The key challenge in the preparation of perovskite solar cells is to enhance the reproducibility of PSC manufacturing,particularly by better controlling multiple high-dimensional process parameters.This study proposes...The key challenge in the preparation of perovskite solar cells is to enhance the reproducibility of PSC manufacturing,particularly by better controlling multiple high-dimensional process parameters.This study proposes a machine learning(ML)approach to efficiently predict and analyze perovskite film fabrication processes.By evaluating five classic ML algorithms on 130 experimental data sets from blade-coating parameters,the Random Forest(RF)model was identified as the most effective,enabling rapid prediction of over 100,000 parameter sets in just 10 min-equivalent to 3 years of manual experimentation.The RF model demonstrated strong predictive accuracy,with an R^(2) close to 0.8.This approach led to the identification of optimal process parameter combinations,significantly improving the reproducibility of PSCs and reducing performance variance by approximately threefold,thereby advancing the development of scalable manufacturing processes.展开更多
The aging process is an inexorable fact throughout our lives and is considered a major factor in develo ping neurological dysfunctions associated with cognitive,emotional,and motor impairments.Aging-associated neurode...The aging process is an inexorable fact throughout our lives and is considered a major factor in develo ping neurological dysfunctions associated with cognitive,emotional,and motor impairments.Aging-associated neurodegenerative diseases are characterized by the progressive loss of neuronal structure and function.展开更多
Ironmaking process(IP)is indispensable to modern iron and steel industry,where real-time monitoring is crucial for achieving high molten iron quality(MIQ)with low energy consumption.While neural network-based models s...Ironmaking process(IP)is indispensable to modern iron and steel industry,where real-time monitoring is crucial for achieving high molten iron quality(MIQ)with low energy consumption.While neural network-based models show some promising results,they are generally limited by non-negligible drawbacks such as interpretability issues of feature learning.To address these issues,we propose a novel concept based on the shallow-to-deep correlation network representation regression(Sh-to-De CNRR).Our approach,shallow correlation network representation regression(ShCNRR),combines neural network and canonical correlation analysis thoughts to generate explainable features via shallow correlation network representation(CNR).A twin inverse network is then derived to obtain the explicit model output,leveraging the shallow CNR.To capture deeper nonlinear information,we extend ShCNRR into a hierarchical deep correlation network representation regression(DeCNRR)model that features stacked neural networks,enabling us to learn deeper CNR from process data.The feasibility and advantages of our proposals are validated by theoretical derivations and practical IP cases,which contain one MIQ regression and three MIQ-related fault detection tasks.The results reveal that highly fused statistical and neural network models yield superior monitoring performance compared to current state-of-the-art models,while statistical tests verify the convincing feature mining.展开更多
The chemical industry plays a critical role in supporting global economic development,yet its traditional production paradigms are associated with high resource consumption,energy demand,and environmental impact.To de...The chemical industry plays a critical role in supporting global economic development,yet its traditional production paradigms are associated with high resource consumption,energy demand,and environmental impact.To deal with the growing regulatory burden,societal demands,and environmental targets,eco-friendly processes in chemicals have become one of the major approaches to ensuring industrialization with environmental safety.This review includes an overall summary of the recent developments of green chemical processes with the focus on the basic principles,facilitating structures,and technologies that form the basis of sustainable chemical production.The most important advances in sustainable feedstocks,green catalysis,environmentally benign solvents,energy-efficient and intensified process technologies are also essential introductions,and the importance of digitalization,artificial intelligence,and life cycle-informed assessment tools in environmental performance optimization is also increasing.The review also discusses some of the barriers related to industry implementation,such as scalability,economic viability,and the necessity of having strong sustainability verification.Using chosen industrial case studies of China,South Korea,and Vietnam,various ways of integrating eco-friendly processes are demonstrated,including the adoption of renewable energy and low-carbon hydrogen,the adoption of circular plastics,and refinery energy optimization.Such examples demonstrate the significance of regional settings,system-level integration,and open environmental assessment in achieving significant sustainability results.The presented insights should guide the way future research proceeds and facilitate the shift toward the low-impact chemical manufacturing systems that are resilient.展开更多
In rock engineering,natural cracks in rock masses subjected to external loads tend to initiate and propagate,leading to potential safety hazards.To investigate the effect of cracking behavior on the mechanical propert...In rock engineering,natural cracks in rock masses subjected to external loads tend to initiate and propagate,leading to potential safety hazards.To investigate the effect of cracking behavior on the mechanical properties of rocks,the cracking processes of pre-cracked rocks have been extensively studied using numerical modeling methods.The peridynamics(PD)exhibits advantages over other numerical methods due to the absence of the requirements for remeshing and external crack growth criterion.However,for modeling pre-cracked rock cracking processes under impact,current PD implementations lack generally applicable rock constitutive models and impact contact models,which leads to difficulties in determining rock material parameters and efficiently calculating impact loads.This paper proposes a non-ordinary state-based peridynamics(NOSBPD)modeling method integrating the Drucker-Prager(DP)plasticity model and an efficient contact model to address the above problems.In the proposed method,the Drucker-Prager plasticity model is integrated into the NOSBPD,thereby equipping NOSBPD with the capability to accurately characterize the nonlinear stress-strain relationship inherent in rocks.An efficient contact model between particles and meshes is designed to calculate the impact loads,which is essentially a coupling method of PD with the finite element method(FEM).The effectiveness of the proposed NOSBPD modeling method is verified by comparison with other numerical methods and experiments.Experimental results indicate that the proposed method can effectively and accurately predict the 3D cracking processes of pre-cracked cracks under impact loading,and the maximum principal stress is the key driver behind wing crack formation in pre-cracked rocks.展开更多
To address the persistent challenge of dynamic mismatch between wellbore lifting capacity and reservoir fluid supply,and to establish a robust optimization framework for drainage operations in high-water-cut tight san...To address the persistent challenge of dynamic mismatch between wellbore lifting capacity and reservoir fluid supply,and to establish a robust optimization framework for drainage operations in high-water-cut tight sandstone gas reservoirs,this study systematically investigates the graded optimization and dynamic adaptation of drainage gas recovery technologies.Production data from a representative tight gas field were first employed to forecast reservoir performance.The predictive reliability was rigorously validated through high-precision history matching,thereby providing a quantitatively consistent foundation for subsequent wellbore optimization.Building on this characterization,a coupled simulation framework was developed that integrates wellbore multiphase flow modeling with nodal analysis based on the Inflow Performance Relationship,IPR,and the Vertical Lift Performance,VLP.This coordinated approach enables comprehensive evaluation of process adaptability and dynamic optimization of foam-assisted drainage,mechanical pumping,and jet pumping systems under evolving water-gas ratio,WGR conditions.The results reveal that a progressively increasing water-gas ratio is the dominant factor driving the transition from chemically assisted drainage methods to mechanically enhanced lifting technologies.A distinct quantitative threshold is identified at WGR≈0.002,beyond which mechanical intervention becomes more effective and economically justified.For mechanical pumping and jet pumping systems,a parameter inversion optimization strategy constrained by the target bottomhole flowing pressure,Pwf,is proposed to ensure stable production while maintaining reservoir drawdown control.In particular,the nozzle-to-throat area ratio of the jet pump is identified as the key governing parameter influencing entrainment capacity and lifting efficiency.Moreover,a configuration characterized by small pump diameter,long stroke length,and low operating speed is demonstrated to satisfy drainage requirements while mitigating torque fluctuations,enhancing volumetric efficiency,and improving pump fillage stability.展开更多
Electrocatalytic oxidation is a promising technology for wastewater treatment,but poor mass transfer and low current efficiency impaded its engineering applications.To address these issues,researchers have developed f...Electrocatalytic oxidation is a promising technology for wastewater treatment,but poor mass transfer and low current efficiency impaded its engineering applications.To address these issues,researchers have developed flow-through electrochemical reactors(FERs)primarily based on porous electrodes,where the pore structure significantly impacts the electrochemical reaction.Therefore,this study systematically investigated the impact of different pore sizes on the fluid dynamics,current potential distribution,mass transfer processes,and degradation performance of FERs.Computational Fluid Dynamics(CFD)results indicated that smaller pore sizes(10μm,30μm,and 60μm)significantly enhanced convective effects within the fluid,reduced short fluid paths and dead volume regions within the microchannels,and facilitated mass transfer processes.Additionally,smaller pore sizes were conducive to a uniform distribution of current density.Furthermore,Fe(CN)_(6)^(4−)oxidation experiments revealed that the current density at a pore size of 160μm was notably lower than that at 10μm,indicating slower mass transfer of Fe(CN)_(6)^(4−)within larger channels.Calculations based on experimental results demonstrated that the mass transfer rate at a pore size of 10μm was six times than that at 160μm,further confirming the enhancing effect of smaller pore sizes on the mass transfer process.Lastly,experiments on tetracycline degradation showed that at a residence time of 90 s,the removal efficiencies of tetracycline were 80%and 39.1%for porous electrodes with pore sizes of 10μm and 160μm,respectively,demonstrating the superior removal efficiency of smaller pore sizes for tetracycline degradation.展开更多
The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficie...The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.展开更多
一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家...一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家赫拉克利特所说的“万物流转”,又说“你不能两次踏进同一条河流,因为新的水不断地流过你的身旁”,他所表达的意思是“世界上唯一不变的就是变化”。展开更多
To achieve zero-defect production during computer numerical control(CNC)machining processes,it is imperative to develop effective diagnosis systems to detect anomalies efficiently.However,due to the dynamic conditions...To achieve zero-defect production during computer numerical control(CNC)machining processes,it is imperative to develop effective diagnosis systems to detect anomalies efficiently.However,due to the dynamic conditions of the machine and tooling during machining processes,the relevant diagnosis systems currently adopted in industries are incompetent.To address this issue,this paper presents a novel data-driven diagnosis system for anomalies.In this system,power data for condition monitoring are continuously collected during dynamic machining processes to support online diagnosis analysis.To facilitate the analysis,preprocessing mechanisms have been designed to de-noise,normalize,and align the monitored data.Important features are extracted from the monitored data and thresholds are defined to identify anomalies.Considering the dynamic conditions of the machine and tooling during machining processes,the thresholds used to identify anomalies can vary.Based on historical data,the values of thresholds are optimized using a fruit fly optimization(FFO)algorithm to achieve more accurate detection.Practical case studies were used to validate the system,thereby demonstrating the potential and effectiveness of the system for industrial applications.展开更多
The effect of hot band annealing processes—batch annealing and continuous annealing—on the texture evolution and ridging performance of ferritic stainless steel was investigated.The surface and central layers of the...The effect of hot band annealing processes—batch annealing and continuous annealing—on the texture evolution and ridging performance of ferritic stainless steel was investigated.The surface and central layers of the hot band exhibited strong shear and plane deformation textures,respectively.After batch annealing,the texture intensity of the hot-rolled sheet texture significantly decreased,and a weak recrystallization texture appeared,while fully recrystallized grains occurred after continuous annealing.A complete recrystallized{111}texture was obtained after recrystallization annealing.The sheet subjected to continuous annealing exhibited the highest intensity of{111}texture,which was accompanied by a dispersed grain orientation distribution,resulting in the lowest ridging height.展开更多
Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and oper...Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.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
Current research on heterogeneous advanced oxidation processes(HAOPs)predominantly emphasizes catalyst iteration and innovation.Significant efforts have been made to regulate the electron structure and optimize the el...Current research on heterogeneous advanced oxidation processes(HAOPs)predominantly emphasizes catalyst iteration and innovation.Significant efforts have been made to regulate the electron structure and optimize the electron distribution,thereby increasing the catalytic activity.However,this focus often overshadows an equally essential aspect of HAOPs:the adsorption effect.Adsorption is a critical initiator for triggering the interaction of oxidants and contaminants with heterogeneous catalysts.The efficacy of these interactions is influenced by a variety of physicochemical properties,including surface chemistry and pore sizes,which determine the affinities between contaminants and material surfaces.This dispar ity in affinity is pivotal because it underpins the selective removal of contaminants,especially in complex waste streams containing diverse contaminants and competing matrices.Consequently,understanding and mastering these interfacial interactions is fundamentally indispensable not only for improving pro cess efficiency but also for enhancing the selectivity of contaminant removal.Herein,we highlight the importance of adsorption-driven interfacial interactions for fundamentally elucidating the catalytic mechanisms of HAOPs.Such interactions dictate the overall performance of the treatment processes by balancing the adsorption,reaction,and desorption rates on the catalyst surfaces.Elucidating the adsorption effect not only shifts the paradigm in understanding HAOPs but also improves their practical ity in water treatment and wastewater decontamination.Overall,we propose that revisiting adsorption driven interfacial interactions holds great promise for optimizing catalytic processes to develop effective HAOP strategies.展开更多
In oil and gas well cementing processes,accurately predicting the bottom hole circulating temperature(BHCT)is critical to ensuring effective zonal isolation.Overestimating the temperature can lead to excessive retarda...In oil and gas well cementing processes,accurately predicting the bottom hole circulating temperature(BHCT)is critical to ensuring effective zonal isolation.Overestimating the temperature can lead to excessive retardation issues,while underestimation can cause cementing accidents.Current methods for calculating the BHCT of cement slurry typically simplify the cementing processes to a single-fluid circulation and ignore the impact of pre-cementing processes on temperature,leading to significant discrepancies between calculated and actual results.In this study,the wellbore and formation are simplified into a two-dimensional axisymmetric structure,and a mathematical model of the temperature field under multi-fluid and multi-step conditions is established based on the law of energy conservation.The finite volume method was used to discretize the model,and a transient temperature field solver for the entire cementing process was developed,which can numerically calculate the temperature of any fluid at any time,any location.For an actual well example,the temperature distribution of the wellbore and formation after casing running is taken as the initial condition.Numerical calculations were performed sequentially to calculate the temperature fields of circulation flushing,wellbore preparation,and cementing,as well as the BHCT of the cement slurry.The study reveals that during the circulation flushing stage,the maximum temperature point in the wellbore is located at a distance of about 366 m above the bottom of the well.In the wellbore preparation stage,due to static heat exchange,the maximum temperature point gradually shifts to the bottom of the well.The BHCT of cement slurry changes continuously under cementing processes with multi-fluid and multi-step,making it a transient value.The BHCT of the lead slurry and tail slurry are not equal,with the maximum BHCT of the tail slurry being 2.46°C higher than that of the lead slurry.If circulation flushing and wellbore preparation are not considered,the calculated BHCT of the cement slurry will have errors of+6.8%and-1.9%.The study highlighted that considering thermal effects of all cementing stages,such as circulation flushing and wellbore preparation,in BHCT calculations can help improve prediction accuracy.展开更多
This paper focuses on the preparation of rare earth oxide products from rare earth chloride solutions during the rare earth extraction and separation processes,as well as the recycling of magnesium chloride solutions....This paper focuses on the preparation of rare earth oxide products from rare earth chloride solutions during the rare earth extraction and separation processes,as well as the recycling of magnesium chloride solutions.It proposes the idea of introducing spray pyrolysis technology into the rare earth extraction and separation processes.This paper briefly describes the development history of chloride spray pyrolysis technology,focusing on the research status and application progress of rare earth chloride solution and magnesium chloride solution spray pyrolysis technology,as well as spray pyrolysis equipment.The paper also analyzes the challenges and technical intricacies associated with applying spray pyrolysis technology to chloride solutions in the rare earth extraction and separation processes.Additionally,it explores future trends and proposes strategies to facilitate the full recycling of acids and bases,streamline the process flow,and enhance the prospects for green and low-carbon rare earth metallurgy.展开更多
This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mi...This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.展开更多
To investigate groundwater flow and solute transport characteristics of the karst trough zone in China,tracer experiments were conducted at two adjacent typical karst groundwater flow systems(Yuquandong(YQD)and Migong...To investigate groundwater flow and solute transport characteristics of the karst trough zone in China,tracer experiments were conducted at two adjacent typical karst groundwater flow systems(Yuquandong(YQD)and Migongquan(MGQ))in Sixi valley,western Hubei,China.Highresolution continuous monitoring was utilized to obtain breakthrough curves(BTCs),which were then analyzed using the multi-dispersion model(MDM)and the two-region nonequilibrium model(2RNE)with basic parameters calculated by CXTFIT and QTRACER2.Results showed that:(1)YQD flow system had a complex infiltration matrix with overland flow,conduit flow and fracture flow,while the MGQ flow system was dominated by conduit flow with fast flow transport velocity,but also small amount of fracture flow there;(2)They were well fitted based on the MDM(R^2=0.928)and 2RNE(R^2=0.947)models,indicating that they had strong adaptability in the karst trough zone;(3)conceptual models for YQD and MGQ groundwater systems were generalized.In YQD system,the solute was transported via overland flow during intense rainfall,while some infiltrated down into fissures and conduits.In MGQ system,most were directly transported to spring outlet in the fissureconduit network.展开更多
Effective vegetation reconstruction plays a vital role in the restoration of desert ecosystems.However,in reconstruction of different vegetation types,the community characteristics,assembly processes,and functions of ...Effective vegetation reconstruction plays a vital role in the restoration of desert ecosystems.However,in reconstruction of different vegetation types,the community characteristics,assembly processes,and functions of different soil microbial taxa under environmental changes are still disputed,which limits the understanding of the sustainability of desert restoration.Hence,we investigated the soil microbial community characteristics and functional attributes of grassland desert(GD),desert steppe(DS),typical steppe(TS),and artificial forest(AF)in the Mu Us Desert,China.Our findings confirmed the geographical conservation of soil microbial composition but highlighted decreased microbial diversity in TS.Meanwhile,the abundance of rare taxa and microbial community stability in TS improved.Heterogeneous and homogeneous selection determined the assembly of rare and abundant bacterial taxa,respectively,with both being significantly influenced by soil moisture.In contrast,fungal communities displayed stochastic processes and exhibited sensitivity to soil nutrient conditions.Furthermore,our investigation revealed a noteworthy augmentation in bacterial metabolic functionality in TS,aligning with improved vegetation restoration and the assemblage of abundant bacterial taxa.However,within nutrient-limited soils(GD,DS,and AF),the assembly dynamics of rare fungal taxa assumed a prominent role in augmenting their metabolic capacity and adaptability to desert ecosystems.These results highlighted the variations in the assembly processes and metabolic functions of soil microorganisms during vegetation reestablishment and provided corresponding theoretical support for anthropogenic revegetation of desert ecosystems.展开更多
In November 1984,China launched its first expedition to the Southern Ocean and the Antarctic continent,culminating in the establishment of its first year-round research station—Great Wall Station—on the Antarctic Pe...In November 1984,China launched its first expedition to the Southern Ocean and the Antarctic continent,culminating in the establishment of its first year-round research station—Great Wall Station—on the Antarctic Peninsula in February 1985.Forty years later,in February 2024,China’s fifth research station,Qinling Station,commenced operations on Inexpress-ible Island near Terra Nova Bay.展开更多
基金Key Research and Development Program of Hubei Province,China(Grant No.2022BAA096)Zhejiang Provincial Natural Science Foundation of China(This material is based upon work funded by Zhejiang Provincial Natural Science Foundation of China under Grant No.LR25A020002)support of the Center for Materials Analysis and Characterization,Material Characterization Lab,and Nanofabrication Lab at Hubei University。
文摘The key challenge in the preparation of perovskite solar cells is to enhance the reproducibility of PSC manufacturing,particularly by better controlling multiple high-dimensional process parameters.This study proposes a machine learning(ML)approach to efficiently predict and analyze perovskite film fabrication processes.By evaluating five classic ML algorithms on 130 experimental data sets from blade-coating parameters,the Random Forest(RF)model was identified as the most effective,enabling rapid prediction of over 100,000 parameter sets in just 10 min-equivalent to 3 years of manual experimentation.The RF model demonstrated strong predictive accuracy,with an R^(2) close to 0.8.This approach led to the identification of optimal process parameter combinations,significantly improving the reproducibility of PSCs and reducing performance variance by approximately threefold,thereby advancing the development of scalable manufacturing processes.
文摘The aging process is an inexorable fact throughout our lives and is considered a major factor in develo ping neurological dysfunctions associated with cognitive,emotional,and motor impairments.Aging-associated neurodegenerative diseases are characterized by the progressive loss of neuronal structure and function.
基金supported in part by the Pioneer Research and Development Program of Zhejiang(2025C01021)Zhejiang Province Postdoctoral Research Project Selection Fund(ZJ2025061)+3 种基金the National Science and Technology Major Project-Intelligent Manufacturing Systems and Robotics of China(2025ZD1602000,2025ZD1601800)the National Natural Science Foundation of China(61933015,62273030,62573387)the Natural Science Foundation of Zhejiang province,China(LY24F030004)the Fundamental Research Funds of Zhejiang Sci-Tech University(25222139-Y)。
文摘Ironmaking process(IP)is indispensable to modern iron and steel industry,where real-time monitoring is crucial for achieving high molten iron quality(MIQ)with low energy consumption.While neural network-based models show some promising results,they are generally limited by non-negligible drawbacks such as interpretability issues of feature learning.To address these issues,we propose a novel concept based on the shallow-to-deep correlation network representation regression(Sh-to-De CNRR).Our approach,shallow correlation network representation regression(ShCNRR),combines neural network and canonical correlation analysis thoughts to generate explainable features via shallow correlation network representation(CNR).A twin inverse network is then derived to obtain the explicit model output,leveraging the shallow CNR.To capture deeper nonlinear information,we extend ShCNRR into a hierarchical deep correlation network representation regression(DeCNRR)model that features stacked neural networks,enabling us to learn deeper CNR from process data.The feasibility and advantages of our proposals are validated by theoretical derivations and practical IP cases,which contain one MIQ regression and three MIQ-related fault detection tasks.The results reveal that highly fused statistical and neural network models yield superior monitoring performance compared to current state-of-the-art models,while statistical tests verify the convincing feature mining.
文摘The chemical industry plays a critical role in supporting global economic development,yet its traditional production paradigms are associated with high resource consumption,energy demand,and environmental impact.To deal with the growing regulatory burden,societal demands,and environmental targets,eco-friendly processes in chemicals have become one of the major approaches to ensuring industrialization with environmental safety.This review includes an overall summary of the recent developments of green chemical processes with the focus on the basic principles,facilitating structures,and technologies that form the basis of sustainable chemical production.The most important advances in sustainable feedstocks,green catalysis,environmentally benign solvents,energy-efficient and intensified process technologies are also essential introductions,and the importance of digitalization,artificial intelligence,and life cycle-informed assessment tools in environmental performance optimization is also increasing.The review also discusses some of the barriers related to industry implementation,such as scalability,economic viability,and the necessity of having strong sustainability verification.Using chosen industrial case studies of China,South Korea,and Vietnam,various ways of integrating eco-friendly processes are demonstrated,including the adoption of renewable energy and low-carbon hydrogen,the adoption of circular plastics,and refinery energy optimization.Such examples demonstrate the significance of regional settings,system-level integration,and open environmental assessment in achieving significant sustainability results.The presented insights should guide the way future research proceeds and facilitate the shift toward the low-impact chemical manufacturing systems that are resilient.
基金support from the National Natural Science Foundation of China(Grant Nos.42277161 and 42230709).
文摘In rock engineering,natural cracks in rock masses subjected to external loads tend to initiate and propagate,leading to potential safety hazards.To investigate the effect of cracking behavior on the mechanical properties of rocks,the cracking processes of pre-cracked rocks have been extensively studied using numerical modeling methods.The peridynamics(PD)exhibits advantages over other numerical methods due to the absence of the requirements for remeshing and external crack growth criterion.However,for modeling pre-cracked rock cracking processes under impact,current PD implementations lack generally applicable rock constitutive models and impact contact models,which leads to difficulties in determining rock material parameters and efficiently calculating impact loads.This paper proposes a non-ordinary state-based peridynamics(NOSBPD)modeling method integrating the Drucker-Prager(DP)plasticity model and an efficient contact model to address the above problems.In the proposed method,the Drucker-Prager plasticity model is integrated into the NOSBPD,thereby equipping NOSBPD with the capability to accurately characterize the nonlinear stress-strain relationship inherent in rocks.An efficient contact model between particles and meshes is designed to calculate the impact loads,which is essentially a coupling method of PD with the finite element method(FEM).The effectiveness of the proposed NOSBPD modeling method is verified by comparison with other numerical methods and experiments.Experimental results indicate that the proposed method can effectively and accurately predict the 3D cracking processes of pre-cracked cracks under impact loading,and the maximum principal stress is the key driver behind wing crack formation in pre-cracked rocks.
基金supported by the Major Science and Technology Project of PetroChina Company Limited“Research on Key Technologies for Enhancing Recovery in Tight Sandstone Gas Reservoirs”,specifically under its third sub-project:“Research on Integrated Fracturing,Drainage,and Production Technology to Enhance Single-Well Production in Water-Bearing Gas Reservoirs”(Grant number:2023ZZ25YJ03).
文摘To address the persistent challenge of dynamic mismatch between wellbore lifting capacity and reservoir fluid supply,and to establish a robust optimization framework for drainage operations in high-water-cut tight sandstone gas reservoirs,this study systematically investigates the graded optimization and dynamic adaptation of drainage gas recovery technologies.Production data from a representative tight gas field were first employed to forecast reservoir performance.The predictive reliability was rigorously validated through high-precision history matching,thereby providing a quantitatively consistent foundation for subsequent wellbore optimization.Building on this characterization,a coupled simulation framework was developed that integrates wellbore multiphase flow modeling with nodal analysis based on the Inflow Performance Relationship,IPR,and the Vertical Lift Performance,VLP.This coordinated approach enables comprehensive evaluation of process adaptability and dynamic optimization of foam-assisted drainage,mechanical pumping,and jet pumping systems under evolving water-gas ratio,WGR conditions.The results reveal that a progressively increasing water-gas ratio is the dominant factor driving the transition from chemically assisted drainage methods to mechanically enhanced lifting technologies.A distinct quantitative threshold is identified at WGR≈0.002,beyond which mechanical intervention becomes more effective and economically justified.For mechanical pumping and jet pumping systems,a parameter inversion optimization strategy constrained by the target bottomhole flowing pressure,Pwf,is proposed to ensure stable production while maintaining reservoir drawdown control.In particular,the nozzle-to-throat area ratio of the jet pump is identified as the key governing parameter influencing entrainment capacity and lifting efficiency.Moreover,a configuration characterized by small pump diameter,long stroke length,and low operating speed is demonstrated to satisfy drainage requirements while mitigating torque fluctuations,enhancing volumetric efficiency,and improving pump fillage stability.
基金supported by the National Natural Science Foundation of China(Nos.U22A20241 and 21876105)Shaanxi“Scientist&Engineer”Team(No.2023KXJ-131)Xianyang Key S&T Special Projects(No.L2023-ZDKJ-QCY-SXGG-GY-007).
文摘Electrocatalytic oxidation is a promising technology for wastewater treatment,but poor mass transfer and low current efficiency impaded its engineering applications.To address these issues,researchers have developed flow-through electrochemical reactors(FERs)primarily based on porous electrodes,where the pore structure significantly impacts the electrochemical reaction.Therefore,this study systematically investigated the impact of different pore sizes on the fluid dynamics,current potential distribution,mass transfer processes,and degradation performance of FERs.Computational Fluid Dynamics(CFD)results indicated that smaller pore sizes(10μm,30μm,and 60μm)significantly enhanced convective effects within the fluid,reduced short fluid paths and dead volume regions within the microchannels,and facilitated mass transfer processes.Additionally,smaller pore sizes were conducive to a uniform distribution of current density.Furthermore,Fe(CN)_(6)^(4−)oxidation experiments revealed that the current density at a pore size of 160μm was notably lower than that at 10μm,indicating slower mass transfer of Fe(CN)_(6)^(4−)within larger channels.Calculations based on experimental results demonstrated that the mass transfer rate at a pore size of 10μm was six times than that at 160μm,further confirming the enhancing effect of smaller pore sizes on the mass transfer process.Lastly,experiments on tetracycline degradation showed that at a residence time of 90 s,the removal efficiencies of tetracycline were 80%and 39.1%for porous electrodes with pore sizes of 10μm and 160μm,respectively,demonstrating the superior removal efficiency of smaller pore sizes for tetracycline degradation.
基金supported by the National Key Research and Development Program of China(2023YFB3307801)the National Natural Science Foundation of China(62394343,62373155,62073142)+3 种基金Major Science and Technology Project of Xinjiang(No.2022A01006-4)the Programme of Introducing Talents of Discipline to Universities(the 111 Project)under Grant B17017the Fundamental Research Funds for the Central Universities,Science Foundation of China University of Petroleum,Beijing(No.2462024YJRC011)the Open Research Project of the State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024B70).
文摘The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.
文摘一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家赫拉克利特所说的“万物流转”,又说“你不能两次踏进同一条河流,因为新的水不断地流过你的身旁”,他所表达的意思是“世界上唯一不变的就是变化”。
基金funding from the EU Smarter project(PEOPLE-2013-IAPP-610675)
文摘To achieve zero-defect production during computer numerical control(CNC)machining processes,it is imperative to develop effective diagnosis systems to detect anomalies efficiently.However,due to the dynamic conditions of the machine and tooling during machining processes,the relevant diagnosis systems currently adopted in industries are incompetent.To address this issue,this paper presents a novel data-driven diagnosis system for anomalies.In this system,power data for condition monitoring are continuously collected during dynamic machining processes to support online diagnosis analysis.To facilitate the analysis,preprocessing mechanisms have been designed to de-noise,normalize,and align the monitored data.Important features are extracted from the monitored data and thresholds are defined to identify anomalies.Considering the dynamic conditions of the machine and tooling during machining processes,the thresholds used to identify anomalies can vary.Based on historical data,the values of thresholds are optimized using a fruit fly optimization(FFO)algorithm to achieve more accurate detection.Practical case studies were used to validate the system,thereby demonstrating the potential and effectiveness of the system for industrial applications.
文摘The effect of hot band annealing processes—batch annealing and continuous annealing—on the texture evolution and ridging performance of ferritic stainless steel was investigated.The surface and central layers of the hot band exhibited strong shear and plane deformation textures,respectively.After batch annealing,the texture intensity of the hot-rolled sheet texture significantly decreased,and a weak recrystallization texture appeared,while fully recrystallized grains occurred after continuous annealing.A complete recrystallized{111}texture was obtained after recrystallization annealing.The sheet subjected to continuous annealing exhibited the highest intensity of{111}texture,which was accompanied by a dispersed grain orientation distribution,resulting in the lowest ridging height.
基金supported by the National Key Research and Development Program of China (2020YFB1713800)the National Natural Science Foundation of China (92267205)+1 种基金the Hunan Provincial Innovation Foundation for Postgraduate (CX2022 0267)the Fundamental Research Funds for the Central Universities of Central South University (2022ZZTS0181)。
文摘Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.
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supported by the National Key Research and Development Program of China(2022YFC3205300)the National Natural Science Foundation of China(22176124).
文摘Current research on heterogeneous advanced oxidation processes(HAOPs)predominantly emphasizes catalyst iteration and innovation.Significant efforts have been made to regulate the electron structure and optimize the electron distribution,thereby increasing the catalytic activity.However,this focus often overshadows an equally essential aspect of HAOPs:the adsorption effect.Adsorption is a critical initiator for triggering the interaction of oxidants and contaminants with heterogeneous catalysts.The efficacy of these interactions is influenced by a variety of physicochemical properties,including surface chemistry and pore sizes,which determine the affinities between contaminants and material surfaces.This dispar ity in affinity is pivotal because it underpins the selective removal of contaminants,especially in complex waste streams containing diverse contaminants and competing matrices.Consequently,understanding and mastering these interfacial interactions is fundamentally indispensable not only for improving pro cess efficiency but also for enhancing the selectivity of contaminant removal.Herein,we highlight the importance of adsorption-driven interfacial interactions for fundamentally elucidating the catalytic mechanisms of HAOPs.Such interactions dictate the overall performance of the treatment processes by balancing the adsorption,reaction,and desorption rates on the catalyst surfaces.Elucidating the adsorption effect not only shifts the paradigm in understanding HAOPs but also improves their practical ity in water treatment and wastewater decontamination.Overall,we propose that revisiting adsorption driven interfacial interactions holds great promise for optimizing catalytic processes to develop effective HAOP strategies.
基金supported by the National Natural Science Foundation of China(No.U22B6003 and No.52274010)the China Scholarship Council(No.202008080235)。
文摘In oil and gas well cementing processes,accurately predicting the bottom hole circulating temperature(BHCT)is critical to ensuring effective zonal isolation.Overestimating the temperature can lead to excessive retardation issues,while underestimation can cause cementing accidents.Current methods for calculating the BHCT of cement slurry typically simplify the cementing processes to a single-fluid circulation and ignore the impact of pre-cementing processes on temperature,leading to significant discrepancies between calculated and actual results.In this study,the wellbore and formation are simplified into a two-dimensional axisymmetric structure,and a mathematical model of the temperature field under multi-fluid and multi-step conditions is established based on the law of energy conservation.The finite volume method was used to discretize the model,and a transient temperature field solver for the entire cementing process was developed,which can numerically calculate the temperature of any fluid at any time,any location.For an actual well example,the temperature distribution of the wellbore and formation after casing running is taken as the initial condition.Numerical calculations were performed sequentially to calculate the temperature fields of circulation flushing,wellbore preparation,and cementing,as well as the BHCT of the cement slurry.The study reveals that during the circulation flushing stage,the maximum temperature point in the wellbore is located at a distance of about 366 m above the bottom of the well.In the wellbore preparation stage,due to static heat exchange,the maximum temperature point gradually shifts to the bottom of the well.The BHCT of cement slurry changes continuously under cementing processes with multi-fluid and multi-step,making it a transient value.The BHCT of the lead slurry and tail slurry are not equal,with the maximum BHCT of the tail slurry being 2.46°C higher than that of the lead slurry.If circulation flushing and wellbore preparation are not considered,the calculated BHCT of the cement slurry will have errors of+6.8%and-1.9%.The study highlighted that considering thermal effects of all cementing stages,such as circulation flushing and wellbore preparation,in BHCT calculations can help improve prediction accuracy.
基金supported by the National Key Research and Development Program of China(2022YFB3504501)the National Natural Science Foundation of China(52274355)。
文摘This paper focuses on the preparation of rare earth oxide products from rare earth chloride solutions during the rare earth extraction and separation processes,as well as the recycling of magnesium chloride solutions.It proposes the idea of introducing spray pyrolysis technology into the rare earth extraction and separation processes.This paper briefly describes the development history of chloride spray pyrolysis technology,focusing on the research status and application progress of rare earth chloride solution and magnesium chloride solution spray pyrolysis technology,as well as spray pyrolysis equipment.The paper also analyzes the challenges and technical intricacies associated with applying spray pyrolysis technology to chloride solutions in the rare earth extraction and separation processes.Additionally,it explores future trends and proposes strategies to facilitate the full recycling of acids and bases,streamline the process flow,and enhance the prospects for green and low-carbon rare earth metallurgy.
基金supported by the National Natural Science Foundation of China(No.12372045)the National Key Research and the Development Program of China(Nos.2023YFC2205900,2023YFC2205901)。
文摘This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.
基金supported by the National Natural Science Foundation of China(Nos.42007178 and 41907327)the Natural Science Foundation of Hubei(Nos.2020CFB463 and 2019CFB372)+4 种基金China Geological Survey(Nos.DD20160304 and DD20190824)Fundamental Research Funds for the Central Universities(Nos.CUG 190644 and CUGL180817)National Key Research and Development Program(No.2019YFC1805502)Key Laboratory of Karst Dynamics,MNR and GZAR(Institute of Karst Geology,CAGS)Guilin(No.KDL201703)Key Laboratory of Karst Ecosystem and Treatment of Rocky Desertification,MNR and IRCK by UNESCO(No.KDL201903)。
文摘To investigate groundwater flow and solute transport characteristics of the karst trough zone in China,tracer experiments were conducted at two adjacent typical karst groundwater flow systems(Yuquandong(YQD)and Migongquan(MGQ))in Sixi valley,western Hubei,China.Highresolution continuous monitoring was utilized to obtain breakthrough curves(BTCs),which were then analyzed using the multi-dispersion model(MDM)and the two-region nonequilibrium model(2RNE)with basic parameters calculated by CXTFIT and QTRACER2.Results showed that:(1)YQD flow system had a complex infiltration matrix with overland flow,conduit flow and fracture flow,while the MGQ flow system was dominated by conduit flow with fast flow transport velocity,but also small amount of fracture flow there;(2)They were well fitted based on the MDM(R^2=0.928)and 2RNE(R^2=0.947)models,indicating that they had strong adaptability in the karst trough zone;(3)conceptual models for YQD and MGQ groundwater systems were generalized.In YQD system,the solute was transported via overland flow during intense rainfall,while some infiltrated down into fissures and conduits.In MGQ system,most were directly transported to spring outlet in the fissureconduit network.
基金supported by the National Natural Science Foundation of China(No.42007428)the National Forage Industry Technology System Program of China(No.CARS34)+1 种基金the Key Research and Development Program of Shaanxi,China(No.2022SF-285)Shaanxi Province Forestry Science and Technology Innovation Program,China(No.SXLK2022-02-14)。
文摘Effective vegetation reconstruction plays a vital role in the restoration of desert ecosystems.However,in reconstruction of different vegetation types,the community characteristics,assembly processes,and functions of different soil microbial taxa under environmental changes are still disputed,which limits the understanding of the sustainability of desert restoration.Hence,we investigated the soil microbial community characteristics and functional attributes of grassland desert(GD),desert steppe(DS),typical steppe(TS),and artificial forest(AF)in the Mu Us Desert,China.Our findings confirmed the geographical conservation of soil microbial composition but highlighted decreased microbial diversity in TS.Meanwhile,the abundance of rare taxa and microbial community stability in TS improved.Heterogeneous and homogeneous selection determined the assembly of rare and abundant bacterial taxa,respectively,with both being significantly influenced by soil moisture.In contrast,fungal communities displayed stochastic processes and exhibited sensitivity to soil nutrient conditions.Furthermore,our investigation revealed a noteworthy augmentation in bacterial metabolic functionality in TS,aligning with improved vegetation restoration and the assemblage of abundant bacterial taxa.However,within nutrient-limited soils(GD,DS,and AF),the assembly dynamics of rare fungal taxa assumed a prominent role in augmenting their metabolic capacity and adaptability to desert ecosystems.These results highlighted the variations in the assembly processes and metabolic functions of soil microorganisms during vegetation reestablishment and provided corresponding theoretical support for anthropogenic revegetation of desert ecosystems.
文摘In November 1984,China launched its first expedition to the Southern Ocean and the Antarctic continent,culminating in the establishment of its first year-round research station—Great Wall Station—on the Antarctic Peninsula in February 1985.Forty years later,in February 2024,China’s fifth research station,Qinling Station,commenced operations on Inexpress-ible Island near Terra Nova Bay.
