Airfoil fin printed circuit heat exchangers (PCHEs) have broad application prospects in the naval, aerospace, electric power, and petrochemical industries. The channel structure is a critical factor affecting their thermal-hydraulic characteristics. In this study, a novel PCHE channel structure with staggered NACA 0025 airfoil-shaped fins was proposed; accordingly, the thermal-hydraulic characteristics of the novel channel structure using carbon dioxide as the working fluid at different fin heights under different operating conditions (trans-, near-, and far-critical) were investigated. The results indicated that the thermal-hydraulic performance of the PCHE under the trans-critical operating condition was better than that under the near-critical and far-critical operating conditions. Compared with conventional airfoil fin channels, the novel airfoil fin channel attained comparable comprehensive performance while reducing the fin volume by 50%, thus achieving a more lightweight PCHE de... [more]

Acerola is considered a superfruit, rich in vitamin C, phenolics, and carotenoids, and having a high antioxidant capacity. However, it is poor in oligosaccharides. Ultrasound technology can improve the bioavailability of several bioactive compounds, improving the nutritional content of several fruit juices. This work evaluated the use of ultrasound processing on acerola juice supplemented with fructooligosaccharides (FOS; 1% w/w) and its effects on the availability of vitamins, carotenoids, and phenolic content. The antioxidant capacity of the juice was correlated with its bioactive contents. The study evaluated the effects of important sonication parameters, such as ultrasonic power density, processing time, and processing temperature. The application of ultrasound was efficient in increasing the availability of some vitamins. As a result, ultrasound application increased the availability of vitamins B1, B3, B5, C, carotenoids, and phenolic compounds. This improvement increased the an... [more]

There is an increasing demand to improve the overall sustainability of the biopharmaceutical industry. A barrier to improvement has been the limited research undertaken in the area of environmental impact of key design decisions. The aim of this study was to perform a comprehensive evaluation of the impact of buffer management strategy and technology selection on overall process efficiency using process mass intensity (PMI) as a metric for comparison. The environmental impact of buffer management has yet to be fully understood, despite buffers being one of the most resource-intensive aspects of biopharmaceutical manufacturing. A detailed process model was used to evaluate the impact of buffer management on a monoclonal antibody (MAB) process at the 2000 L scale. This was achieved by means of a non-replicated full factorial design composed of six variables: product titre, quantity of unique buffers, preparation frequency, single-use threshold and equipment cleaning duration with two lev... [more]

The ubiquitous presence of microplastics (MPs) is a topic of great concern. Not only do MPs themselves represent potential toxicants for human health, they are never found alone in the environment and interact with and adsorb a variety of toxicologically significant pollutants. This review summarises recent work on interactions between MPs and heavy metals in the environment, with a special focus on arsenic, one of the most widespread and problematic water contaminants. Evidence for the adsorption of arsenic onto MP surfaces is given and the recent research into the consequences of this phenomenon for freshwater, marine, and soil environments presented. Finally, the lack of research into the significance of interactions between arsenic and MPs during drinking water treatment is highlighted. The performance of arsenic removal technologies is governed by a multitude of different factors, and with MPs detected in water sources all over the world, data on how these MPs impact the removal o... [more]

The end of the 20th century brought about drastic changes in the tropospheric ozone (O3) around the globe. It is, therefore, highly important to gain insight into O3 formation mechanisms and their key precursors in order to assist policymaking to combat O3 pollution. This article synthesizes a bibliometric analysis of O3 formation sensitivity from 1965 to 2022, reported in English language journals available in the Web of Science Core Collection. This study shows that constant expansion in the number of publications has occurred since 2008, with the highest number occurring in 2021. Most publications are from the United States of America (USA), with 406 papers (42.7%), followed by China with 128 papers (13.5%), and the United Kingdom (UK) with 87 papers (9.1%). Citation burst analysis and significant and highly cited research work analysis are used to discover and assess evolving research tendencies. The thematic evolution of author-supplied keywords indicates that the terms “volatile... [more]

The proper understanding of reservoir properties is an important step prior to forecasting fluid productions and deploying development strategies of a coalbed methane (CBM) reservoir. The assisted history matching (AHM) technique is a powerful technique that can derive reservoir properties based on production data, which however is usually rather time-consuming because hundreds or even thousands of numerical simulation runs are required before reasonable results can be obtained. This paper proposed the use of a newly developed algorithm, namely the Bayesian adaptive direct searching (BADS) algorithm, for assisting history matching of fractured vertical CBM wells to derive reservoir property values. The proposed method was applied on representative fractured vertical wells in the low-permeable CBM reservoirs in the Qinshui Basin, China. Results showed that the proposed method is capable of deriving reasonable estimates of key reservoir properties within a number of 50 numerical simulati... [more]

The accurate acquisition of downhole engineering parameters, such as real-time pressure and temperature measurements, plays a crucial role in mitigating drilling risks and preventing accidents. In this study, we present the design of a real-time data acquisition and transmission system for drilling operations. The system utilizes a near-bit measurement method to simultaneously measure downhole parameters, including mud pressure and temperature. By analyzing the pressure and temperature frequencies obtained from a quartz crystal pressure gauge and compensating for temperature effects, accurate pressure values are obtained. The resistance value of a PT1000 sensor is measured, and a second-order fitting is performed using laboratory scale coefficients to determine the temperature values. The data acquisition system employs an advanced microcontroller as the main control chip, along with an A/D conversion chip. Additionally, signal amplification, data storage modules, data transmission mod... [more]

In numerous subterranean projects, the impact of groundwater on the safety of the engineering undertaking is of paramount significance. Fractures, functioning as the primary channels for seepage within subterranean rock masses, necessitate the complex and challenging task of accurately characterizing seepage patterns and quantitatively investigating the effect of fissure parameters on fluid dynamics within the rock masses. This article presents a stochastic fissure model incorporated within a finite element framework, which captures the probabilistic distribution of fissures found in nature. It provides a comprehensive analysis of the distribution of pore water pressure and Darcy velocity fields. It unveils the permeation patterns of fissured rock masses and establishes a series of fissure models, quantitatively investigating the correlations between matrix permeability, water pressure, fissure density, fissure length, the length power law, fissure angle, the dispersion coefficient, fi... [more]

The growth and phase formation features, along with the influence of structure and morphology on the electronic, optical, and transport properties of Cr2GeC and Cr2-xMnxGeC MAX phase thin films synthesized by magnetron sputtering technique, were studied. It was found that the Cr:Ge:C atomic ratios most likely play the main role in the formation of a thin film of the MAX phase. A slight excess of carbon and manganese doping significantly improved the phase composition of the films. Cr2GeC films with a thicknesses exceeding 40 nm consisted of crystallites with well-developed facets, exhibiting metallic optical and transport properties. The hopping conduction observed in the Cr2-xMnxGeC film could be attributed to the columnar form of crystallites. Calculations based on a two-band model indicated high carrier concentrations N, P and mobility μ in the best-synthesized Cr2GeC film, suggesting transport properties close to single crystal material. The findings of this study can be utilized t... [more]

Reliability is a fundamental concept for power systems, and the optimal placement of switchable devices is a valuable tool for improvements in this area. The goal of this paper is to propose an optimal allocation method for circuit breakers and switches that can break the cost−reliability dilemma and simultaneously achieve reliability and economic improvement in terms of the distribution network. Moreover, in view of the fact that variations in the load level can affect the reliability of the distribution network, the variations of different load level scenarios are considered in this paper, where a mixed integer linear programming (MILP) model based on fictitious fault flows is established to derive the optimal allocation scheme that can adapt to the changes of multiple scenarios regarding the load. Meanwhile, due to the constraints of reliability indices, the post-fault reconfiguration scheme of a distribution network under different load level scenarios can also be obtained to enhan... [more]

spp. and Yersinia enterocolitica are priority bacteriological public health hazards in pork safety. For more successful control, it is necessary to collect data on their prevalence throughout the meat chain using the concept of harmonized epidemiological indicators. The aim of this study was to determine their prevalence in fattening pigs under different housing conditions by recovering the pathogen from tonsils and by serological testing of diaphragm meat juice at slaughterhouses. The overall prevalence of Salmonella spp. and Y. enterocolitica in tonsils was 9.8% and 6.5%, respectively, with no significant differences between large and small farms (p > 0.05). In general, seroprevalence of Salmonella spp. was 48.35% and of Yersinia 13.18% (p < 0.05) but without significant differences of individual seroprevalence between farm types. No association was found between detection of Salmonella spp. or Y. enterocolitica in tonsils and seroprevalence (φc = 0.121, p = 0.420; φc = 0.027, p =... [more]

With the high efficiency and automation of converter smelting, it is becoming increasingly important to predict and control the endpoint temperature of the converter. Based on the heat balance, a model for predicting the molten pool temperature in a converter was established. Moreover, the statistical method of multiple linear regression was used to calculate the converter heat loss coefficient, greatly improving the prediction accuracy of the mechanistic model. Using the model, the oxidation process for each element in the molten pool, the melting processes of scrap, and the flux were also calculated. The model could better approximate the actual smelting process. Data from a 130 t converter were collected to validate the model. When the error ranges were limited to ±20 and ±15 °C, the model hit rates were 96 and 86.7%, respectively.

In order to alleviate the serious problem of scaling in oilfield water injection pipelines, we developed a scale collection device and applied it in the field based on the idea to “change passive descaling to active descaling”, but the effect is not stable, so we need to improve the descaling effect. Firstly, this paper analyzed the effect of surface physical properties of eight non-metallic materials on CaCO3 scale growth and their mechanisms through shear experiments. Then, the influence of surface properties (roughness, contact angle, surface energy) on the scale growth characteristics was investigated. Finally, the influence of material surface properties on the friction coefficient was studied by a cyclic experiment. The results showed that except for PTFE (polytetrafluoroethylene), the fouling amount of the other seven materials changed abruptly at 18 h, and the maximum fouling amount of FRP was 2.05 g/m3. It was found by scanning electron microscopy that the fouling particles on... [more]

Electrical systems consist of varied components that are used for power distribution, supply, and transfer. During transmission, component failures occur as a result of signal interruptions and peak utilization. Therefore, fault diagnosis should be performed to prevent fluctuations in the power distribution. This article proposes a fluctuation-reducing fault diagnosis method (FRFDM) for use in power distribution networks. The designed method employs fuzzy linear inferences to identify fluctuations in electrical signals that occur due to peak load demand and signal interruptions. The fuzzy process identifies the fluctuations in electrical signals that occur during distribution intervals. The linear relationship between two peak wavelets throughout the intervals are verified across successive distribution phases. In this paper, non-recurrent validation for these fluctuations is considered based on the limits found between the power drop and failure. This modification is used for preventi... [more]

The chemical industry is a typical high-carbon emitting industry, and achieving the goal of net zero emissions by 2050 is challenging. Therefore, metal chemical enterprises have to explore a special path of low-carbon development. This article conducted a case study on a Chinese metal chemical production enterprise with a processing scale of 28,000 t/year. Starting from the analysis of energy consumption carbon emissions, this article used available statistical data at the enterprise level to build a carbon emission estimation model for the enterprise combining different emission categories. Moreover, we also calculated the carbon emissions and carbon emission intensity of the enterprise from 2014 to 2022. Further quantitative analyses on the impact of production scale, energy efficiency, energy structure, and emission coefficient on carbon increment were also conducted using a logarithmic mean divisia index (LMDI) model. The results showed that the reduction in carbon emissions of the... [more]

Several papers have proven that advanced process controller (APC) systems can save more energy in the process than proportional-integral-differential (PID) controller systems. Therefore, implementing an APC system is ultimately beneficial for saving energy in the plant. In a typical APC system deployment, the APC model parameters are calculated from dynamic data intervals obtained through the plant test. However, depending on the proficiency of the APC engineer, the results of the plant test and the APC model parameters are implemented differently. To minimize the influence of the APC engineer and calculate universal APC model parameters, a technique is needed to obtain dynamic data without a plant test. In this study, we utilize time-series data from a real petrochemical plant to determine dynamic intervals and estimate APC model parameters, which have not been investigated in previous studies. This involves extracting the data of the dynamic intervals with the smallest mean absolute... [more]

The escalating global concern for sustainable development necessitates an in-depth understanding of the role of renewable energy projects. Evaluating their impact on economic, environmental, and social sustainability is of significant importance. In this study, the impact of green energy projects on economic, environmental, and social sustainability across APEC countries from 2010 to 2021 is comprehensively assessed using machine learning models. The employed machine learning models revealed associations between key variables and sustainability implications of green energy projects. Renewable energy consumption emerged as a significant contributor to economic performance, scoring a compelling importance score of 0.34. Concurrently, fossil fuel energy consumption and urban population were identified as key influencers on environmental outcomes and social impacts, respectively, with importance scores of 0.36 and 0.42. The empirical evidence presented in this research underscores the pivo... [more]

In the process of anaerobic dry fermentation to produce biogas, maintaining a suitable pH in the environment is more conducive to the degradation of crop straw. When the pH in the fermentation environment is too low, the process of anaerobic digestion by anaerobic bacteria is inhibited. Therefore, it is necessary to quickly adjust the pH. In this work, we studied the control technology of a pH regulation system and then constructed a T-S fuzzy controller. Upon simplifying the T-S fuzzy controller, the system delay time was reduced, and two genetic algorithms with different fitness performance indicators were used to optimize the T-S fuzzy control. The simulation experiment in this study was designed through simulation software, and the results show that the improved control method has a fast regulation ability. Finally, on-site experiments were conducted using the four control methods under the acidification conditions set in the experimental device. The results show that the control m... [more]

In order to overcome the shortcomings of existing electrowetting display defect detection models in terms of computational complexity, structural complexity, detection speed, and detection accuracy, this article proposes an improved YOLOv7-based electrowetting display defect detection model. The model effectively optimizes the detection performance of display defects, especially small target defects, by integrating GhostNetV2 modules, Acmix attention mechanisms, and NGWD (Normalized Gaussian Wasserstein Distance) Loss. At the same time, it reduces the parameter size of the network model and improves the inference efficiency of the network. This article evaluates the performance of an improved model using a self-constructed electrowetting display defect dataset. The experimental results show that the proposed improved model achieves an average detection rate (mAP) of 89.5% and an average inference time of 35.9 ms. Compared to the original network, the number of parameters and computatio... [more]

Turbidite fans, serving as good reservoirs for petroleum accumulation, are typically formed during deep faulting periods in continental basins, particularly in steep slope zones. However, gentle slope zones are also significant and unique for the formation of turbidite fans. These turbidite fans hold immense importance in exploring concealed lithological reservoirs. Taking the Chezhen Depression of Bohai Bay Basin as an example, we conducted a comprehensive study of the turbidite fan deposits in the gentle slope zone. Our results indicate that (1) small-scale distal-source turbidite fans are a common sedimentary type in the Chezhen Depression of the Bohai Bay Basin; (2) the study area is mainly characterized by seven lithofacies; (3) there are incomplete Bouma sequences in the study interval. This study is an important turbidite investigation into continental faulted basins, and it can also provide an important reference value for exploration and development in unconventional reservoir... [more]

Machine learning is the main technical means for lithofacies logging identification. As the main target of shale oil spatial distribution prediction, mud shale petrography is subjected to the constraints of stratigraphic inhomogeneity and logging information redundancy. Therefore, choosing the most applicable machine learning method for different geological characteristics and data situations is one of the key aspects of high-precision lithofacies identification. However, only a few studies have been conducted on the applicability of machine learning methods for mud shale petrography. This paper aims to identify lithofacies using commonly used machine learning methods. The study employs five supervised learning algorithms, namely Random Forest Algorithm (RF), BP Neural Network Algorithm (BPANN), Gradient Boosting Decision Tree Method (GBDT), Nearest Neighbor Method (KNN), and Vector Machine Method (SVM), as well as four unsupervised learning algorithms, namely K-means, DBSCAN, SOM, and... [more]

Rock burst is easy to occur in the water-rich roadway of coal mines, which is closely related to the energy dissipation and fracture mechanism of rocks under coupled hydro-mechanical (H-M) unloading. Therefore, in combination with the triaxial loading and unloading process and H-M coupling effect, the mechanical test of layered sandstones under coupled hydro-mechanical unloading (TLUTP) was conducted. The energy dissipation and fracture mechanism were revealed. The results show that: (1) The influence of layered angles on the peak volumetric strain is more sensitive than that of confining pressure under conventional triaxial loading with H-M coupling (CTLTP). On the contrary, the influence of confining pressure on the peak volumetric strain is more sensitive than that of layered angles under TLUTP. (2) With increasing layered angles, the peak elastic energy density under CTLTP shows the “W” shaped evolution characteristic, while that of under TLUTP shows the “N” shaped evolution charac... [more]

Implementing intelligent identification of faults in hydroelectric units helps in the timely detection of faults and taking measures to minimize economic losses. Therefore, improving the accuracy of fault signal recognition has always been a research focus. This study is based on the improved empirical mode decomposition (EMD) theory to study the denoising and feature extraction of vibration signals of hydroelectric units and uses the backpropagation neural network (BPNN) to establish corresponding connections between signal features and vibration fault states. The improved EMD in this study can improve the performance of noise reduction processing and contribute to the accurate identification of vibration faults. The vibration fault identification criteria can adopt three dimensionless feature parameters: peak skewness coefficient, valley skewness coefficient, and kurtosis coefficient of the second- and third-order components of the signal, with recognition rates and accuracy reaching... [more]

Cryogenic fracturing, which uses liquid nitrogen (LN2) as a fracturing fluid, is a waterless fracturing method. However, previous attempts to investigate the fracture morphology of rocks after LN2 quenching have been mainly based on standard scanning electron microscopy (SEM) analysis at room temperature. This can be problematic since thermally-induced fractures created by temperature difference tend to close as a sample warms and thermal stress relaxes. To address this issue, we established a novel approach employing Cryo-scanning electron microscopy (Cryo-SEM) to investigate the fracture patterns induced by liquid nitrogen quenching under cryogenic conditions. This method can achieve in-situ visualization of fractures and pores with a nano-scale resolution at −190 °C. X-ray computed tomography (CT) is also employed to illustrate the fracture distribution inside samples. Cryo-SEM and standard SEM are compared, and statistical assessments are conducted to quantify fracture aperture siz... [more]

In a context where air quality has become a global concern, modelling techniques are becoming very popular for analysing pollutant dispersion conditions. While models based on empirical formulations are most widely used for industrial scenarios, singular cases involving complex terrain or large obstacles in the vicinity of emission sources require a more robust approach to evaluate the dispersion conditions. In this research, a computational fluid dynamics (CFD) model is developed to analyse the dispersion of pollutants from an industrial facility whose location and characteristics suggest the occurrence of complex flow features that affect the dispersion patterns. In addition, the variation of the gas exit temperatures of waste heat recovery systems is proposed as a measure to mitigate the impact of the plume. The simulation results show unexpected deviations in the plume path affecting vulnerable areas in a nearby mountainside, but increasing the gas exit temperature is useful to pre... [more]