analyzed. The results indicated that the inorganic salts present in crab shells and bone meal within the food waste enhanced the ash content of the HC. The ash content of food waste HC with crab shells (FWH-CS) and food waste HC with bone meal (FWH-BM) was 18.43% and 30.26%, respectively. The removal of bones and crab shells from food waste increased the calorific value of the HC. The HC was subjected to K₂CO₃ activation to produce the activated HC (AHC) for Cu²⁺ adsorption. Static adsorption experiments revealed that the food waste AHC (FWA) exhibited the highest Cu²⁺ adsorption capacity (27.575 mg/g), whereas AHC from food waste with crab shells (FWA-CS) showed the lowest (23.9 mg/g). Combining the adsorption kinetics analysis, the adsorption mechanism for all three AHC samples predominantly involved chemisorption, supplemented by physisorption. Adsorption isotherm analysis indicated that both monolayer and multilayer adsorption processed in the three AHC samples.

The control challenges upturn if the non–self-regulating (integrating) processes include process time delay, double poles at the origin, and inverse behavior. None of the existing literature has considered all the above-mentioned types of processes in the frame of a unified control method. In this manuscript, a novel PD-PID–based dead time compensator is developed for a wide range of difficult integrating systems to achieve satisfactory performance and enhanced robustness against unwanted disturbance variables and process uncertainties. Analytical relations are derived for the inner and outer loop controllers using the direct synthesis approach. Further, suitable recommendations are made to choose the appropriate value of the tunable constants so that the robustness constraints are satisfied. The efficacy of the developed control laws is justified by implementing them on various case studies through numerical simulations and theoretical analysis. The suggested method is found capable of mitigating the effect of ramp-type disturbances also in addition to the step disturbance.

The traditional data-driven methods for predicting the component content of rare earth elements (REEs) suffer from several drawbacks, notably a considerable delay in data labeling, elevated costs, and a massive quantity of unused unlabeled data. To enhance the prediction of REE component content, this paper suggests an improved L2 regularization semi-supervised extreme learning machine based on DBSCAN and RBM, coined RBM-DL2SELM. First, to mitigate the potential issues of an unresolvable Moore–Penrose generalized inverse or model overfitting, which are common in traditional graph-based SELMs, the L2 regularization term is integrated into the SELM framework, forming the L2SELM model. This enhancement boosts the model's generalization ability. Second, the L2SELM model's structure is further refined by incorporating the density-based spatial clustering of applications with noise (DBSCAN) algorithm. This step addresses concerns related to data imbalance in production processes, missing data entries, and potential data errors. Moreover, to overcome the instability issues stemming from the random initialization of weights and biases in the L2SELM, the restricted Boltzmann machine (RBM) is introduced for adaptive optimization to elevate both the prediction accuracy and reliability of the model. Finally, simulation verification using field data from Pr/Nd extraction demonstrates that the proposed method offers substantial advantages in terms of accuracy, stability, and data utilization. These attributes render it more suitable for practical applications in real-world scenarios, where vast amounts of unlabeled data are typically available at rare earth extraction sites.

Modern chemical production processes are complex and diverse, and fault detection methods have always been an important part of complex chemical production process research. The variance and mean of the multibatch dynamic nonstationary process vary with time, which makes the dynamic canonical correlation analysis (DCCA) method deficient in calculating statistics and modeling the multibatch process. Therefore, the DCCA with multibatch benchmark distance (DCCA–MBD) for fault detection method is proposed in this paper. Based on the use of DCCA, the detection of multibatch dynamic nonstationary process is based on DCCA improved by the multibatch modeling scheme and statistic calculation method of the MBD method. Through numerical simulation and monitoring experiments of continuous stirred tank reactor production, it is verified that the method of this paper is more effective in dealing with multibatch dynamic nonstationary process compared with conventional methods.

As a functional artificial carbon material, mesophase coke has shown notable potential in various fields (particularly in energy storage and metal smelting) due to its outstanding properties. To achieve high-value utilization of mesophase coke in energy transition, 20 green cokes with distinct properties and structures were selected as raw materials and calcined to get mesophase coke. A series of properties were characterized and quantified. Meanwhile, the structure–activity relationship between optical microstructure and mesophase coke properties was established by linear fitting. In fact, mosaic and leaflet structures significantly influenced the disorder degree of mesophase coke, the former exhibiting a more pronounced effect than the latter. The total content of them showed strong correlations with micro-strength intensity and amorphous carbon content. In contrast, the fibrous structure contributed to structural regularity. The change of its content directly affected the true density and graphite microcrystal content. Moreover, fibrous and leaflet structures exhibited lower reactivity with oxygen. The combined content of them showed a negative correlation with the oxidation (combustion) reaction rate. Among these cokes, ZHC was most special. Its optical structure exhibited an extreme distribution, the mosaic structure content reached 97.1%, and OTI was only 128. It showed the highest disorder degree, which marked it as a potential hard carbon precursor. This work provides the theoretical and experimental foundations for the high-value utilization of mesophase coke.

The practical application of conventional Ti/SnO₂-Sb electrodes is hindered by their low oxygen evolution potential (OEP) and rapid deactivation. Rare earth doping has been proposed to enhance electrode stability and catalytic activity; however, the comparative effects of different rare earth elements (e.g., La vs. Eu) on both structural and electrochemical properties remain unclear. In this paper, the fabrication of lanthanum- and europium-doped Ti/SnO₂-Sb (TSS) electrodes and the electrochemical degradation of methylene blue (MB) were investigated. Although both lanthanum (La) and europium (Eu) are rare earth elements with many similarities, lanthanum (La)-modified electrode exhibits distinct performance characteristics. It is indicated that the Ti/SnO₂-Sb-La (TSSL) electrode has a comparatively better crystal phase, enhanced surface hydrophobicity, and better electrochemical performance. The TSSL electrode exhibits a higher OEP of 1.84 V than other electrodes, showing a 0.15 V increase when compared with the undoped electrode. The peak oxidation current reaches 0.82 mA/cm², which provides more active sites for the catalytic reaction. It is found that the La-doped electrode has better performance in wastewater treatment; the TSSL electrode shows a 45% increase in degradation efficiency when compared with the Ti/SnO₂-Sb-Eu (TSSE) electrode for treatment of 50 ppm MB in 25 min. Moreover, the effects of current density and the initial concentration of MB were also investigated. Considering the degradation rate and energy consumption, the optimum current density for wastewater treatment is 20 mA/cm². The outcome indicated that the degradation of MB was up to 98.5% in 25 min.