With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance s...With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.展开更多
Purpose–The precast concrete slab track(PST)has advantages of fewer maintenance frequencies,better smooth rides and structural stability,which has been widely applied in urban rail transit.Precise positioning of prec...Purpose–The precast concrete slab track(PST)has advantages of fewer maintenance frequencies,better smooth rides and structural stability,which has been widely applied in urban rail transit.Precise positioning of precast concrete slab(PCS)is vital for keeping the initial track regularity.However,the cast-in-place process of the self-compacting concrete(SCC)filling layer generally causes a large deformation of PCS due to the water-hammer effect of flowing SCC,even cracking of PCS.Currently,the buoyancy characteristic and influencing factors of PCS during the SCC casting process have not been thoroughly studied in urban rail transit.Design/methodology/approach–In this work,a Computational Fluid Dynamics(CFD)model is established to calculate the buoyancy of PCS caused by the flowing SCC.The main influencing factors,including the inlet speed and flowability of SCC,have been analyzed and discussed.A new structural optimization scheme has been proposed for PST to reduce the buoyancy caused by the flowing SCC.Findings–The simulation and field test results showed that the buoyancy and deformation of PCS decreased obviously after adopting the new scheme.Originality/value–The findings of this study can provide guidance for the control of the deformation of PCS during the SCC construction process.展开更多
Magnesium and magnesium alloys,serving as crucial lightweight structural materials and hydrogen storage elements,find extensive applications in space technology,aviation,automotive,and magnesium-based hydrogen industr...Magnesium and magnesium alloys,serving as crucial lightweight structural materials and hydrogen storage elements,find extensive applications in space technology,aviation,automotive,and magnesium-based hydrogen industries.The global production of primary magnesium has reached approximately 1.2 million tons per year,with anticipated diversification in future applications and significant market demand.Nevertheless,approximately 80%of the world’s primary magnesium is still manufactured through the Pidgeon process,grappling with formidable issues including high energy consumption,massive carbon emission,significant resource depletion,and environmental pollution.The implementation of the relative vacuum method shows potential in breaking through technological challenges in the Pidgeon process,facilitating clean,low-carbon continuous magnesium smelting.This paper begins by introducing the principles of the relative vacuum method.Subsequently,it elucidates various innovative process routes,including relative vacuum ferrosilicon reduction,aluminum thermal reduction co-production of spinel,and aluminum thermal reduction co-production of calcium aluminate.Finally,and thermodynamic foundations of the relative vacuum,a quantitative analysis of the material,energy flows,carbon emission,and production cost for several new processes is conducted,comparing and analyzing them against the Pidgeon process.The study findings reveal that,with identical raw materials,the relative vacuum silicon thermal reduction process significantly decreases raw material consumption,energy consumption,and carbon dioxide emissions by 15.86%,30.89%,and 26.27%,respectively,compared to the Pidgeon process.The relative vacuum process,using magnesite as the raw material and aluminum as the reducing agent,has the lowest magnesium-to-feed ratio,at only 3.385.Additionally,its energy consumption and carbon dioxide emissions are the lowest,at 1.817 tce/t Mg and 7.782 t CO_(2)/t Mg,respectively.The energy consumption and carbon emissions of the relative vacuum magnesium smelting process co-producing calcium aluminate(12CaO·7Al_(2)O_(3),3CaO·Al_(2)O_(3),and CaO·Al_(2)O_(3))are highly correlated with the consumption of dolomite in the raw materials.When the reduction temperature is around 1473.15 K,the critical volume fraction of magnesium vapor for different processes varies within the range of 5%–40%.Production cost analysis shows that the relative vacuum primary magnesium smelting process has significant economic benefits.This paper offers essential data support and theoretical guidance for achieving energy efficiency,carbon reduction in magnesium smelting,and the industrial adoption of innovative processes.展开更多
The Mg-9Li-1Zn(LZ91)alloy was subjected to an ultrasonic surface rolling process(USRP)with varying passes for the purpose of modifying its surface state.The USRP transformed surface residual stress from initial tensil...The Mg-9Li-1Zn(LZ91)alloy was subjected to an ultrasonic surface rolling process(USRP)with varying passes for the purpose of modifying its surface state.The USRP transformed surface residual stress from initial tensile stress to compressive stress,decreasing the surface roughness and increasing the ratio of the β-Li phase.The USRPed LZ91 sample(3 passes)showed superior corrosion resistance,with the corrosion current density changing from 57.11 to 24.70μA cm^(-2),and the polarization resistance increasing from 576.3 to 1146.1Ωcm^(2).According to the corrosion procedure evaluations,in situ observation revealed that the LZ91 alloy initially experiences pitting,which subsequently develops into cracking.The substantial area coverage of the β-Li phase facilitates the formation of a protective film on the surface,effectively delaying localized corrosion.展开更多
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving ...This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving average behavior—SARMA(1,1)L under exponential white noise.Unlike previous works that rely on simplified models such as AR(1)or assume independence,this research derives for the first time an exact two-sided Average Run Length(ARL)formula for theModified EWMAchart under SARMA(1,1)L conditions,using a mathematically rigorous Fredholm integral approach.The derived formulas are validated against numerical integral equation(NIE)solutions,showing strong agreement and significantly reduced computational burden.Additionally,a performance comparison index(PCI)is introduced to assess the chart’s detection capability.Results demonstrate that the proposed method exhibits superior sensitivity to mean shifts in autocorrelated environments,outperforming existing approaches.The findings offer a new,efficient framework for real-time quality control in complex seasonal processes,with potential applications in environmental monitoring and intelligent manufacturing systems.展开更多
With the increasing per capita demand for animal protein,there is a growing interest in the abundant abalone protein resources.Abalone proteins are known for their nutritional and functional properties that contribute...With the increasing per capita demand for animal protein,there is a growing interest in the abundant abalone protein resources.Abalone proteins are known for their nutritional and functional properties that contribute to flavor and texture.We systematically constructed the relationship between abalone protein,processing,and proteomics.This paper reviews the nutritional properties of abalone proteins and evaluates the effects of different thermal processing techniques,non-thermal processing,and freezing on abalone proteins.In addition,we synthesize published abalone proteomics studies and the use of proteomics technology to better elucidate the quality changes of abalone and its products,and as a technical basis for the study of blue food marker proteins.It is important direction to clearly explain the protein composition and meat quality mechanism of abalone in the processing and storage by proteomic.During various types of thermal processing,non-thermal processing,and freezing of abalone,the various chemical forces between protein molecules are disrupted,which in turn leads to different degrees of denaturation,aggregation,and gelation,which may have an impact on the organoleptic properties,bioavailability,and digestibility of abalone muscle.Proteomics is used in abalone biology studies to understand developmental biology,physiology,disease,stress,and species identification and can also be a powerful tool to characterize processing methods on abalone quality properties.展开更多
This paper introduces a computational cognitive architecture that serves as a comprehensive computational theory of the human mind,from cognitive science and computational psychology.The cognitive architecture(named C...This paper introduces a computational cognitive architecture that serves as a comprehensive computational theory of the human mind,from cognitive science and computational psychology.The cognitive architecture(named Clarion)has been justified by,and validated against,psychological data,findings,and theoretical constructs.One important theoretical background for it is the dual-process theories,which led to its overall two-level structuring in a hybrid neuro-symbolic way.Furthermore,given the recent advances in AI and computing technology,LLMs are being incorporated into the model to better capture human intuition and instinct(and implicit processes in general),in order to further enhance Clarion.Integrating Clarion and LLMs can also help to develop AI systems that are more capable,more reliable,and more human-like.Overall,the paper advocates a multidisciplinary approach towards developing better models for cognitive science and for AI.展开更多
Nondestructive testing(NDT)methods such as visual inspection and ultrasonic testing are widely applied in manufacturing quality control,but they remain limited in their ability to detect defect characteristics.Visual ...Nondestructive testing(NDT)methods such as visual inspection and ultrasonic testing are widely applied in manufacturing quality control,but they remain limited in their ability to detect defect characteristics.Visual inspection depends strongly on operator experience,while ultrasonic testing requires physical contact and stable coupling conditions that are difficult to maintain in production lines.These constraints become more pronounced when defect-related information is scarce or when background noise interferes with signal acquisition in manufacturing processes.This study presents a non-contact acoustic method for diagnosing defects in scroll compressors during the manufacturing process.The diagnostic approach leverages Mel-frequency cepstral coefficients(MFCC),and shorttime Fourier transform(STFT)parameters to capture the rotational frequency and harmonic characteristics of the scroll compressor.These parameters enable the extraction of defect-related features even in the presence of background noise.A convolutional neural network(CNN)model was constructed using MFCCs and spectrograms as image inputs.The proposed method was validated using acoustic data collected from compressors operated at a fixed rotational speed under real manufacturing process.The method identified normal operation and three defect types.These results demonstrate the applicability of this method in noise-prone manufacturing environments and suggest its potential for improving product quality,manufacturing reliability and productivity.展开更多
Supercapacitors are efficient and versatile energy storage devices,offering remarkable power density,fast charge/discharge rates,and exceptional cycle life.As research continues to push the boundaries of their perform...Supercapacitors are efficient and versatile energy storage devices,offering remarkable power density,fast charge/discharge rates,and exceptional cycle life.As research continues to push the boundaries of their performance,electrode fabrication techniques are critical aspects influencing the overall capabilities of supercapacitors.Herein,we aim to shed light on the advantages offered by dry electrode processing for advanced supercapacitors.Notably,our study explores the performance of these electrodes in three different types of electrolytes:organic,ionic liquids,and quasi-solid states.By examining the impact of dry electrode processing on various electrode and electrolyte systems,we show valuable insights into the versatility and efficacy of this technique.The supercapacitors employing dry electrodes demonstrated significant improvements compared with conventional wet electrodes,with a lifespan extension of+45%in organic,+192%in ionic liquids,and+84%in quasi-solid electrolytes.Moreover,the increased electrode densities achievable through the dry approach directly translate to improved volumetric outputs,enhancing energy storage capacities within compact form factors.Notably,dry electrode-prepared supercapacitors outperformed their wet electrode counterparts,exhibiting a higher energy density of 6.1 Wh cm^(-3)compared with 4.7 Wh cm^(-3)at a high power density of 195Wcm^(-3),marking a substantial 28%energy improvement in the quasi-solid electrolyte.展开更多
Fault diagnosis in industrial process is essential for ensuring production safety and efficiency.However,existing methods exhibit limited capability in recognizing hard samples and struggle to maintain consistency in ...Fault diagnosis in industrial process is essential for ensuring production safety and efficiency.However,existing methods exhibit limited capability in recognizing hard samples and struggle to maintain consistency in feature distributions across domains,resulting in suboptimal performance and robustness.Therefore,this paper proposes a fault diagnosis neural network for hard sample mining and domain adaptive(SmdaNet).First,the method uses deep belief networks(DBN)to build a diagnostic model.Hard samples are mined based on the loss values,dividing the data set into hard and easy samples.Second,elastic weight consolidation(EWC)is used to train the model on hard samples,effectively preventing information forgetting.Finally,the feature space domain adaptation is introduced to optimize the feature space by minimizing the Kullback–Leibler divergence of the feature distributions.Experimental results show that the proposed SmdaNet method outperforms existing approaches in terms of classification accuracy,robustness and interpretability on the penicillin simulation and Tennessee Eastman process datasets.展开更多
By combining with an improved model on engraving process,a two-phase flow interior ballistic model has been proposed to accurately predict the flow and energy conversion behaviors of pyrotechnic actuators.Using comput...By combining with an improved model on engraving process,a two-phase flow interior ballistic model has been proposed to accurately predict the flow and energy conversion behaviors of pyrotechnic actuators.Using computational fluid dynamics(CFD),the two-phase flow and piston engraving characteristics of a pyrotechnic actuator are investigated.Initially,the current model was utilized to examine the intricate,multi-dimensional flow,and energy conversion characteristics of the propellant grains and combustion gas within the pyrotechnic actuator chamber.It was discovered that the combustion gas on the wall's constant transition from potential to kinetic energy,along with the combined effect of the propellant motion,are what create the pressure oscillation within the chamber.Additionally,a numerical analysis was conducted to determine the impact of various parameters on the pressure oscillation and piston motion,including pyrotechnic charge,pyrotechnic particle size,and chamber structural dimension.The findings show that decreasing the pyrotechnic charge will lower the terminal velocity,while increasing and decreasing the pyrotechnic particle size will reduce the pressure oscillation in the chamber.The pyrotechnic particle size has minimal bearing on the terminal velocity.The results of this investigation offer a trustworthy forecasting instrument for comprehending and creating pyrotechnic actuator designs.展开更多
Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem....Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.展开更多
For a long time,the conventional superplastic forming temperature for Ti alloys is generally too high(~900-920℃),which leads to too long production cycles,heavy surface oxidation,and property reduction.In this study,...For a long time,the conventional superplastic forming temperature for Ti alloys is generally too high(~900-920℃),which leads to too long production cycles,heavy surface oxidation,and property reduction.In this study,an ultrafine bimodal microstructure,consisting of ultrafine equiaxed microstructure(0.66μm)and 43.3%lamellar microstructure,was achieved in the Ti-6Al-4V alloy by friction stir processing(FSP).The low-temperature superplastic behavior and deformation mechanism of the FSP Ti-6Al-4V alloy were investigated at temperatures of 550-675℃and strain rates ranging from 1×10^(−4)to 3×10^(−3)s^(−1).The FSP alloy exhibited superplastic elongations of>200%at the temperature range from 550 to 650℃,and an optimal superplastic elongation of 611%was achieved at 625℃and 1×10^(−4)s^(−1).This is the first time to report the low-temperature superplasticity of the bimodal microstructure in Ti alloys.Grain boundary sliding was identified as the dominant deformation mechanism,which was effectively accommodated by the comprehensive effect of dislocation-inducedβphase precipitation and dynamic spheroidization of the lamellar structure.This study provides a novel insight into the low-temperature superplastic deformation behavior of the bimodal microstructure.展开更多
Models that predict a forest stand’s evolution are essential for developing plans for sustainable management.A simple mathematical framework was developed that con-siders the individual tree and stand basal area unde...Models that predict a forest stand’s evolution are essential for developing plans for sustainable management.A simple mathematical framework was developed that con-siders the individual tree and stand basal area under random resource competition and is based on two assumptions:(1)a sigmoid-type stochastic process governs tree and stand basal area dynamics of living and dying trees,and(2)the total area that a tree may potentially occupy determines the number of trees per hectare.The most effective method to satisfy these requirements is formalizing each tree diameter and potentially occupied area using Gompertz-type stochastic differential equations governed by fixed and mixed-effect parameters.Data from permanent experimental plots from long-term Lithuania experiments were used to construct the tree and stand basal area models.The new models were relatively unbiased for live trees of all species,including silver birch(Betula pen-dula Roth)and downy birch(Betula pubescens Ehrh.),[spruce(Picea abies),and pine(Pinus sylvestris)].Less reliable predic-tions were made for the basal area of dying trees.Pines gave the highest accuracy prediction of mean basal area among all live trees.The mean basal area prediction for all dying trees was lower than that for live trees.Among all species,pine also had the best average basal area prediction accuracy for live trees.Newly developed basal area growth and yield models can be recommended despite their complex formulation and implementation challenges,particularly in situations when data is scarce.This is because the newly observed plot provides sufficient information to calibrate random effects.展开更多
The latest progress in the process optimization and stability improvement of third-generation cephalosporins in recent years was reviewed.The introduction of green chemistry,enzyme catalysis,nanotechnology,lyophilizat...The latest progress in the process optimization and stability improvement of third-generation cephalosporins in recent years was reviewed.The introduction of green chemistry,enzyme catalysis,nanotechnology,lyophilization,and nitrogen-filled packaging technologies can only improve production efficiency and reduce the generation of by-products,but also significantly extend the shelf life of drugs.In the future,process automation and intelligent technology will further optimize the large-scale production process,and the combination of nanotechnology and precision drug delivery will promote the improvement of effect in clinical applications.展开更多
Rhizoma Atractylodis has a long history of medicinal use,a broad spectrum of applications,and diverse clinical utility.This review provides a comprehensive overview of Rhizoma Atractylodis,focusing on its processing,c...Rhizoma Atractylodis has a long history of medicinal use,a broad spectrum of applications,and diverse clinical utility.This review provides a comprehensive overview of Rhizoma Atractylodis,focusing on its processing,chemical composition,pharmacological effects,and product development.Processing methods include stir-frying with bran,stir-frying until cooked,and rinsing with rice-washed water.The chemical composition includes terpenes,alkynes,sugars and their glycosides,organic acids and their esters,coumarins,and other compounds.Its pharmacological effects include the treatment of gastrointestinal diseases,arthritis,liver diseases,lung diseases,cancer,cardiovascular conditions,etc.Finally,product development prospects include decoction-ready products,Chinese patent medicines,health products and daily necessities,epidemic prevention,and veterinary medicine.Notably,this review simultaneously discusses,analyzes,and explores the quality control and industrial development of Rhizoma Atractylodis,offering guidance for advancing related industries,enhancing production quality control,and exploring innovative strategies for the clinical application and development of new Rhizoma Atractylodis-based products.展开更多
Whole grain foods are valued for their health benefits but face persistent challenges related to sensory acceptability,storage stability,and processing complexity.This review evaluates superheated steam(SHS)technology...Whole grain foods are valued for their health benefits but face persistent challenges related to sensory acceptability,storage stability,and processing complexity.This review evaluates superheated steam(SHS)technology as a disruptive approach to whole grain processing,highlighting its ability to enhance nutritional retention,modulate microstructure,and extend shelf-life under low-oxygen,high-temperature conditions.SHS suppresses oxidative degradation,restructures starch–protein–lipid matrices,and inactivates enzymes and spoilage microorganisms with minimal nutrient loss.Despite these advantages,SHS faces limitations,including uneven heat penetration in dense kernels,component specific thermal sensitivity and higher equipment costs.By integrating mechanistic insights with practical outcomes,this paper synthesizes recent advances,highlights key scientific and technological gaps,and proposes strategies for industrial application and integration with intelligent food processing systems.This review provides a concise theoretical and technical roadmap for sustainable whole grain innovation.展开更多
Objective To optimize the ultrasonic extraction process for benzoic acid as a harmful substance in Paeonia lactiflora Pall.(P.lactiflora Pall.).Methods Methanol and ethanol solutions at different concentration gradien...Objective To optimize the ultrasonic extraction process for benzoic acid as a harmful substance in Paeonia lactiflora Pall.(P.lactiflora Pall.).Methods Methanol and ethanol solutions at different concentration gradients(25,50,75%)were prepared to investigate the effects of extraction solvents on the extraction efficiency of benzoic acid.The influences of ultrasonic frequency(35,50 Hz),ultrasonic power(40,60,80,100 W),ultrasonic time(10,20,30,40,50,60 minutes),and ultrasonic temperature(20,30,40,50℃)on the extraction efficiency were examined.Orthogonal experiments were conducted to analyze the effects of temperature,time,and ultrasonic power on the extraction efficiency and to screen the optimal ultrasonic extraction process.Results Various influencing factors had certain effects on the extraction efficiency of benzoic acid from P.lactiflora Pall.Single-factor analysis revealed that 25%methanol,ultrasonic frequency of 50 Hz,ultrasonic power of 40 W,ultrasonic time of 10minutes,and ultrasonic temperature of 30℃yielded the highest extraction efficiency for benzoic acid.The order of influence of different factors on the extraction efficiency was temperature>time>power.The optimal conditions obtained from orthogonal experiments were:extraction solvent of 25%methanol,ultrasonic frequency of 50 Hz,ultrasonic time of 20 minutes,ultrasonic power of 40 W,and ultrasonic temperature of 30℃.Conclusion Under the conditions of 25%methanol as the extraction solvent,ultrasonic frequency of 50 Hz,ultrasonic time of 20 minutes,ultrasonic power of 40 W,and ultrasonic temperature of 30℃,the extraction efficiency of benzoic acid from P.lactiflora Pall.was the highest.This method offers advantages such as simple operation,small sample size requirement,and low solvent consumption,providing a reliable analytical approach for quality control and safety evaluation of P.lactiflora Pall.展开更多
High ammonia-nitrogen digestate has become a key bottleneck limiting the anaerobic digestion of organic solid waste.Vacuum ammonia stripping can simultaneously remove and recover ammonia nitrogen,which has attracted a...High ammonia-nitrogen digestate has become a key bottleneck limiting the anaerobic digestion of organic solid waste.Vacuum ammonia stripping can simultaneously remove and recover ammonia nitrogen,which has attracted a lot of attention in recent years.To investigate the parameter effects on the efficiency and mass transfer,five combination conditions(53℃ 15 kPa,60°C 20 kPa,65°C 25 kPa,72°C 35 kPa,and 81°C 50 kPa)were conducted for ammonia stripping of sludge digestate.The results showed that 80%of ammonia nitrogen was stripped in 45 min for all experimental groups,but the ammonia transfer coefficient varied under different conditions,which increased with the rising of boiling point temperature,and reached the maximum value(39.0 mm/hr)at 81°C 50 kPa.The ammonia nitrogen removal efficiency was more than 80%for 30 min vacuum stripping after adjusting the initial pH to above 9.5,and adjustment of the initial alkalinity also affects the pH value of liquid digestate.It was found that pH and alkalinity are the key factors influencing the ammonia nitrogen dissociation and removal efficiency,while temperature and vacuum mainly affect the ammonia nitrogen mass transfer and removal velocity.In terms of the mechanism of vacuum ammonia stripping,it underwent alkalinity destruction,pH enhancement,ammonia nitrogen dissociation,and free ammonia removal.In this study,two-stage experiments of alkalinity destruction and ammonia removal were also carried out,which showed that the two-stage configuration was beneficial for ammonia removal.It provides a theoretical basis and practical technology for the vacuum ammonia stripping from liquid digestate of organic solid waste.展开更多
基金funded by scientific research projects under Grant JY2024B011.
文摘With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.
文摘Purpose–The precast concrete slab track(PST)has advantages of fewer maintenance frequencies,better smooth rides and structural stability,which has been widely applied in urban rail transit.Precise positioning of precast concrete slab(PCS)is vital for keeping the initial track regularity.However,the cast-in-place process of the self-compacting concrete(SCC)filling layer generally causes a large deformation of PCS due to the water-hammer effect of flowing SCC,even cracking of PCS.Currently,the buoyancy characteristic and influencing factors of PCS during the SCC casting process have not been thoroughly studied in urban rail transit.Design/methodology/approach–In this work,a Computational Fluid Dynamics(CFD)model is established to calculate the buoyancy of PCS caused by the flowing SCC.The main influencing factors,including the inlet speed and flowability of SCC,have been analyzed and discussed.A new structural optimization scheme has been proposed for PST to reduce the buoyancy caused by the flowing SCC.Findings–The simulation and field test results showed that the buoyancy and deformation of PCS decreased obviously after adopting the new scheme.Originality/value–The findings of this study can provide guidance for the control of the deformation of PCS during the SCC construction process.
基金supported by the China Postdoctoral Science Foundation(No.2023T160088)the Youth Fund of the National Natural Science Foundation of China(No.52304324).
文摘Magnesium and magnesium alloys,serving as crucial lightweight structural materials and hydrogen storage elements,find extensive applications in space technology,aviation,automotive,and magnesium-based hydrogen industries.The global production of primary magnesium has reached approximately 1.2 million tons per year,with anticipated diversification in future applications and significant market demand.Nevertheless,approximately 80%of the world’s primary magnesium is still manufactured through the Pidgeon process,grappling with formidable issues including high energy consumption,massive carbon emission,significant resource depletion,and environmental pollution.The implementation of the relative vacuum method shows potential in breaking through technological challenges in the Pidgeon process,facilitating clean,low-carbon continuous magnesium smelting.This paper begins by introducing the principles of the relative vacuum method.Subsequently,it elucidates various innovative process routes,including relative vacuum ferrosilicon reduction,aluminum thermal reduction co-production of spinel,and aluminum thermal reduction co-production of calcium aluminate.Finally,and thermodynamic foundations of the relative vacuum,a quantitative analysis of the material,energy flows,carbon emission,and production cost for several new processes is conducted,comparing and analyzing them against the Pidgeon process.The study findings reveal that,with identical raw materials,the relative vacuum silicon thermal reduction process significantly decreases raw material consumption,energy consumption,and carbon dioxide emissions by 15.86%,30.89%,and 26.27%,respectively,compared to the Pidgeon process.The relative vacuum process,using magnesite as the raw material and aluminum as the reducing agent,has the lowest magnesium-to-feed ratio,at only 3.385.Additionally,its energy consumption and carbon dioxide emissions are the lowest,at 1.817 tce/t Mg and 7.782 t CO_(2)/t Mg,respectively.The energy consumption and carbon emissions of the relative vacuum magnesium smelting process co-producing calcium aluminate(12CaO·7Al_(2)O_(3),3CaO·Al_(2)O_(3),and CaO·Al_(2)O_(3))are highly correlated with the consumption of dolomite in the raw materials.When the reduction temperature is around 1473.15 K,the critical volume fraction of magnesium vapor for different processes varies within the range of 5%–40%.Production cost analysis shows that the relative vacuum primary magnesium smelting process has significant economic benefits.This paper offers essential data support and theoretical guidance for achieving energy efficiency,carbon reduction in magnesium smelting,and the industrial adoption of innovative processes.
基金financially supported by the National Natural Science Foundation of China(No.52271091)the National Key Research and Development Program of China(No.2021YFB3701100)the Natural Science Foundation Project of Ningxia Province(No.2023AAC03324).
文摘The Mg-9Li-1Zn(LZ91)alloy was subjected to an ultrasonic surface rolling process(USRP)with varying passes for the purpose of modifying its surface state.The USRP transformed surface residual stress from initial tensile stress to compressive stress,decreasing the surface roughness and increasing the ratio of the β-Li phase.The USRPed LZ91 sample(3 passes)showed superior corrosion resistance,with the corrosion current density changing from 57.11 to 24.70μA cm^(-2),and the polarization resistance increasing from 576.3 to 1146.1Ωcm^(2).According to the corrosion procedure evaluations,in situ observation revealed that the LZ91 alloy initially experiences pitting,which subsequently develops into cracking.The substantial area coverage of the β-Li phase facilitates the formation of a protective film on the surface,effectively delaying localized corrosion.
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
基金financially by the National Research Council of Thailand(NRCT)under Contract No.N42A670894.
文摘This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving average behavior—SARMA(1,1)L under exponential white noise.Unlike previous works that rely on simplified models such as AR(1)or assume independence,this research derives for the first time an exact two-sided Average Run Length(ARL)formula for theModified EWMAchart under SARMA(1,1)L conditions,using a mathematically rigorous Fredholm integral approach.The derived formulas are validated against numerical integral equation(NIE)solutions,showing strong agreement and significantly reduced computational burden.Additionally,a performance comparison index(PCI)is introduced to assess the chart’s detection capability.Results demonstrate that the proposed method exhibits superior sensitivity to mean shifts in autocorrelated environments,outperforming existing approaches.The findings offer a new,efficient framework for real-time quality control in complex seasonal processes,with potential applications in environmental monitoring and intelligent manufacturing systems.
基金supported by the Cross-disciplinary Integration Project of Fujian Agriculture and Forestry University(71202103C)Science and Technology Projects of Fuzhou Ocean Research Institute(2022F16).
文摘With the increasing per capita demand for animal protein,there is a growing interest in the abundant abalone protein resources.Abalone proteins are known for their nutritional and functional properties that contribute to flavor and texture.We systematically constructed the relationship between abalone protein,processing,and proteomics.This paper reviews the nutritional properties of abalone proteins and evaluates the effects of different thermal processing techniques,non-thermal processing,and freezing on abalone proteins.In addition,we synthesize published abalone proteomics studies and the use of proteomics technology to better elucidate the quality changes of abalone and its products,and as a technical basis for the study of blue food marker proteins.It is important direction to clearly explain the protein composition and meat quality mechanism of abalone in the processing and storage by proteomic.During various types of thermal processing,non-thermal processing,and freezing of abalone,the various chemical forces between protein molecules are disrupted,which in turn leads to different degrees of denaturation,aggregation,and gelation,which may have an impact on the organoleptic properties,bioavailability,and digestibility of abalone muscle.Proteomics is used in abalone biology studies to understand developmental biology,physiology,disease,stress,and species identification and can also be a powerful tool to characterize processing methods on abalone quality properties.
文摘This paper introduces a computational cognitive architecture that serves as a comprehensive computational theory of the human mind,from cognitive science and computational psychology.The cognitive architecture(named Clarion)has been justified by,and validated against,psychological data,findings,and theoretical constructs.One important theoretical background for it is the dual-process theories,which led to its overall two-level structuring in a hybrid neuro-symbolic way.Furthermore,given the recent advances in AI and computing technology,LLMs are being incorporated into the model to better capture human intuition and instinct(and implicit processes in general),in order to further enhance Clarion.Integrating Clarion and LLMs can also help to develop AI systems that are more capable,more reliable,and more human-like.Overall,the paper advocates a multidisciplinary approach towards developing better models for cognitive science and for AI.
基金supported in part by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(RS-2023-00239657)in part by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.RS-2024-00423772)。
文摘Nondestructive testing(NDT)methods such as visual inspection and ultrasonic testing are widely applied in manufacturing quality control,but they remain limited in their ability to detect defect characteristics.Visual inspection depends strongly on operator experience,while ultrasonic testing requires physical contact and stable coupling conditions that are difficult to maintain in production lines.These constraints become more pronounced when defect-related information is scarce or when background noise interferes with signal acquisition in manufacturing processes.This study presents a non-contact acoustic method for diagnosing defects in scroll compressors during the manufacturing process.The diagnostic approach leverages Mel-frequency cepstral coefficients(MFCC),and shorttime Fourier transform(STFT)parameters to capture the rotational frequency and harmonic characteristics of the scroll compressor.These parameters enable the extraction of defect-related features even in the presence of background noise.A convolutional neural network(CNN)model was constructed using MFCCs and spectrograms as image inputs.The proposed method was validated using acoustic data collected from compressors operated at a fixed rotational speed under real manufacturing process.The method identified normal operation and three defect types.These results demonstrate the applicability of this method in noise-prone manufacturing environments and suggest its potential for improving product quality,manufacturing reliability and productivity.
基金funding of the joint Polish-German project SUPILMIX(PR-1173/27)by the German Research Foundation(DFG,Deutsche Forschungsgemeinschaft)+1 种基金funding from the Alexander von Humboldt Foundation.D.L.the German Chemical Industry Fund for the financial support through a Liebig Fellowship.
文摘Supercapacitors are efficient and versatile energy storage devices,offering remarkable power density,fast charge/discharge rates,and exceptional cycle life.As research continues to push the boundaries of their performance,electrode fabrication techniques are critical aspects influencing the overall capabilities of supercapacitors.Herein,we aim to shed light on the advantages offered by dry electrode processing for advanced supercapacitors.Notably,our study explores the performance of these electrodes in three different types of electrolytes:organic,ionic liquids,and quasi-solid states.By examining the impact of dry electrode processing on various electrode and electrolyte systems,we show valuable insights into the versatility and efficacy of this technique.The supercapacitors employing dry electrodes demonstrated significant improvements compared with conventional wet electrodes,with a lifespan extension of+45%in organic,+192%in ionic liquids,and+84%in quasi-solid electrolytes.Moreover,the increased electrode densities achievable through the dry approach directly translate to improved volumetric outputs,enhancing energy storage capacities within compact form factors.Notably,dry electrode-prepared supercapacitors outperformed their wet electrode counterparts,exhibiting a higher energy density of 6.1 Wh cm^(-3)compared with 4.7 Wh cm^(-3)at a high power density of 195Wcm^(-3),marking a substantial 28%energy improvement in the quasi-solid electrolyte.
基金support from the following foundations:the National Natural Science Foundation of China(62322309,62433004)Shanghai Science and Technology Innovation Action Plan(23S41900500)Shanghai Pilot Program for Basic Research(22TQ1400100-16).
文摘Fault diagnosis in industrial process is essential for ensuring production safety and efficiency.However,existing methods exhibit limited capability in recognizing hard samples and struggle to maintain consistency in feature distributions across domains,resulting in suboptimal performance and robustness.Therefore,this paper proposes a fault diagnosis neural network for hard sample mining and domain adaptive(SmdaNet).First,the method uses deep belief networks(DBN)to build a diagnostic model.Hard samples are mined based on the loss values,dividing the data set into hard and easy samples.Second,elastic weight consolidation(EWC)is used to train the model on hard samples,effectively preventing information forgetting.Finally,the feature space domain adaptation is introduced to optimize the feature space by minimizing the Kullback–Leibler divergence of the feature distributions.Experimental results show that the proposed SmdaNet method outperforms existing approaches in terms of classification accuracy,robustness and interpretability on the penicillin simulation and Tennessee Eastman process datasets.
基金supported by the National Natural Science Foundation of China(Grant No.11972194).
文摘By combining with an improved model on engraving process,a two-phase flow interior ballistic model has been proposed to accurately predict the flow and energy conversion behaviors of pyrotechnic actuators.Using computational fluid dynamics(CFD),the two-phase flow and piston engraving characteristics of a pyrotechnic actuator are investigated.Initially,the current model was utilized to examine the intricate,multi-dimensional flow,and energy conversion characteristics of the propellant grains and combustion gas within the pyrotechnic actuator chamber.It was discovered that the combustion gas on the wall's constant transition from potential to kinetic energy,along with the combined effect of the propellant motion,are what create the pressure oscillation within the chamber.Additionally,a numerical analysis was conducted to determine the impact of various parameters on the pressure oscillation and piston motion,including pyrotechnic charge,pyrotechnic particle size,and chamber structural dimension.The findings show that decreasing the pyrotechnic charge will lower the terminal velocity,while increasing and decreasing the pyrotechnic particle size will reduce the pressure oscillation in the chamber.The pyrotechnic particle size has minimal bearing on the terminal velocity.The results of this investigation offer a trustworthy forecasting instrument for comprehending and creating pyrotechnic actuator designs.
基金supported by the Start-up Fund from Hainan University(No.KYQD(ZR)-20077)。
文摘Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.
基金supported by the funding from the Shi Changxu Innovation Center for Advanced Materials(No.SCXKFJJ202210)the National Natural Science Foundation of China(No.52271043)+2 种基金the Youth Innovation Promotion Association of the Chinese Academy of Sciences(No.2021193)the Liaoning Province Excellent Youth Foundation(No.2024JH3/10200021)the Liaoning Revitalization Talents Program(No.XLYC2403094).
文摘For a long time,the conventional superplastic forming temperature for Ti alloys is generally too high(~900-920℃),which leads to too long production cycles,heavy surface oxidation,and property reduction.In this study,an ultrafine bimodal microstructure,consisting of ultrafine equiaxed microstructure(0.66μm)and 43.3%lamellar microstructure,was achieved in the Ti-6Al-4V alloy by friction stir processing(FSP).The low-temperature superplastic behavior and deformation mechanism of the FSP Ti-6Al-4V alloy were investigated at temperatures of 550-675℃and strain rates ranging from 1×10^(−4)to 3×10^(−3)s^(−1).The FSP alloy exhibited superplastic elongations of>200%at the temperature range from 550 to 650℃,and an optimal superplastic elongation of 611%was achieved at 625℃and 1×10^(−4)s^(−1).This is the first time to report the low-temperature superplasticity of the bimodal microstructure in Ti alloys.Grain boundary sliding was identified as the dominant deformation mechanism,which was effectively accommodated by the comprehensive effect of dislocation-inducedβphase precipitation and dynamic spheroidization of the lamellar structure.This study provides a novel insight into the low-temperature superplastic deformation behavior of the bimodal microstructure.
基金supported by the Horizon Europe Framework Programme(HORIZON),call Teaming for Excellence(HORIZONWIDERA-2022-ACCESS-01-two-stage)-Creation of the Centre of Excellence in Smart Forestry“Forest 4.0”No.101059985″This research was cofunded by FOREST 4.0-“Ekscelencijos centras tvariai miško bioekonomikai vystyti”(Nr.10-042-P-0002).
文摘Models that predict a forest stand’s evolution are essential for developing plans for sustainable management.A simple mathematical framework was developed that con-siders the individual tree and stand basal area under random resource competition and is based on two assumptions:(1)a sigmoid-type stochastic process governs tree and stand basal area dynamics of living and dying trees,and(2)the total area that a tree may potentially occupy determines the number of trees per hectare.The most effective method to satisfy these requirements is formalizing each tree diameter and potentially occupied area using Gompertz-type stochastic differential equations governed by fixed and mixed-effect parameters.Data from permanent experimental plots from long-term Lithuania experiments were used to construct the tree and stand basal area models.The new models were relatively unbiased for live trees of all species,including silver birch(Betula pen-dula Roth)and downy birch(Betula pubescens Ehrh.),[spruce(Picea abies),and pine(Pinus sylvestris)].Less reliable predic-tions were made for the basal area of dying trees.Pines gave the highest accuracy prediction of mean basal area among all live trees.The mean basal area prediction for all dying trees was lower than that for live trees.Among all species,pine also had the best average basal area prediction accuracy for live trees.Newly developed basal area growth and yield models can be recommended despite their complex formulation and implementation challenges,particularly in situations when data is scarce.This is because the newly observed plot provides sufficient information to calibrate random effects.
基金Supported by the Funds from Central Government for Guiding Local Science and Technology Development(ZY20230102)Planning Project of Scientific Research and Technology Development in Guilin(20220104-4,20210202-1)Science and Technology Planing Project of Guangxi(Guike AB24010263).
文摘The latest progress in the process optimization and stability improvement of third-generation cephalosporins in recent years was reviewed.The introduction of green chemistry,enzyme catalysis,nanotechnology,lyophilization,and nitrogen-filled packaging technologies can only improve production efficiency and reduce the generation of by-products,but also significantly extend the shelf life of drugs.In the future,process automation and intelligent technology will further optimize the large-scale production process,and the combination of nanotechnology and precision drug delivery will promote the improvement of effect in clinical applications.
基金by Hubei Provincial Natural Science Foundation of China(2023AFD154)Wuhan Municipal Health Scientific Research Fund(WZ24Z22).
文摘Rhizoma Atractylodis has a long history of medicinal use,a broad spectrum of applications,and diverse clinical utility.This review provides a comprehensive overview of Rhizoma Atractylodis,focusing on its processing,chemical composition,pharmacological effects,and product development.Processing methods include stir-frying with bran,stir-frying until cooked,and rinsing with rice-washed water.The chemical composition includes terpenes,alkynes,sugars and their glycosides,organic acids and their esters,coumarins,and other compounds.Its pharmacological effects include the treatment of gastrointestinal diseases,arthritis,liver diseases,lung diseases,cancer,cardiovascular conditions,etc.Finally,product development prospects include decoction-ready products,Chinese patent medicines,health products and daily necessities,epidemic prevention,and veterinary medicine.Notably,this review simultaneously discusses,analyzes,and explores the quality control and industrial development of Rhizoma Atractylodis,offering guidance for advancing related industries,enhancing production quality control,and exploring innovative strategies for the clinical application and development of new Rhizoma Atractylodis-based products.
基金supported by the Central Government-Guided Local Science and Technology Development Fund(2024ZY0100)。
文摘Whole grain foods are valued for their health benefits but face persistent challenges related to sensory acceptability,storage stability,and processing complexity.This review evaluates superheated steam(SHS)technology as a disruptive approach to whole grain processing,highlighting its ability to enhance nutritional retention,modulate microstructure,and extend shelf-life under low-oxygen,high-temperature conditions.SHS suppresses oxidative degradation,restructures starch–protein–lipid matrices,and inactivates enzymes and spoilage microorganisms with minimal nutrient loss.Despite these advantages,SHS faces limitations,including uneven heat penetration in dense kernels,component specific thermal sensitivity and higher equipment costs.By integrating mechanistic insights with practical outcomes,this paper synthesizes recent advances,highlights key scientific and technological gaps,and proposes strategies for industrial application and integration with intelligent food processing systems.This review provides a concise theoretical and technical roadmap for sustainable whole grain innovation.
基金supported by the Heilongjiang Provincial Administration of Traditional Chinese Medicine Project(ZHY18-153).
文摘Objective To optimize the ultrasonic extraction process for benzoic acid as a harmful substance in Paeonia lactiflora Pall.(P.lactiflora Pall.).Methods Methanol and ethanol solutions at different concentration gradients(25,50,75%)were prepared to investigate the effects of extraction solvents on the extraction efficiency of benzoic acid.The influences of ultrasonic frequency(35,50 Hz),ultrasonic power(40,60,80,100 W),ultrasonic time(10,20,30,40,50,60 minutes),and ultrasonic temperature(20,30,40,50℃)on the extraction efficiency were examined.Orthogonal experiments were conducted to analyze the effects of temperature,time,and ultrasonic power on the extraction efficiency and to screen the optimal ultrasonic extraction process.Results Various influencing factors had certain effects on the extraction efficiency of benzoic acid from P.lactiflora Pall.Single-factor analysis revealed that 25%methanol,ultrasonic frequency of 50 Hz,ultrasonic power of 40 W,ultrasonic time of 10minutes,and ultrasonic temperature of 30℃yielded the highest extraction efficiency for benzoic acid.The order of influence of different factors on the extraction efficiency was temperature>time>power.The optimal conditions obtained from orthogonal experiments were:extraction solvent of 25%methanol,ultrasonic frequency of 50 Hz,ultrasonic time of 20 minutes,ultrasonic power of 40 W,and ultrasonic temperature of 30℃.Conclusion Under the conditions of 25%methanol as the extraction solvent,ultrasonic frequency of 50 Hz,ultrasonic time of 20 minutes,ultrasonic power of 40 W,and ultrasonic temperature of 30℃,the extraction efficiency of benzoic acid from P.lactiflora Pall.was the highest.This method offers advantages such as simple operation,small sample size requirement,and low solvent consumption,providing a reliable analytical approach for quality control and safety evaluation of P.lactiflora Pall.
基金supported by the National Key Research and Development Program of China(No.2020YFC1908702)the National Natural Science Foundation of China(No.52131002)+1 种基金the Science and Technology Commission of Shanghai Municipality(No.22dz1209200)China Three Gorges Corporation(No.202403018).
文摘High ammonia-nitrogen digestate has become a key bottleneck limiting the anaerobic digestion of organic solid waste.Vacuum ammonia stripping can simultaneously remove and recover ammonia nitrogen,which has attracted a lot of attention in recent years.To investigate the parameter effects on the efficiency and mass transfer,five combination conditions(53℃ 15 kPa,60°C 20 kPa,65°C 25 kPa,72°C 35 kPa,and 81°C 50 kPa)were conducted for ammonia stripping of sludge digestate.The results showed that 80%of ammonia nitrogen was stripped in 45 min for all experimental groups,but the ammonia transfer coefficient varied under different conditions,which increased with the rising of boiling point temperature,and reached the maximum value(39.0 mm/hr)at 81°C 50 kPa.The ammonia nitrogen removal efficiency was more than 80%for 30 min vacuum stripping after adjusting the initial pH to above 9.5,and adjustment of the initial alkalinity also affects the pH value of liquid digestate.It was found that pH and alkalinity are the key factors influencing the ammonia nitrogen dissociation and removal efficiency,while temperature and vacuum mainly affect the ammonia nitrogen mass transfer and removal velocity.In terms of the mechanism of vacuum ammonia stripping,it underwent alkalinity destruction,pH enhancement,ammonia nitrogen dissociation,and free ammonia removal.In this study,two-stage experiments of alkalinity destruction and ammonia removal were also carried out,which showed that the two-stage configuration was beneficial for ammonia removal.It provides a theoretical basis and practical technology for the vacuum ammonia stripping from liquid digestate of organic solid waste.
