Improve Process Efficiency Research Articles

Firm Responses to Nonprescriptive Pollution Regulation: Evidence from China’s Corporate Social Responsibility Reporting Mandate

ABSTRACT We employ China’s mandatory corporate social responsibility (CSR) reporting requirement as a natural experiment to evaluate firm-level responses to a Chinese rule that required public firms to issue CSR reports beginning in 2009. Our difference-in-differences analyses of firm-level data indicate that firms required to issue CSR reports decreased overall pollution levels after the mandate. Conventional wisdom (the “Traditional View”) argues that pollution reductions cannot be achieved without profit sacrifice. Conversely, under the Porter Hypothesis, non-prescriptive regulations can stimulate innovations that improve process efficiency and lead to both pollution reductions and improved long-term shareholder returns. Our results reveal evidence of reduced asset returns among firms required to issue CSR reports after 2008, consistent with both hypotheses. However, consistent with the Porter Hypothesis and not the Traditional View, positive three-day stock returns surrounding the release of CSR report information suggest that shareholders favorably view the overall long-term effects of required CSR reports.

Application of University English Teaching Model Based on Artificial Intelligence Technology

Abstract This paper is based on cloud computing technology in artificial intelligence and a clustering algorithm controlled to divide teaching resource attributes and reduce packet loss rates in the system. Data mining is used to reduce the number of iterations and improve processing efficiency, a learner model is designed, and the teaching process and assessment tasks are constructed to realize the teaching model. Two natural classes of students at the University of S. underwent an instructional experiment. The results showed that the experimental class reached about 70% of the overall students in the achievement range of 85-95 and 95-100. The college English teaching model based on artificial intelligence technology’s feasibility and effectiveness are proven by the significant improvement in performance compared to the control class.

Advanced control strategies for continuous capture of monoclonal antibodies based upon biolayer interferometry.

The semi and fully continuous production of monoclonal antibodies (mAbs) has been gaining traction as a lower cost, and efficient production of mAbs to broaden patient access. To be truly flexible and adaptive to process demands, the industry has lacked sufficient advanced control strategies. The variation of the upstream product concentration typically cannot be handled by the downstream capture step, which is configured for a constant feed concentration and fixed binding capacity. This inflexibility leads to losses of efficiency and product yield. This study shows that these challenges can be overcome by a novel advanced control strategy concept that includes dynamic control throughout a perfusion bioreactor, with cell retention by alternating tangential flow, integrated with simulated moving bed (SMB) multi-column chromatography. The automation workflow and advanced control strategy were implemented through the use of a visual programming development environment. This enabled dynamic flow control across the upstream and downstream process integrated with a dynamic column loading of the SMB. A sensor prototype, based on continuous biolayer interferometry measurements was applied to detect mAb breakthrough within the last column flow-through to manage column switching. This novel approach provided higher specificity and lower background signal compared to commonly used spectroscopy methods, resulting in an optimized resin utilization while simultaneously avoiding product loss. The dynamic loading was found to provide a twofold increase of the mAb concentration in the eluate compared to a conservative approach with a predefined recipe with similar impurity removal. This concept shows that advanced control strategies can lead to significant process efficiency and yield improvement.

Production and Activity Characterization of Lipase from Bacillus flexus InaCC-B486

Lipases are widely used in a variety of industries, both to develop products and to improve process efficiency. The need for lipase increased along with the wider application of this enzyme. Therefore, studies related to the search for potential lipase-producing microbes that answer the needs of the industry are required to be carried out continuously. Enzymes produced by microbes are preferred because they can be produced quickly compared to other sources. Bacillus flexus InaCC-B486 was used to produce lipase in this study with olive oil as substrate. This research aimed to observe the production of lipase from B. flexus InaCC-B486 and characterize its activity. The result shows that the production of B. flexus InaCC-B486 lipase was optimal at day-4 which was 11.983 ± 0.101 U/mL. The activity of B. flexus InaCC-B486 lipase was optimal at an incubation time of 15 minutes (2.810 U/mL), pH of 8.0 (3.173 U/mL), and a temperature of 35 oC (3.173 U/mL). These findings can be used for further applications, both in research and industry, that use B. flexus InaCC-B486 as a resource for lipase production or any related applications.

The use of low-pressure cold plasma optimization for microbial decontamination and physicochemical preservation of strawberries

Strawberries are highly perishable products due to the presence of elevated water levels, thereby necessitating postharvest treatments to enhance physicochemical quality (PQ) and shelf-life. Although several studies have focused on the development of cold plasma processes as postharvest treatments, the effect of the processing parameters remains unexplored. Therefore, this study aimed to assess the use of low-pressure cold plasma (LP-CP) for microbial decontamination and physicochemical preservation of strawberries using Taguchi orthogonal array design (OAD). The results showed that optimizing LP-CP treatment power, exposure time, and reactor pressure delayed the deterioration rates of strawberries by approximately 10–60 %. Total plate count (TPC) showed that LP-CP treatment with 90 W for 90 s at 0.076 Torr was the optimum condition for microbial decontamination. Color change (CC) was optimally inhibited with 90 W for 60 s in 0.076 Torr. Meanwhile, the optimum condition for maintaining electrolyte leakage (EL) was 30 W for 30 s at 0.076 Torr. Optimal ascorbic acid (AA) content and antioxidant capacity (DPPH) were obtained at 30 W for 30 s in <0.756 Torr. LP-CP processing parameters of power (up to 59–96 %) had the most significant contribution (p < 0.05), followed by time (up to 29 %) and pressure (up to 10 %). Based on the results, reducing microbial contamination preserved the surface color of strawberries. Lowering LP-CP power input, exposure time, and reactor pressure was suggested to enhance the membrane integrity and antioxidant properties of the samples. The results of this study could promote green industrialization of strawberries by reducing waste and improving process efficiency to achieve sustainable development.

Influence of the material composition of the original ore on the quality of the iron ore concentrate.

In recent years, improvement of blast-furnace process efficiency is largely associated with an increase in the iron content in iron ore concentrates – the feedstock for agglomeration. The study of the material composition of ores, their features makes it possible to adjust the scheme of preparation of raw materials and determine the optimal content of iron in the concentrate for further processing. When studying the material composition, it was found that the sample of iron ore is represented by ferruginous quartzites of the silicate-magnetite type in combination with magnetite. In the original ore sample, magnetite dominates among ore minerals, and quartz and amphiboles dominate among rock-forming minerals. The coarseness and shape of the isolation of magnetite in all varieties of rocks are generally favorable for enrichment; the mineral is characterized by clear rectilinear and slightly sinuous contours, which contributes to its selective isolation. It should be noted that the magnetite content remains constant as the coarseness of the sample material decreases. Automated mineralogical analysis established that 87.36% of iron is associated with magnetite (with fluctuations in size classes from 86.23 to 91.37%); the rest of the iron is associated with rock-forming minerals. The theoretical content of magnetite in iron ore concentrate, achievable by enriching the original ore of the studied sample, according to automated mineralogical analysis, will be 95.83%, which, in turn, will provide the maximum possible iron content in magnetite concentrate of 68.57%. The existing technology of mining and enrich-ment using 4 types of ores from the Stoilenskoye deposit, with a planned increase in iron to 70%, will result to fluctua-tions of this component in the concentrate from 68.0 to 70.0%.

Multi-information integration-based life cycle analysis of greenhouse gas emissions for prefabricated construction: A case study of Shenzhen

Prefabricated buildings have gained significant attention in the global construction sector due to their potential to reduce construction time, improve process efficiency, save resources, and mitigate pollution. It is crucial to quantify the greenhouse gas (GHG) emissions associated with prefabrication technologies for sustainable development. However, there is a lack of comprehensive research utilizing advanced technological tools to examine and visualize GHG emissions in prefabrication. To address this gap, this study employed a multi-information method that combines Building Information Modeling (BIM) and Geographic Information System (GIS) with a life cycle assessment (LCA) approach to analyze and calculate the spatial distribution of GHG emissions from prefabricated components and buildings during the production, transportation, and construction stages in China. The results indicate the following: (1) GHG emissions per cubic meter for the prefabricated exterior wall, balcony, and stair are 1080.77 kgCO2eq/m3, 867.84 kgCO2eq/m3, and 1902.63 kgCO2eq/m3, respectively, with an overall average of 1138.58 kgCO2eq/m3 for prefabricated components. (2) The production phase is responsible for the largest share of GHG emissions, accounting for 96.11%, while the transportation and construction phases contribute 3.30% and 0.59% of the total emissions, respectively. (3) The selection of prefabricated construction can lead to a 31.62% reduction in total GHG emissions per square meter, with emissions of 269.16 kgCO2eq/m2, compared to traditional construction with emissions of 393.63 kgCO2eq/m2. The study contributes to the knowledge body by providing a systematic method for calculating GHG emissions and offering traceability and visibility of GHG emissions for prefabricated components and prefabricated buildings. These findings can serve as practical references for future research to achieve the construction industry's green and low-carbon goals.

Mechanism and performance evaluation of transient and selective laser processing of glass based on optical monitoring.

Femtosecond laser processing has been widely applied in glass processing owing to its ability to fabricate microscale components. To improve processing efficiency, a transient and selective laser (TSL) processing technique was previously developed, in which electron excitation was induced inside a transparent medium by a single pulse of femtosecond (fs) laser, and a single pulse of microsecond (µs) laser can be selectively absorbed in this excited region to heat and remove the material. However, because of its high speed removal process, the unclear mechanism and inefficient evaluation of its processing performance limit its further application. This study analyzes the transient spatiotemporal evolution of the induced plasma and the related material removal mechanism of the TSL processing using a side high-speed monitoring method. To achieve a rapid performance evaluation, a quantitative analysis of the optical plasma signals (on a microsecond timescale) generated in TSL processing was performed by employing a developed coaxial high-speed monitoring method using a photodetector. The variations in the shapes, intensity distribution, and dimensions of the plasma were quantitatively investigated. In addition, the relation between the plasma signal and drilling performance under different laser parameters, including hole depth, hole types, and cracks, was explored and quantitatively analyzed. The revealed mechanism is expected to contribute to the broadening of the application of TSL processing in microfabrication. Furthermore, the developed high-speed and precision monitoring technology can be utilized for high-speed evaluation and precision control of machining quality in real time during ultrahigh-speed laser machining, without time-consuming camera observations.

Strategic directions of development and increase of the economic efficiency of sunflower production and processing

The article highlights the problems of strategic development and increasing the efficiency of sunflower production and processing. The primary attention is paid to identifying the agro-technological features of the crop (first, high energy intensity and extremely demanding restrictions on predecessors and compliance with crop rotations. A comprehensive assessment of the problems accompanying its production and processing was carried out. They relate to two dimensions of the environment: production-economic and ecological. Therefore, strategic directions for sunflower production development and improving the efficiency of its production and processing should cover, satisfy, and harmonize them. Production and economic strategies should include a selection of new high-yielding varieties and hybrids, innovative production techniques and technologies, new means of plant protection and fertilizers and land reclamation, and innovations in processing, logistics, implementation, and marketing policy. Nevertheless, they should not manifest themselves due to violating ecological imperatives. Therefore, ecological strategies should include modern agrotechnical techniques with simultaneous improvement of the quality of the environment and soil fertility. Only such a comprehensive approach will ensure a high level of development of the industry at the level of agricultural enterprises and a comprehensive increase in sunflower production and processing efficiency without harming the environment. The directions of development and improvement of sunflower production and processing efficiency should now be considered in two inextricably linked planes: from the standpoint of production and economic improvement of management and under the conditions of compliance with its ecological limitations and ensuring the ecological quality of the produced products. Irrational technology leads not only to a drop in productivity and a decrease in the quality of products but also to a catastrophic deterioration of the environment. This undoubtedly leads to additional costs and reduces the potential for effective development of agrarian enterprises for sunflower production and processing. Therefore, introducing innovations in all stages of the reproduction process of sunflower and its processing compliance with ecological imperatives should be a priority of strategic directions of development and improvement of the efficiency of industries at the level of agricultural enterprises. Keywords: agricultural enterprises, sunflower, production, processing, development, strategic directions, efficiency improvement, production and economic processes, ecological imperatives, harmonization.

The use of milk Fourier-transform infrared spectra for predicting cheesemaking traits in Grana Padano Protected Designation of Origin cheese

The prediction of the cheese yield (%CY) traits for curd, solids, and retained water and the amount of fat, protein, solids, and energy recovered from the milk into the curd (%REC) by Bayesian models, using Fourier-transform infrared spectroscopy (FTIR), can be of significant economic interest to the dairy industry and can contribute to the improvement of the cheese process efficiency. The yields give a quantitative measure of the ratio between weights of the input and output of the process, whereas the nutrient recovery allows to assess the quantitative transfer of a component from milk to cheese (expressed in % of the initial weight). The aims of this study were: (1) to investigate the feasibility of using bulk milk spectra to predict %CY and %REC traits, and (2) to quantify the effect of the dairy industry and the contribution of single-spectrum wavelengths on the prediction accuracy of these traits using vat milk samples destined to the production of Grana Padano Protected Designation of Origin cheese. Information from 72 cheesemaking days (in total, 216 vats) from 3 dairy industries were collected. For each vat, the milk was weighed and analyzed for composition (total solids [TS], lactose, protein, and fat). After 48 h from cheesemaking, each cheese was weighed, and the resulting whey was sampled for composition as well (TS, lactose, protein, and fat). Two spectra from each milk sample were collected in the range between 5,011 and 925 cm-1 and averaged before the data analysis. The calibration models were developed via a Bayesian approach by using the BGLR (Bayesian Generalized Linear Regression) package of R software. The performance of the models was assessed by the coefficient of determination (R2VAL) and the root mean squared error (RMSEVAL) of validation. Random cross-validation (CVL) was applied [80% calibration and 20% validation set] with 10 replicates. Then, a stratified cross-validation (SCV) was performed to assess the effect of the dairy industry on prediction accuracy. The study was repeated using a selection of informative wavelengths to assess the necessity of using whole spectra to optimize prediction accuracy. Results showed the feasibility of using FTIR spectra and Bayesian models to predict cheesemaking traits. The R2VAL values obtained with the CVL procedure were promising in particular for the %CY and %REC for protein, ranging from 0.44 to 0.66 with very low RMSEVAL (from 0.16 to 0.53). Prediction accuracy obtained with the SCV was strongly influenced by the dairy factory industry. The general low values gained with the SCV do not permit a practical application of this approach, but they highlight the importance of building calibration models with a dataset covering the largest possible sample variability. This study also demonstrated that the use of the full FTIR spectra may be redundant for the prediction of the cheesemaking traits and that a specific selection of the most informative wavelengths led to improved prediction accuracy. This could lead to the development of dedicated spectrometers using selected wavelengths with built-in calibrations for the online prediction of these innovative traits.

Potential of Raman spectroscopy for in-line measurement of raw milk composition

A method for the in-line measurement of raw milk composition is beneficial for the dairy industry as it allows processors to make timely decisions, i.e., standardization, prior to the milk entering a process. It facilitates more enhanced operational control and offers the potential for improved process efficiencies. One such technology of potential commercial value is Raman spectroscopy due to its ability to measure macromolecules in an aqueous environment and compatibility with in-line measurements. This study investigated the suitability of Raman spectroscopy to measure macro components (fat, protein, and lactose) in raw milk. 80 raw milk samples were analysed using a Raman spectroscopy instrument coupled with an optical fibre probe. Variations in process variables such as temperature and the effect of agitation on Raman spectral features (intensity, shape, and wavelength shift) were considered prior to model development. Due to the overlapping response of fat, protein, and lactose in the Raman spectrum of raw milk, multivariate regression models were developed for their quantification. The developed partial least squares (PLS) regression models predicted the percentage of fat, protein, and lactose in raw milk with a root mean square error of prediction (RMSEP) of 0.15, 0.11 and 0.04, coefficient of determination for prediction (R2p) 0.96, 0.89 and 0.89, and the ratio of prediction error to deviation (RPD) of 8.16, 3.16, and 2.89.

Attention preserves the selectivity of feature-tuned normalization.

Attention and divisive normalization both contribute to making visual processing more efficient. Attention selectively increases the neural gain of relevant information in the early visual cortex, resulting in stronger perceived salience for attended regions or features. Divisive normalization improves processing efficiency by suppressing responses to homogeneous inputs and highlighting salient boundaries, facilitating sparse coding of inputs. Theoretical and empirical research suggest a tight link between attention and normalization, wherein attending to a stimulus results in a release from normalization, thereby allowing for an increase in neural response gain. In the present study, we address whether attention alters the qualitative properties of normalization. Specifically, we examine how attention influences the feature-tuned nature of normalization, whereby suppression is stronger between visual stimuli whose orientation contents are similar, and weaker when the orientations are different. Ten human observers viewed stimuli that varied in orientation content while we acquired fMRI BOLD responses under two attentional states: attending toward or attending away from the stimulus. Our results indicate that attention does not alter the specificity of feature-tuned normalization. Instead, attention seems to enhance visuocortical responses evenly, regardless of the degree of orientation similarity within the stimulus. Since visuocortical responses exhibit adaptation to statistical regularities in natural scenes, we conclude that while attention can selectively increase the gain of responses to attended items, it does not appear to alter the ecologically relevant correspondence between orientation differences and strength of tuned normalization.NEW & NOTEWORTHY The magnitude of visuocortical BOLD responses scales with orientation differences in visual stimuli, with the strongest response suppression for collinear stimuli and least suppression for orthogonal, in a way that appears to match natural scene statistics. We examined the effects of attention on this feature-tuned property of suppression and found that while attending to a stimulus increases the overall gain of visuocortical responses, the qualitative properties of feature-tuning remain unchanged, suggesting attention preserves tuned normalization properties.

A Graphical Tool to Describe the Operating Point of Direct Reduction Shaft Processes

This article presents a new graphical tool for direct reduction shaft processes inspired by the Rist diagram developed for blast furnaces. The tool represents gas flows using vectors, with specific consumption and specific oxidation as components to indicate gas/iron ratios. Key features include consideration of gas chemical composition for vector directions, easy visual representation of gas mixtures, as well as reduction and carburization rates of direct reduced iron (DRI). The tool also includes thermodynamic conditions for reduction from the Chaudron diagram, analogous to the Rist diagram. Several practical applications are presented, including quantifying gas moisture, evaluating the measurement consistency of flowmeters and gas analyzers in top gas recycling, and evaluating instantaneous DRI production by analyzing reducing gas at the inlet and outlet of the shaft. This graphical tool could be useful for production teams to monitor and optimize process flows and promote understanding among students, engineers, technicians, and operators. Its potential for online use further enhances its practical value. As a result, the tool is of significant academic and industrial interest in improving process efficiency and optimization.

Environmental Impact Assessment of Frozen Peas Production from Conventional and Organic Farming in Italy

Increasing legume intake through dietary diversification confers nutritional and environmental benefits. This study used life cycle assessment to evaluate the environmental impacts of producing frozen green peas from conventional and organic farming. We explored two ways of treating farm data: modeling each farm (baseline) and using a uniform distribution of each farm parameter’s average, maximum, and minimum values (alternative). We also assessed the indirect land-use change (iLUC) impacts by applying a deterministic model and used the EF 3.0 method to estimate the midpoint environmental impacts. The results of the two scenarios for pea cultivation (including iLUC) showed notable differences in absolute terms with minor discrepancies in the contribution analysis (e.g., climate change (CC) for the baseline and alternative were 0.98 and 2.09 kg CO2 eq./kg fresh peas, respectively). Generally, conventional peas had a higher environmental impact than organic peas, although this was not uniformly observed across all farms. When included, iLUC accounted for nearly half of the CC score. Pea cultivation was the most impactful phase due to emissions from fertilizers and field operations. The impacts of pea production can be reduced by anaerobic digestion of pea residues with energy and nutrient recycling. However, improvements in processing and nitrogen use efficiency could significantly enhance the overall environmental performance of frozen green peas. In summary, this study emphasizes the need for sustainable practices to minimize the environmental impact of frozen pea production.

Dynamics of physical flows measured by positron emission techniques

Short lived positron emitting species are used to produce flow-following tracer particles to study flow dynamics in a technique known as positron emission particle tracking (PEPT). The photon pairs produced by positron annihilation are detected in time coincidence by arrays of high-speed position sensitive detectors. Reconstruction of consecutive annihilations are used to determine the near-instantaneous position of the tracer particle. Hence, the resulting bulk flow dynamics are derived, including residence times, velocities, accelerations, and related kinematic properties. The Department of Physics at the University of Cape Town uses PEPT to study dynamic physical processes, turbulent, and multiphase flows. Studies aim to address global challenge topics including problems in water scarce environments, reducing industrial wastes, and enhancing developments towards sustainable economies through improved process efficiencies and design led approaches. The PEPT Cape Town enterprise is discussed, including the development of flow metrology systems and complementary nuclear measurement techniques. Research encompasses four key themes: radioisotope tracer production, instrumentation & detector development, data acquisition & processing, and flow metrology.

Bioprocess Modelling, Intensification, and Control

&#x0D; &#x0D; &#x0D; Chemical bio-process intensification, driven by the need for sustainability and cost-effectiveness, has gained significant attention. This study explores dynamic intensification (DI), a strategy to enhance efficiency by modifying system dynamics, operation, and control. Two key approaches are investigated: intensification by design, involving deliberate changes to physical and chemical aspects, and intensification by control, optimizing process parameters and feedback loops.&#x0D; The research focuses on the Optimal Periodic Control (OPC) method, dating back to the 1960s but now revisited with modern technology. To investigate the feasibility and effectiveness of dynamic intensification, a comprehensive methodology encompassing modeling, optimization, linearization, dynamic intensification, and control was designed. This methodology ensures systematic and standardized testing of DI in various bioprocesses within the industry.&#x0D; Through rigorous case studies, such as Activated Sludge Model No. 1 (ASM1) and an antibiotic perfusion bioreactor, the study demonstrates the practical benefits of dynamic intensification, including enhanced process efficiency and control. Applying model predictive control (MPC) further improves process efficiency, and the intensified systems are thoroughly compared to their respective base cases.&#x0D; The insights gained from this research contribute to advancements in bioprocessing technologies across industries, paving the way for sustainable and efficient engineering practices. Engineers can leverage this knowledge and the developed methodology to optimize chemical bioprocesses through innovative intensification strategies, ultimately promoting more eco-friendly and economical bio-processes. The study's comprehensive methodology offers valuable insights and understanding for bioprocess engineers seeking to enhance system efficiency and contribute to a greener future.&#x0D; &#x0D; &#x0D;

Modeling and thermodynamic analysis of the hydration and germination of triticale seeds

We investigated the hydration and germination of triticale seeds to improve process efficiency and product quality. The grains were hydrated in a water bath at 20–50 °C for 15 h. Temperatures of 20 °C–30 °C and process times as of 660 min provided the best hydration rates without hindering the germination capacity of the seeds. Throughout processing, we monitored the moisture uptake and volumetric expansion of the samples by gravimetric methods. A tracer pigment added to the hydration water and the images obtained with scaning electron microscopy elucidated the mechanisms of moisture uptake throughout the grains. The water penetrated the grain primarily through the micropyle, a region of high porosity. The mass transfer (diffusion coefficient) and thermodynamic properties (enthalpy, entropy, and Gibbs free energy) indicated the process is non-spontaneous and endothermic. Such piece of information is helpful in equipment design. We also proposed mathematical models, based on Fick's Law of Diffusion, that predict the moisture content of cereal seeds hydrated at 20–50 °C for up to 15 h with great accuracy (P ≤ 3.36% and RMSE≤1.54%).

Microbial community composition of food waste before anaerobic digestion

Anaerobic digestion is widely used to process and recover value from food waste. Commercial food waste anaerobic digestion facilities seek improvements in process efficiency to enable higher throughput. There is limited information on the composition of microbial communities in food waste prior to digestion, limiting rational exploitation of the catalytic potential of microorganisms in pretreatment processes. To address this knowledge gap, bacterial and fungal communities in food waste samples from a commercial anaerobic digestion facility were characterised over 3 months. The abundance of 16S rRNA bacterial genes was approximately five orders of magnitude higher than the abundance of the fungal intergenic spacer (ITS) sequence, suggesting the numerical dominance of bacteria over fungi in food waste before anaerobic digestion. Evidence for the mass proliferation of bacteria in food waste during storage prior to anaerobic digestion is presented. The composition of the bacterial community shows variation over time, but lineages within the Lactobacillaceae family are consistently dominant. Nitrogen content and pH are correlated to community variation. These findings form a foundation for understanding the microbial ecology of food waste and provide opportunities to further improve the throughput of anaerobic digestion.

Numerical Investigation on Effect of Bubbles Arrangement and Volume Fraction on Apparent Viscosity of Bubbly Suspensions

Background:: Bubbly suspensions can be often run into in natural and industrial processes. The addition of bubbles with different sizes can lead to a significant change in the rheological properties of a matrix liquid. It is extremely significant to fully understand the rheological properties of bubbly suspensions for improving process efficiencies and optimizing productive processes. background: Bubbly suspensions can be often run into in natural and industrial processes. The addition of bubbles of different sizes can greatly change the rheological properties of matrix liquid. It is extremely significant to fully understand the rheological properties of bubbly suspensions for improving process efficiencies and optimizing productive processes. Objective:: The objective of this study is to explore qualitatively the physical law and internal mechanism of the apparent viscosity of suspensions formed by a Newtonian liquid containing different bubbles. Methods:: Based on the parallel plate model of shear flow, the volume of fluid method (VOF) was used to investigate the effect of bubble arrangement and volume fraction on the apparent viscosity of bubbly suspensions at low volume fractions. The piecewise linear interface calculation (PLIC) method was applied to reconstruct the interface based on the phase function. Results:: The present results show that the relative viscosity (ηr) of bubbly suspensions shows a nonlinear change with an increase in bubble volume fraction (ϕ). When the capillary number (Ca) is less than 0.6, ηr shows a nonlinear increase with an increase in ϕ (ηr increases from 1 to 1.03 with an increase in ϕ from 0 to 2.94% at Ca=0.1). However, Ca is greater than or equal to 0.6, ηr shows a nonlinear decrease with an increase in ϕ (ηr decreases from 1 to 0.92 with an increase in ϕ from 0 to 2.94% at Ca=2.5). Even if ϕ is the same, different arrangements of bubbles can lead to different magnitudes of apparent viscosity of bubbly suspensions Conclusion:: As ϕ increases, the region of low shear rate increases, which leads to a non−linear decrease in the relative viscosity. When ϕ is the same, the different arrangements of bubbles can lead to different effects on bubble dynamics and flow fields. This results in different viscous dissipation in bubbly suspensions. Thus, the apparent viscosity of bubbly suspensions is different. other: Nothing

Effect of scanning spacing on the efficiency of CFRP hole-cutting with multi-pass scanning strategy by nanosecond laser

Short-pulse laser processing of CFRP can reduce heat damage, but the efficiency is limited. In this paper, the efficiency of CFRP hole-cutting with multi-pass scanning strategy by nanosecond laser was investigated. The differences in material removal mechanisms at different scanning spacing and the effect of different scanning spacing on processing efficiency were investigated. The results show that the material is removed by the combined effect of laser ablation and mechanical erosion effect with different scanning spacing, and the appropriate scanning spacing and number of passes can effectively utilize the effect of mechanical erosion to improve processing efficiency. With a repetition frequency of 80 kHz (pulse energy is 141.25 μJ), the shortest cutting time is 73.3 s, and the optimal scanning spacing is 0.06 mm (focused spot diameter is 22 μm), which is 26.8 % more efficient compared to the scanning spacing of 0.08 mm and number of passes with 3; As laser repetition frequency increases, the optimal scanning spacing for processing efficiency increases and the optimal number of passes decreases.