Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley a...Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.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
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
The growing demand for carbon neutrality has heightened the focus on CO_(2)hydrogenation as a viable strategy for transforming carbon dioxide into valuable chemicals and fuels.Advanced machine learning(ML)approaches i...The growing demand for carbon neutrality has heightened the focus on CO_(2)hydrogenation as a viable strategy for transforming carbon dioxide into valuable chemicals and fuels.Advanced machine learning(ML)approaches integrate materials science with artificial intelligence,enabling scientists to identify hidden patterns in datasets,make informed decisions,and reduce the need for labor-intensive,repetitive experimentation.This review provides a comprehensive overview of ML applications in the thermocatalytic hydrogenation of CO_(2).Following an introduction to ML tools and workflows,various ML algorithms employed in CO_(2)hydrogenation are systematically categorized and reviewed.Next,the application of ML in catalyst discovery is discussed,highlighting its role in identifying optimal compositions and structures.Then,ML-driven strategies for process optimization,particularly in enhancing CO_(2)conversion and product selectivity,are examined.Studies modeling descriptors,spanning catalyst properties and reaction conditions,to predict catalytic performance are analyzed.Consequently,ML-based mechanistic studies are reviewed to elucidate reaction pathways,identify key intermediates,and optimize catalyst performance.Finally,key challenges and future perspectives in leveraging ML for advancing CO_(2)hydrogenation research are presented.展开更多
Objective:To investigate the application effects of intelligent guidance systems in optimizing health check-up process management.Methods:A total of 400 examinees who underwent physical examinations at the hospital’s...Objective:To investigate the application effects of intelligent guidance systems in optimizing health check-up process management.Methods:A total of 400 examinees who underwent physical examinations at the hospital’s Health Management Center from January to December 2024 were randomly divided into a control group(200 cases)and an observation group(200 cases).The control group used traditional manual guidance methods,while the observation group employed the intelligent guidance system.The study compared two groups in terms of completion time,waiting time for each procedure,check-up efficiency scores,examinee satisfaction,and report issuance time.Results:The overall examination time in the observation group(85.3±12.7 minutes)was significantly shorter than that in the control group(142.6±18.5 minutes)(P<0.01);average waiting time per procedure decreased by 62.4%;check-up efficiency scores(8.9±0.8 points)were significantly higher than those in the control group(5.2±1.1 points)(P<0.01);satisfaction reached 96.5%,significantly higher than the control group’s 78.0%(P<0.01);and report issuance time was advanced by 1.5 days.Conclusion:Intelligent guidance systems can significantly optimize check-up processes,improve work efficiency,and examinee satisfaction,demonstrating significant clinical application value.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
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
Aluminum alloys manufactured using traditional processes are increasingly unable to meet the high flexibility and performance requirements of modern engineering.In this study,Al-Mg-Sc-Zr alloys were manufactured via l...Aluminum alloys manufactured using traditional processes are increasingly unable to meet the high flexibility and performance requirements of modern engineering.In this study,Al-Mg-Sc-Zr alloys were manufactured via laser powder bed fusion(LPBF)to obtain high-performance aluminum alloys.To this end,process parameter optimization and heat treatment were adopted.The optimal process parameters were determined by initially analyzing the relative density and defect distribution under varying energy densities.The sample obtained under the optimal process parameters exhibited a relative density of 99.84%.Subsequently,the corresponding phase compositions,microstructures,and mechanical performance of the as-fabricated specimens were determined using the optimal process parameters before and after heat treatment.The microstructures of the samples showed typical equiaxed columnar bimodal grain structures,with Al_(3)(Sc,Zr)precipitates detected.The samples exhibited no significant anisotropy before and after heat treatment,while the grain orientation differences were dominated by high-angle grain boundaries.The mechanical properties of all the samples were characterized using tensile and hardness tests.The yield strength,ultimate tensile strength,and elongation of the sample were 475.0 MPa,508.2 MPa,and 8.3%,respectively.Overall,samples with high density,low porosity,high strength,and high plasticity were obtained by process parameter optimization and appropriate heat treatment.展开更多
A reasonable process plan is an important basis for implementing wire arc additive and subtractive hybrid manufacturing(ASHM),and a new optimization method is proposed.Firstly,the target parts and machining tools are ...A reasonable process plan is an important basis for implementing wire arc additive and subtractive hybrid manufacturing(ASHM),and a new optimization method is proposed.Firstly,the target parts and machining tools are modeled by level set functions.Secondly,the mathematical model of the additive direction optimization problem is established,and an improved particle swarm optimization algorithm is designed to decide the best additive direction.Then,the two-step strategy is used to plan the hybrid manufacturing alternating sequence.The target parts are directly divided into various processing regions;each processing region is optimized based on manufacturability and manufacturing efficiency,and the optimal hybrid manufacturing alternating sequence is obtained by merging some processing regions.Finally,the method is used to outline the process plan of the designed example model and applied to the actual hybrid manufacturing process of the model.The manufacturing result shows that the method can meet the main considerations in hybrid manufacturing.In addition,the degree of automation of process planning is high,and the dependence on manual intervention is low.展开更多
The optimization of reaction processes is crucial for the green, efficient, and sustainable development of the chemical industry. However, how to address the problems posed by multiple variables, nonlinearities, and u...The optimization of reaction processes is crucial for the green, efficient, and sustainable development of the chemical industry. However, how to address the problems posed by multiple variables, nonlinearities, and uncertainties during optimization remains a formidable challenge. In this study, a strategy combining interpretable machine learning with metaheuristic optimization algorithms is employed to optimize the reaction process. First, experimental data from a biodiesel production process are collected to establish a database. These data are then used to construct a predictive model based on artificial neural network (ANN) models. Subsequently, interpretable machine learning techniques are applied for quantitative analysis and verification of the model. Finally, four metaheuristic optimization algorithms are coupled with the ANN model to achieve the desired optimization. The research results show that the methanol: palm fatty acid distillate (PFAD) molar ratio contributes the most to the reaction outcome, accounting for 41%. The ANN-simulated annealing (SA) hybrid method is more suitable for this optimization, and the optimal process parameters are a catalyst concentration of 3.00% (mass), a methanol: PFAD molar ratio of 8.67, and a reaction time of 30 min. This study provides deeper insights into reaction process optimization, which will facilitate future applications in various reaction optimization processes.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
[Objectives] To optimize the crystallization process of ceftriaxone sodium using response surface methodology (RSM) for enhancing both the crystallization rate and the quality of the final product. [Methods] Four key ...[Objectives] To optimize the crystallization process of ceftriaxone sodium using response surface methodology (RSM) for enhancing both the crystallization rate and the quality of the final product. [Methods] Four key factors, including crystallization temperature, stirring speed, solvent drop rate, and seed crystal content, were employed as independent variables, while the crystallization rate served as the response variable. The Box-Behnken response surface method was utilized for the optimization design. [Results] The optimal parameters for the crystallization process, determined through optimization, were as follows: a temperature of 10.6 ℃, a stirring rate of 150 rpm, a solvent drop rate of 1.50 mL/min, and a seed crystal content of 0.12 g. Validation tests conducted under these conditions yielded an average crystallization rate of 94.38% for the refined product. [Conclusions] The crystallization efficiency of ceftriaxone sodium is markedly enhanced, thereby offering substantial support for its industrial production and clinical application.展开更多
To solve the problems of deformation,micro-cracks,and residual tensile stress in laser cladding coatings,the technique of laser cladding with Fe-based memory alloy can be considered.However,the process of in-situ synt...To solve the problems of deformation,micro-cracks,and residual tensile stress in laser cladding coatings,the technique of laser cladding with Fe-based memory alloy can be considered.However,the process of in-situ synthesis of Fe-based memory alloy coatings is extremely complex.At present,there is no clear guidance scheme for its preparation process,which limits its promotion and application to some extent.Therefore,in this study,response surface methodology(RSM)was used to model the response surface between the target values and the cladding process parameters.The NSGA-2 algorithm was employed to optimize the process parameters.The results indicate that the composite optimization method consisting of RSM and the NSGA-2 algorithm can establish a more accurate model,with an error of less than 4.5%between the predicted and actual values.Based on this established model,the optimal scheme for process parameters corresponding to different target results can be rapidly obtained.The prepared coating exhibits a uniform structure,with no defects such as pores,cracks,and deformation.The surface roughness and microhardness of the coating are enhanced,the shaping quality of the coating is effectively improved,and the electrochemical corrosion performance of the coating in 3.5%NaCl solution is obviously better than that of the substrate,providing an important guide for engineering applications.展开更多
This paper examines the challenges in the technical briefing process for construction projects,including a three-level system and issues related to formalization.An optimization approaches was introduced based on the ...This paper examines the challenges in the technical briefing process for construction projects,including a three-level system and issues related to formalization.An optimization approaches was introduced based on the PDCA cycle,alongside the application of BIM and AR technologies.The key preparatory measures were outlined in this study and the functions of the management system was mentioned.Through case comparisons,this paper demonstrated that these optimizations can significantly improve efficiency and quality,support the development of an evaluation system to verify results,and highlight the critical role of organizational support.展开更多
In the project procurement management process of telecommunication enterprises,due to the complexity of technology,the professional procurement project manager is responsible for the whole process of professional proc...In the project procurement management process of telecommunication enterprises,due to the complexity of technology,the professional procurement project manager is responsible for the whole process of professional procurement in a one-stop way.The integration of this management process superficially improves labor productivity,but in essence lacks effective checks and balances and supervision.In order to supervise the project procurement management process and ensure the legal compliance of procurement management,this paper studies the project procurement management process of telecommunication enterprises,proposes the optimization process of project procurement management in the segmentation of purchasing manager-business manager,and constructs a matrix project procurement management model,which will contribute to the overall improvement of the telecommunication enterprises’procurement performance.展开更多
Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors an...Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors and performance defects,leading to a decline in product quality and affecting its service life.This study proposes a process parameter optimization method that considers the mechanical properties of printed specimens and production costs.To improve the quality of silicone printing samples and reduce production costs,three machine learning models,kernel extreme learning machine(KELM),support vector regression(SVR),and random forest(RF),were developed to predict these three factors.Training data were obtained through a complete factorial experiment.A new dataset is obtained using the Euclidean distance method,which assigns the elimination factor.It is trained with Bayesian optimization algorithms for parameter optimization,the new dataset is input into the improved double Gaussian extreme learning machine,and finally obtains the improved KELM model.The results showed improved prediction accuracy over SVR and RF.Furthermore,a multi-objective optimization framework was proposed by combining genetic algorithm technology with the improved KELM model.The effectiveness and reasonableness of the model algorithm were verified by comparing the optimized results with the experimental results.展开更多
In response to the challenges associated with the traditional synthesis process of hymenidin,such as complex reaction steps,low yields,high costs,and environmental concerns,the synthesis process has been significantly...In response to the challenges associated with the traditional synthesis process of hymenidin,such as complex reaction steps,low yields,high costs,and environmental concerns,the synthesis process has been significantly enhanced by optimizing reaction conditions,screening for efficient catalysts,and incorporating the concepts of green chemistry.The optimized process has significantly improved the synthesis efficiency and product quality of hymenidin,reduced production costs,and minimized environmental pollution,thereby providing robust support for its industrial production and broad application.展开更多
The packaging quality of coaxial laser diodes(CLDs)plays a pivotal role in determining their optical performance and long-term reliability.As the core packaging process,high-precision laser welding requires precise co...The packaging quality of coaxial laser diodes(CLDs)plays a pivotal role in determining their optical performance and long-term reliability.As the core packaging process,high-precision laser welding requires precise control of process parameters to suppress optical power loss.However,the complex nonlinear relationship between welding parameters and optical power loss renders traditional trial-and-error methods inefficient and imprecise.To address this challenge,a physics-informed(PI)and data-driven collaboration approach for welding parameter optimization is proposed.First,thermal-fluid-solid coupling finite element method(FEM)was employed to quantify the sensitivity of welding parameters to physical characteristics,including residual stress.This analysis facilitated the identification of critical factors contributing to optical power loss.Subsequently,a Gaussian process regression(GPR)model incorporating finite element simulation prior knowledge was constructed based on the selected features.By introducing physics-informed kernel(PIK)functions,stress distribution patterns were embedded into the prediction model,achieving high-precision optical power loss prediction.Finally,a Bayesian optimization(BO)algorithm with an adaptive sampling strategy was implemented for efficient parameter space exploration.Experimental results demonstrate that the proposedmethod effectively establishes explicit physical correlations between welding parameters and optical power loss.The optimized welding parameters reduced optical power loss by 34.1%,providing theoretical guidance and technical support for reliable CLD packaging.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
With growing concerns over environmental issues,ethylene manufacturing is shifting from a sole focus on economic benefits to an additional consideration of environmental impacts.The operation of the thermal cracking f...With growing concerns over environmental issues,ethylene manufacturing is shifting from a sole focus on economic benefits to an additional consideration of environmental impacts.The operation of the thermal cracking furnace in ethylene manufacturing determines not only the profitability of an ethylene plant but also the carbon emissions it releases.While multi-objective optimization of the thermal cracking furnace to balance profit with environmental impact is an effective solution to achieve green ethylene man-ufacturing,it carries a high computational demand due to the complex dynamic processes involved.In this work,artificial intelligence(AI)is applied to develop a novel hybrid model based on physically consistent machine learning(PCML).This hybrid model not only reduces the computational demand but also retains the interpretability and scalability of the model.With this hybrid model,the computational demand of the multi-objective dynamic optimization is reduced to 77 s.The optimization results show that dynamically adjusting the operating variables with coke formation can effectively improve profit and reduce CO_(2)emissions.In addition,the results from this study indicate that sacrificing 28.97%of the annual profit can significantly reduce the annual CO_(2)emissions by 42.89%.The key findings of this study highlight the great potential for green ethylene manufacturing based on AI through modeling and optimization approaches.This study will be important for industrial practitioners and policy-makers.展开更多
基金supported by the General Program of the National Natural Science Foundation of China(No.52274326)the China Baowu Low Carbon Metallurgy Innovation Foundation(No.BWLCF202109)the Seventh Batch of Ten Thousand Talents Plan of China(No.ZX20220553).
文摘Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.
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.
文摘The growing demand for carbon neutrality has heightened the focus on CO_(2)hydrogenation as a viable strategy for transforming carbon dioxide into valuable chemicals and fuels.Advanced machine learning(ML)approaches integrate materials science with artificial intelligence,enabling scientists to identify hidden patterns in datasets,make informed decisions,and reduce the need for labor-intensive,repetitive experimentation.This review provides a comprehensive overview of ML applications in the thermocatalytic hydrogenation of CO_(2).Following an introduction to ML tools and workflows,various ML algorithms employed in CO_(2)hydrogenation are systematically categorized and reviewed.Next,the application of ML in catalyst discovery is discussed,highlighting its role in identifying optimal compositions and structures.Then,ML-driven strategies for process optimization,particularly in enhancing CO_(2)conversion and product selectivity,are examined.Studies modeling descriptors,spanning catalyst properties and reaction conditions,to predict catalytic performance are analyzed.Consequently,ML-based mechanistic studies are reviewed to elucidate reaction pathways,identify key intermediates,and optimize catalyst performance.Finally,key challenges and future perspectives in leveraging ML for advancing CO_(2)hydrogenation research are presented.
文摘Objective:To investigate the application effects of intelligent guidance systems in optimizing health check-up process management.Methods:A total of 400 examinees who underwent physical examinations at the hospital’s Health Management Center from January to December 2024 were randomly divided into a control group(200 cases)and an observation group(200 cases).The control group used traditional manual guidance methods,while the observation group employed the intelligent guidance system.The study compared two groups in terms of completion time,waiting time for each procedure,check-up efficiency scores,examinee satisfaction,and report issuance time.Results:The overall examination time in the observation group(85.3±12.7 minutes)was significantly shorter than that in the control group(142.6±18.5 minutes)(P<0.01);average waiting time per procedure decreased by 62.4%;check-up efficiency scores(8.9±0.8 points)were significantly higher than those in the control group(5.2±1.1 points)(P<0.01);satisfaction reached 96.5%,significantly higher than the control group’s 78.0%(P<0.01);and report issuance time was advanced by 1.5 days.Conclusion:Intelligent guidance systems can significantly optimize check-up processes,improve work efficiency,and examinee satisfaction,demonstrating significant clinical application value.
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.
基金supported by National Natural Science Foundation of China(Grant Nos.5233500651975073)State Key Laboratory of Mechanical Transmission for Advanced Equipment(Grant No.SKLMT-MSKFKT-202104).
文摘Aluminum alloys manufactured using traditional processes are increasingly unable to meet the high flexibility and performance requirements of modern engineering.In this study,Al-Mg-Sc-Zr alloys were manufactured via laser powder bed fusion(LPBF)to obtain high-performance aluminum alloys.To this end,process parameter optimization and heat treatment were adopted.The optimal process parameters were determined by initially analyzing the relative density and defect distribution under varying energy densities.The sample obtained under the optimal process parameters exhibited a relative density of 99.84%.Subsequently,the corresponding phase compositions,microstructures,and mechanical performance of the as-fabricated specimens were determined using the optimal process parameters before and after heat treatment.The microstructures of the samples showed typical equiaxed columnar bimodal grain structures,with Al_(3)(Sc,Zr)precipitates detected.The samples exhibited no significant anisotropy before and after heat treatment,while the grain orientation differences were dominated by high-angle grain boundaries.The mechanical properties of all the samples were characterized using tensile and hardness tests.The yield strength,ultimate tensile strength,and elongation of the sample were 475.0 MPa,508.2 MPa,and 8.3%,respectively.Overall,samples with high density,low porosity,high strength,and high plasticity were obtained by process parameter optimization and appropriate heat treatment.
基金The National Natural Science Foundation of China(No.52305381)the Natural Science Foundation of Jiangsu Province(No.BK20210351)the Fundamental Research Funds for the Central Universities(No.30923011008).
文摘A reasonable process plan is an important basis for implementing wire arc additive and subtractive hybrid manufacturing(ASHM),and a new optimization method is proposed.Firstly,the target parts and machining tools are modeled by level set functions.Secondly,the mathematical model of the additive direction optimization problem is established,and an improved particle swarm optimization algorithm is designed to decide the best additive direction.Then,the two-step strategy is used to plan the hybrid manufacturing alternating sequence.The target parts are directly divided into various processing regions;each processing region is optimized based on manufacturability and manufacturing efficiency,and the optimal hybrid manufacturing alternating sequence is obtained by merging some processing regions.Finally,the method is used to outline the process plan of the designed example model and applied to the actual hybrid manufacturing process of the model.The manufacturing result shows that the method can meet the main considerations in hybrid manufacturing.In addition,the degree of automation of process planning is high,and the dependence on manual intervention is low.
基金supported by the National Natural Science Foundation of China(22408227,22238005)the Postdoctoral Research Foundation of China(GZC20231576).
文摘The optimization of reaction processes is crucial for the green, efficient, and sustainable development of the chemical industry. However, how to address the problems posed by multiple variables, nonlinearities, and uncertainties during optimization remains a formidable challenge. In this study, a strategy combining interpretable machine learning with metaheuristic optimization algorithms is employed to optimize the reaction process. First, experimental data from a biodiesel production process are collected to establish a database. These data are then used to construct a predictive model based on artificial neural network (ANN) models. Subsequently, interpretable machine learning techniques are applied for quantitative analysis and verification of the model. Finally, four metaheuristic optimization algorithms are coupled with the ANN model to achieve the desired optimization. The research results show that the methanol: palm fatty acid distillate (PFAD) molar ratio contributes the most to the reaction outcome, accounting for 41%. The ANN-simulated annealing (SA) hybrid method is more suitable for this optimization, and the optimal process parameters are a catalyst concentration of 3.00% (mass), a methanol: PFAD molar ratio of 8.67, and a reaction time of 30 min. This study provides deeper insights into reaction process optimization, which will facilitate future applications in various reaction optimization processes.
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 Central Guided Local Science and Technology Development Funds(ZY20230102)Guilin Scientific Research and Technology Development Programme Project(2023010301-1,20220104-4)+1 种基金Guangxi Science and Technology Programme Project(GK AB24010263)Guangxi Innovation Driving Development Special Funds Project(GK AA22096020).
文摘[Objectives] To optimize the crystallization process of ceftriaxone sodium using response surface methodology (RSM) for enhancing both the crystallization rate and the quality of the final product. [Methods] Four key factors, including crystallization temperature, stirring speed, solvent drop rate, and seed crystal content, were employed as independent variables, while the crystallization rate served as the response variable. The Box-Behnken response surface method was utilized for the optimization design. [Results] The optimal parameters for the crystallization process, determined through optimization, were as follows: a temperature of 10.6 ℃, a stirring rate of 150 rpm, a solvent drop rate of 1.50 mL/min, and a seed crystal content of 0.12 g. Validation tests conducted under these conditions yielded an average crystallization rate of 94.38% for the refined product. [Conclusions] The crystallization efficiency of ceftriaxone sodium is markedly enhanced, thereby offering substantial support for its industrial production and clinical application.
基金financial supports from the National Natural Science Foundation of China-Youth Project(51801076)the Provincial Colleges and Universities Natural Science Research Project of Jiangsu Province(18KJB430009)+1 种基金the Postdoctoral Research Support Project of Jiangsu Province(1601055C)the Senior Talents Research Startup of Jiangsu University(14JDG126)。
文摘To solve the problems of deformation,micro-cracks,and residual tensile stress in laser cladding coatings,the technique of laser cladding with Fe-based memory alloy can be considered.However,the process of in-situ synthesis of Fe-based memory alloy coatings is extremely complex.At present,there is no clear guidance scheme for its preparation process,which limits its promotion and application to some extent.Therefore,in this study,response surface methodology(RSM)was used to model the response surface between the target values and the cladding process parameters.The NSGA-2 algorithm was employed to optimize the process parameters.The results indicate that the composite optimization method consisting of RSM and the NSGA-2 algorithm can establish a more accurate model,with an error of less than 4.5%between the predicted and actual values.Based on this established model,the optimal scheme for process parameters corresponding to different target results can be rapidly obtained.The prepared coating exhibits a uniform structure,with no defects such as pores,cracks,and deformation.The surface roughness and microhardness of the coating are enhanced,the shaping quality of the coating is effectively improved,and the electrochemical corrosion performance of the coating in 3.5%NaCl solution is obviously better than that of the substrate,providing an important guide for engineering applications.
文摘This paper examines the challenges in the technical briefing process for construction projects,including a three-level system and issues related to formalization.An optimization approaches was introduced based on the PDCA cycle,alongside the application of BIM and AR technologies.The key preparatory measures were outlined in this study and the functions of the management system was mentioned.Through case comparisons,this paper demonstrated that these optimizations can significantly improve efficiency and quality,support the development of an evaluation system to verify results,and highlight the critical role of organizational support.
文摘In the project procurement management process of telecommunication enterprises,due to the complexity of technology,the professional procurement project manager is responsible for the whole process of professional procurement in a one-stop way.The integration of this management process superficially improves labor productivity,but in essence lacks effective checks and balances and supervision.In order to supervise the project procurement management process and ensure the legal compliance of procurement management,this paper studies the project procurement management process of telecommunication enterprises,proposes the optimization process of project procurement management in the segmentation of purchasing manager-business manager,and constructs a matrix project procurement management model,which will contribute to the overall improvement of the telecommunication enterprises’procurement performance.
基金supported by the National Key R&D Program of China(No.2022YFA1005204l)。
文摘Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors and performance defects,leading to a decline in product quality and affecting its service life.This study proposes a process parameter optimization method that considers the mechanical properties of printed specimens and production costs.To improve the quality of silicone printing samples and reduce production costs,three machine learning models,kernel extreme learning machine(KELM),support vector regression(SVR),and random forest(RF),were developed to predict these three factors.Training data were obtained through a complete factorial experiment.A new dataset is obtained using the Euclidean distance method,which assigns the elimination factor.It is trained with Bayesian optimization algorithms for parameter optimization,the new dataset is input into the improved double Gaussian extreme learning machine,and finally obtains the improved KELM model.The results showed improved prediction accuracy over SVR and RF.Furthermore,a multi-objective optimization framework was proposed by combining genetic algorithm technology with the improved KELM model.The effectiveness and reasonableness of the model algorithm were verified by comparing the optimized results with the experimental results.
文摘In response to the challenges associated with the traditional synthesis process of hymenidin,such as complex reaction steps,low yields,high costs,and environmental concerns,the synthesis process has been significantly enhanced by optimizing reaction conditions,screening for efficient catalysts,and incorporating the concepts of green chemistry.The optimized process has significantly improved the synthesis efficiency and product quality of hymenidin,reduced production costs,and minimized environmental pollution,thereby providing robust support for its industrial production and broad application.
基金funded by the National Key R&D Program of China,Grant No.2024YFF0504904.
文摘The packaging quality of coaxial laser diodes(CLDs)plays a pivotal role in determining their optical performance and long-term reliability.As the core packaging process,high-precision laser welding requires precise control of process parameters to suppress optical power loss.However,the complex nonlinear relationship between welding parameters and optical power loss renders traditional trial-and-error methods inefficient and imprecise.To address this challenge,a physics-informed(PI)and data-driven collaboration approach for welding parameter optimization is proposed.First,thermal-fluid-solid coupling finite element method(FEM)was employed to quantify the sensitivity of welding parameters to physical characteristics,including residual stress.This analysis facilitated the identification of critical factors contributing to optical power loss.Subsequently,a Gaussian process regression(GPR)model incorporating finite element simulation prior knowledge was constructed based on the selected features.By introducing physics-informed kernel(PIK)functions,stress distribution patterns were embedded into the prediction model,achieving high-precision optical power loss prediction.Finally,a Bayesian optimization(BO)algorithm with an adaptive sampling strategy was implemented for efficient parameter space exploration.Experimental results demonstrate that the proposedmethod effectively establishes explicit physical correlations between welding parameters and optical power loss.The optimized welding parameters reduced optical power loss by 34.1%,providing theoretical guidance and technical support for reliable CLD packaging.
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the financial support of the National Key Research and Development Program of China(2021YFE0112800)EU RISE project OPTIMAL(101007963).
文摘With growing concerns over environmental issues,ethylene manufacturing is shifting from a sole focus on economic benefits to an additional consideration of environmental impacts.The operation of the thermal cracking furnace in ethylene manufacturing determines not only the profitability of an ethylene plant but also the carbon emissions it releases.While multi-objective optimization of the thermal cracking furnace to balance profit with environmental impact is an effective solution to achieve green ethylene man-ufacturing,it carries a high computational demand due to the complex dynamic processes involved.In this work,artificial intelligence(AI)is applied to develop a novel hybrid model based on physically consistent machine learning(PCML).This hybrid model not only reduces the computational demand but also retains the interpretability and scalability of the model.With this hybrid model,the computational demand of the multi-objective dynamic optimization is reduced to 77 s.The optimization results show that dynamically adjusting the operating variables with coke formation can effectively improve profit and reduce CO_(2)emissions.In addition,the results from this study indicate that sacrificing 28.97%of the annual profit can significantly reduce the annual CO_(2)emissions by 42.89%.The key findings of this study highlight the great potential for green ethylene manufacturing based on AI through modeling and optimization approaches.This study will be important for industrial practitioners and policy-makers.
