With an increase in population and economic development,water withdrawals are close to or even exceed the amount of water available in many regions of the world.Modelling water withdrawals could help water planners im...With an increase in population and economic development,water withdrawals are close to or even exceed the amount of water available in many regions of the world.Modelling water withdrawals could help water planners improve the efficiency of water use,water resources allocation,and management in order to alleviate water crises.However,minimal information has been obtained on how water withdrawals have changed over space and time,especially on a regional or local scale.This research proposes a data-driven framework to help estimate county-level distribution of water withdrawals.Using this framework,spatial statistical methods are used to estimate water withdrawals for agricultural,industrial,and domestic purposes in the Huaihe River watershed in China for the period 1978–2018.Total water withdrawals were found to have more than doubled,from 292.55×10^(8)m^(3) in 1978 to 642.93×10^(8)m^(3) in 2009,and decreased to 602.63×10^(8)m^(3) in 2018.Agricultural water increased from 208.17×10^(8)m^(3) in 1978 to 435.80×10^(8)m^(3) in 2009 and decreased to 360.84×10^(8)m^(3) in 2018.Industrial and domestic water usage constantly increased throughout the 1978–2018 period.In 1978,industrial and domestic demands were 20.35×10^(8)m^(3) and 60.04×10^(8)m^(3),respectively,and up until 2018,the figures were 105.58×10^(8)m^(3) and 136.20×10^(8)m^(3).From a spatial distribution perspective,Moran’s I statistical results show that the total water withdrawal has significant spatial autocorrelation during 1978–2018.The overall trend was a gradual increase in 1978–2010 with withdrawal beginning to decline in 2010–2018.The results of Getis-Ord G_(i)^(*)statistical calculations showed spatially contiguous clusters of total water withdrawal in the Huaihe River watershed during1978–2010,and the spatial agglomeration weakened from 2010 to 2018.This study provides a data-driven framework for assessing water withdrawals to enable a deeper understanding of competing water use among economic sectors as well as water withdrawal modelled with proper data resource and method.展开更多
A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis ca...A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors.In fact,each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models.Inspired by the idea,we propose a hybrid data-driven analysis framework in this study,where the contributions of dominant factors are quantitatively evaluated,the productions are precisely forecasted,and the development optimization suggestions are comprehensively generated.More specifically,game theory and machine learning models are coupled to determine the dominating geological and engineering factors.The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors.A multi-model-fused stacked model is trained for production forecast,which provides the basis for derivative-free optimization algorithms to optimize the development plan.The complete workflow is validated with actual production data collected from the Fuling shale gas field,Sichuan Basin,China.The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization.Comparing with traditional and experience-based approaches,the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.展开更多
Against the backdrop of the national innovation strategy and the digital transformation of education,the traditional“extensive”training model for innovation and entrepreneurship talents struggles to meet the persona...Against the backdrop of the national innovation strategy and the digital transformation of education,the traditional“extensive”training model for innovation and entrepreneurship talents struggles to meet the personalized development needs of students,making an urgent shift toward precision and intelligence necessary.This study constructs a four-dimensional integrated framework centered on data,“Goal-Data-Intervention-Evaluation”,and proposes a data-driven training model for innovation and entrepreneurship talents in universities.By collecting multi-source data such as learning behaviors,competency assessments,and practical projects,the model conducts in-depth analysis of students’individual characteristics and development potential,enabling precise decision-making in goal setting,teaching intervention,and practical guidance.Based on data analysis,a supportive system for personalized teaching and practical activities is established.Combined with process-oriented and summative evaluations,a closed-loop feedback mechanism is formed to improve training effectiveness.This model provides a theoretical framework and practical path for the scientific,personalized,and intelligent development of innovation and entrepreneurship education in universities.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
Under hydrothermal and solvothermal conditions,two novel cobalt-based complexes,{[Co_(2)(CIA)(OH)(1,4-dtb)]·3.2H_(2)O}n(HU23)and{[Co_(2)(CIA)(OH)(1,4-dib)]·3.5H2O·DMF}n(HU24),were successfully construct...Under hydrothermal and solvothermal conditions,two novel cobalt-based complexes,{[Co_(2)(CIA)(OH)(1,4-dtb)]·3.2H_(2)O}n(HU23)and{[Co_(2)(CIA)(OH)(1,4-dib)]·3.5H2O·DMF}n(HU24),were successfully constructed by coordinatively assembling the semi-rigid multidentate ligand 5-(1-carboxyethoxy)isophthalic acid(H₃CIA)with the Nheterocyclic ligands 1,4-di(4H-1,2,4-triazol-4-yl)benzene(1,4-dtb)and 1,4-di(1H-imidazol-1-yl)benzene(1,4-dib),respectively,around Co^(2+)ions.Single-crystal X-ray diffraction analysis revealed that in both complexes HU23 and HU24,the CIA^(3-)anions adopt aκ^(7)-coordination mode,bridging six Co^(2+)ions via their five carboxylate oxygen atoms and one ether oxygen atom.This linkage forms tetranuclear[Co4(μ3-OH)2]^(6+)units.These Co-oxo cluster units were interconnected by CIA^(3-)anions to assemble into 2D kgd-type structures featuring a 3,6-connected topology.The 2D layers were further connected by 1,4-dtb and 1,4-dib,resulting in 3D pillar-layered frameworks for HU23 and HU24.Notably,despite the similar configurations of 1,4-dtb and 1,4-dib,differences in their coordination spatial orientations lead to topological divergence in the 3D frameworks of HU23 and HU24.Topological analysis indicates that the frameworks of HU23 and HU24 can be simplified into a 3,10-connected net(point symbol:(4^(10).6^(3).8^(2))(4^(3))_(2))and a 3,8-connected tfz-d net(point symbol:(4^(3))_(2)((4^(6).6^(18).8^(4)))),respectively.This structural differentiation confirms the precise regulatory role of ligands on the topology of metal-organic frameworks.Moreover,the ultraviolet-visible absorption spectra confirmed that HU23 and HU24 have strong absorption capabilities for ultraviolet and visible light.According to the Kubelka-Munk method,their bandwidths were 2.15 and 2.08 eV,respectively,which are consistent with those of typical semiconductor materials.Variable-temperature magnetic susceptibility measurements(2-300 K)revealed significant antiferromagnetic coupling in both complexes,with their effective magnetic moments decreasing markedly as the temperature lowered.CCDC:2457554,HU23;2457553,HU24.展开更多
Cellulose frameworks have emerged as promising materials for light management due to their exceptional light-scattering capabilities and sustainable nature.Conventional biomass-derived cellulose frameworks face a fund...Cellulose frameworks have emerged as promising materials for light management due to their exceptional light-scattering capabilities and sustainable nature.Conventional biomass-derived cellulose frameworks face a fundamental trade-off between haze and transparency,coupled with impractical thicknesses(≥1 mm).Inspired by squid’s skin-peeling mechanism,this work develops a peroxyformic acid(HCOOOH)-enabled precision peeling strategy to isolate intact 10-μm-thick bamboo green(BG)frameworks—100×thinner than wood-based counterparts while achieving an unprecedented optical performance(88%haze with 80%transparency).This performance surpasses delignified biomass(transparency<40%at 1 mm)and matches engineered cellulose composites,yet requires no energy-intensive nanofibrillation.The preserved native cellulose I crystalline structure(64.76%crystallinity)and wax-coated uniaxial fibril alignment(Hermans factor:0.23)contribute to high mechanical strength(903 MPa modulus)and broadband light scattering.As a light-management layer in polycrystalline silicon solar cells,the BG framework boosts photoelectric conversion efficiency by 0.41%absolute(18.74%→19.15%),outperforming synthetic anti-reflective coatings.The work establishes a scalable,waste-to-wealth route for optical-grade cellulose materials in next-generation optoelectronics.展开更多
CO_(2)reduction technology can promote the resource utilization of carbon and help alleviate global warming and energy supply pressure.It is an effective way to achieve energy conversion and utilization.Covalent organ...CO_(2)reduction technology can promote the resource utilization of carbon and help alleviate global warming and energy supply pressure.It is an effective way to achieve energy conversion and utilization.Covalent organic frameworks(COFs)are porous crystalline materials formed by connecting organic monomers through covalent bonds.They have the characteristics of functional diversity and rich chemical properties.Their advantages,such as high porosity,a wide range of visible light absorption,and excellent charge separation efficiency,give them good potential in CO_(2)capture,separation,and conversion.Currently,Cu is a key metal in the catalytic CO_(2)reduction reaction(CO_(2)RR)for the preparation of high-value-added chemicals.The preparation of highly stable and large-pore Cu-based COFs using COFs as an ideal sacrificial template for loading Cu can be used to develop high-performance electrocatalysts and photocatalysts.In this review,we discuss the latest advancements in this field,including the development of various Cu-based COFs and their applications as catalysts for CO_(2)RR.Here,we mainly introduce the synthesis strategies,some important characterization information,and the applications of electrocatalytic and photocatalytic CO_(2)conversion using these previously reported Cu-based COFs.展开更多
Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to...Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to forecast shale gas production is still challenging due to complex fracture networks,dynamic fracture properties,frac hits,complicated multiphase flow,and multi-scale flow as well as data quality and uncertainty.This work develops an integrated framework for evaluating shale gas well production based on data-driven models.Firstly,a comprehensive dominated-factor system has been established,including geological,drilling,fracturing,and production factors.Data processing and visualization are required to ensure data quality and determine final data set.A shale gas production evaluation model is developed to evaluate shale gas production levels.Finally,the random forest algorithm is used to forecast shale gas production.The prediction accuracy of shale gas production level is higher than 95%based on the shale gas reservoirs in China.Forty-one wells are randomly selected to predict cumulative gas production using the optimal regression model.The proposed shale gas production evaluation frame-work overcomes too many assumptions of analytical or semi-analytical models and avoids huge computation cost and poor generalization for numerical modelling.展开更多
In the manufacturing of thin wall components for aerospace industry,apart from the side wall contour error,the Remaining Bottom Thickness Error(RBTE)for the thin-wall pocket component(e.g.rocket shell)is of the same i...In the manufacturing of thin wall components for aerospace industry,apart from the side wall contour error,the Remaining Bottom Thickness Error(RBTE)for the thin-wall pocket component(e.g.rocket shell)is of the same importance but overlooked in current research.If the RBTE reduces by 30%,the weight reduction of the entire component will reach up to tens of kilograms while improving the dynamic balance performance of the large component.Current RBTE control requires the off-process measurement of limited discrete points on the component bottom to provide the reference value for compensation.This leads to incompleteness in the remaining bottom thickness control and redundant measurement in manufacturing.In this paper,the framework of data-driven physics based model is proposed and developed for the real-time prediction of critical quality for large components,which enables accurate prediction and compensation of RBTE value for the thin wall components.The physics based model considers the primary root cause,in terms of tool deflection and clamping stiffness induced Axial Material Removal Thickness(AMRT)variation,for the RBTE formation.And to incorporate the dynamic and inherent coupling of the complicated manufacturing system,the multi-feature fusion and machine learning algorithm,i.e.kernel Principal Component Analysis(kPCA)and kernel Support Vector Regression(kSVR),are incorporated with the physics based model.Therefore,the proposed data-driven physics based model combines both process mechanism and the system disturbance to achieve better prediction accuracy.The final verification experiment is implemented to validate the effectiveness of the proposed method for dimensional accuracy prediction in pocket milling,and the prediction accuracy of AMRT achieves 0.014 mm and 0.019 mm for straight and corner milling,respectively.展开更多
Steam cracking is the dominant technology for producing light olefins,which are believed to be the foundation of the chemical industry.Predictive models of the cracking process can boost production efficiency and prof...Steam cracking is the dominant technology for producing light olefins,which are believed to be the foundation of the chemical industry.Predictive models of the cracking process can boost production efficiency and profit margin.Rapid advancements in machine learning research have recently enabled data-driven solutions to usher in a new era of process modeling.Meanwhile,its practical application to steam cracking is still hindered by the trade-off between prediction accuracy and computational speed.This research presents a framework for data-driven intelligent modeling of the steam cracking process.Industrial data preparation and feature engineering techniques provide computational-ready datasets for the framework,and feedstock similarities are exploited using k-means clustering.We propose LArge-Residuals-Deletion Multivariate Adaptive Regression Spline(LARD-MARS),a modeling approach that explicitly generates output formulas and eliminates potentially outlying instances.The framework is validated further by the presentation of clustering results,the explanation of variable importance,and the testing and comparison of model performance.展开更多
Reduced order modeling(ROM)techniques are numerical methods that approximate the solution of parametric partial differential equation(PED)by properly combining the high-fidelity solutions of the problem obtained for s...Reduced order modeling(ROM)techniques are numerical methods that approximate the solution of parametric partial differential equation(PED)by properly combining the high-fidelity solutions of the problem obtained for several configurations,i.e.for several properly chosen values of the physical/geometrical parameters characterizing the problem.By starting from a database of high-fidelity solutions related to a certain values of the parameters,we apply the proper orthogonal decomposition with interpolation(PODI)and then reconstruct the variables of interest for new values of the parameters,i.e.different values from the ones included in the database.Furthermore,we present a preliminary web application through which one can run the ROM with a very user-friendly approach,without the need of having expertise in the numerical analysis and scientific computing field.The case study we have chosen to test the efficiency of our algorithm is represented by the aortic blood flow pattern in presence of a left ventricular(LVAD)assist device when varying the pump flow rate.展开更多
(2E,6E)-4-methyl-2,6-bis(pyridin-3-ylmethylene)cyclohexan-1-one(L_(1))and 4-methyl-2,6-bis[(E)-4-(pyridin-4-yl)benzylidene]cyclohexan-1-one(L_(2))were synthesized and combined with isophthalic acid(H_(2)IP),then under...(2E,6E)-4-methyl-2,6-bis(pyridin-3-ylmethylene)cyclohexan-1-one(L_(1))and 4-methyl-2,6-bis[(E)-4-(pyridin-4-yl)benzylidene]cyclohexan-1-one(L_(2))were synthesized and combined with isophthalic acid(H_(2)IP),then under solvothermal conditions,to react with transition metals achieving four novel metal-organic frameworks(MOFs):[Zn(IP)(L_(1))]_(n)(1),{[Cd(IP)(L_(1))]·H_(2)O}_(n)(2),{[Co(IP)(L_(1))]·H_(2)O}_(n)(3),and[Zn(IP)(L_(2))(H_(2)O)]_(n)(4).MOFs 1-4 have been characterized by single-crystal X-ray diffraction,powder X-ray diffraction,thermogravimetry,and elemental analysis.Single-crystal X-ray diffraction shows that MOF 1 crystallizes in the monoclinic crystal system with space group P2_(1)/n,and MOFs 2-4 belong to the triclinic system with the P1 space group.1-3 are 2D sheet structures,2 and 3 have similar structural characters,whereas 4 is a 1D chain structure.Furthermore,1-3 exhibited certain photocatalytic capability in the degradation of rhodamine B(Rh B)and pararosaniline hydrochloride(PH).4could be used as a heterogeneous catalyst for the Knoevenagel reaction starting with benzaldehyde derivative and malononitrile.4 could promote the reaction to achieve corresponding products in moderate yields within 3 h.Moreover,the catalyst exhibited recyclability for up to three cycles without significantly dropping its activity.A mechanism for MOF 4 catalyzed Knoevenagel condensation reaction of aromatic aldehyde and malononitrile has been initially proposed.CCDC:2356488,1;2356497,2;2356499,3;2356498,4.展开更多
Sulfur-doped iron-cobalt tannate nanorods(S-FeCoTA)derived from metal-organic frameworks(MOFs)as electrocatalysts were synthesized via a one-step hydrothermal method.The optimized S-FeCoTA was interlaced by loose nano...Sulfur-doped iron-cobalt tannate nanorods(S-FeCoTA)derived from metal-organic frameworks(MOFs)as electrocatalysts were synthesized via a one-step hydrothermal method.The optimized S-FeCoTA was interlaced by loose nanorods,which had many voids.The S-FeCoTA catalysts exhibited excellent electrochemical oxygen evolution reaction(OER)performance with a low overpotential of 273 mV at 10 mA·cm^(-2)and a small Tafel slope of 36 mV·dec^(-1)in 1 mol·L^(-1)KOH.The potential remained at 1.48 V(vs RHE)at 10 mA·cm^(-2)under continuous testing for 15 h,implying that S-FeCoTA had good stability.The Faraday efficiency of S-FeCoTA was 94%.The outstanding OER activity of S-FeCoTA is attributed to the synergistic effects among S,Fe,and Co,thus promoting electron transfer,reducing the reaction kinetic barrier,and enhancing the OER performance.展开更多
Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate predictio...Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate prediction of displacement and position of VIV in flexible risers remains challenging under actual marine conditions.This study presents a data-driven model for riser displacement prediction that corresponds to field conditions.Experimental data analysis reveals that the XGBoost algorithm predicts the maximum displacement and position with superior accuracy compared with Support vector regression(SVR),considering both computational efficiency and precision.Platform displacement in the Y-direction demonstrates a significant positive correlation with both axial depth and maximum displacement magnitude.The fourth point displacement exhibits the highest contribution to model prediction outcomes,showing a positive influence on maximum displacement while negatively affecting the axial depth of maximum displacement.Platform displacement in the X-and Y-directions exhibits competitive effects on both the riser’s maximum displacement and its axial depth.Through the implementation of XGBoost algorithm and SHapley Additive exPlanation(SHAP)analysis,the model effectively estimates the riser’s maximum displacement and its precise location.This data-driven approach achieves predictions using minimal,readily available data points,enhancing its practical field applications and demonstrating clear relevance to academic and professional communities.展开更多
Exploring efficient microwave absorbing materials(MAMs)has gradually become a hot topic in recent years because it is crucial in both civil and military fields.Metal-organic framework(MOF)has great potential due to it...Exploring efficient microwave absorbing materials(MAMs)has gradually become a hot topic in recent years because it is crucial in both civil and military fields.Metal-organic framework(MOF)has great potential due to its unique composition and bonding mode,which has advantages such as large specific surface area,high porosity,adjustable structure,and designable composition.Herein,MOF-derived MAMs are highlighted based on morphology and structure.The synthesis strategies of MOF-derived MAMs of different dimensions are discussed.On this basis,the structure-activity relationships can be deeply explored through the precise control of material structure and property by atomic engineering.Finally,perspectives are given for the existing problems of MOF-derived MAMs,which will open a new horizon and promote the development of MAMs.展开更多
The preparation of carbon-based electromagnetic wave(EMW)absorbers possessing thin matching thickness,wide absorption bandwidth,strong absorption intensity,and low filling ratio remains a huge challenge.Metal-organic ...The preparation of carbon-based electromagnetic wave(EMW)absorbers possessing thin matching thickness,wide absorption bandwidth,strong absorption intensity,and low filling ratio remains a huge challenge.Metal-organic frameworks(MOFs)are ideal self-sacrificing templates for the construction of carbon-based EMW absorbers.In this work,bimetallic FeMn-MOF-derived MnFe_(2)O_(4)/C/graphene composites were fabricated via a two-step route of solvothermal reaction and the following pyrolysis treatment.The results re-veal the evolution of the microscopic morphology of carbon skeletons from loofah-like to octahedral and then to polyhedron and pomegran-ate after the adjustment of the Fe^(3+)to Mn^(2+)molar ratio.Furthermore,at the Fe^(3+)to Mn^(2+)molar ratio of 2:1,the obtained MnFe_(2)O_(4)/C/graphene composite exhibited the highest EMW absorption capacity.Specifically,a minimum reflection loss of-72.7 dB and a max-imum effective absorption bandwidth of 5.1 GHz were achieved at a low filling ratio of 10wt%.In addition,the possible EMW absorp-tion mechanism of MnFe_(2)O_(4)/C/graphene composites was proposed.Therefore,the results of this work will contribute to the construction of broadband and efficient carbon-based EMW absorbers derived from MOFs.展开更多
Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods...Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods.The research adopts the method of combining theoretical analysis and practical application,and designs the evidence-based value-added evaluation framework,which includes the core elements of a multi-source heterogeneous data acquisition and processing system,a value-added evaluation agent based on a large model,and an evaluation implementation and application mechanism.Through empirical research verification,the evaluation system has remarkable effects in improving learning participation,promoting ability development,and supporting teaching decision-making,and provides a theoretical reference and practical path for educational evaluation reform in the new era.The research shows that the evidence-based value-added evaluation system based on data-driven can reflect students’actual progress more fairly and objectively by accurately measuring the difference in starting point and development range of students,and provide strong support for the realization of high-quality education development.展开更多
Covalent organic frameworks(COFs)have great potential as adsorbents due to their customizable functionality,low density and high porosity.However,COFs powder exists with poor processing and recycling performance.Moreo...Covalent organic frameworks(COFs)have great potential as adsorbents due to their customizable functionality,low density and high porosity.However,COFs powder exists with poor processing and recycling performance.Moreover,due to the accumulation of COFs nanoparticles,it is not conducive to the full utilization of their surface functional groups.Currently,the strategy of COFs assembling into aerogel can be a good solution to this problem.Herein,we successfully synthesize composite aerogels(CSR)by in-situ self-assembly of two-dimensional COFs and graphene based on crosslinking of sodium alginate.Sodium alginate in the composite improves the mechanical properties of the aerogel,and graphene provides a template for the in-situ growth of COFs.Impressively,CSR aerogels with different COFs and sizes can be prepared by changing the moiety of the ligand and modulating the addition amount of COFs.The prepared CSR aerogels exhibit porous,low density,good processability and good mechanical properties.Among them,the density of CSR-N-1.6 is only 5 mg/cm3,which is the lowest density among the reported COF aerogels so far.Due to these remarkable properties,CSR aerogels perform excellent adsorption and recycling properties for the efficient and rapid removal of organic pollutants(organic dyes and antibiotics)from polluted water.In addition,it is also possible to visually recognize the presence of antibiotics by fluorescence detection.This work not only provides a new strategy for synthesizing COF aerogels,but also accelerates the practical application of COF aerogels and contributes to environmental remediation.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
基金Under the auspices of the National Natural Science Foundation of China(No.71203200)the National Social Science Fund Project(No.20&ZD138)+1 种基金the National Science and Technology Platform Construction Project(No.2005DKA32300)the Major Research Projects of the Ministry of Education(No.16JJD770019)。
文摘With an increase in population and economic development,water withdrawals are close to or even exceed the amount of water available in many regions of the world.Modelling water withdrawals could help water planners improve the efficiency of water use,water resources allocation,and management in order to alleviate water crises.However,minimal information has been obtained on how water withdrawals have changed over space and time,especially on a regional or local scale.This research proposes a data-driven framework to help estimate county-level distribution of water withdrawals.Using this framework,spatial statistical methods are used to estimate water withdrawals for agricultural,industrial,and domestic purposes in the Huaihe River watershed in China for the period 1978–2018.Total water withdrawals were found to have more than doubled,from 292.55×10^(8)m^(3) in 1978 to 642.93×10^(8)m^(3) in 2009,and decreased to 602.63×10^(8)m^(3) in 2018.Agricultural water increased from 208.17×10^(8)m^(3) in 1978 to 435.80×10^(8)m^(3) in 2009 and decreased to 360.84×10^(8)m^(3) in 2018.Industrial and domestic water usage constantly increased throughout the 1978–2018 period.In 1978,industrial and domestic demands were 20.35×10^(8)m^(3) and 60.04×10^(8)m^(3),respectively,and up until 2018,the figures were 105.58×10^(8)m^(3) and 136.20×10^(8)m^(3).From a spatial distribution perspective,Moran’s I statistical results show that the total water withdrawal has significant spatial autocorrelation during 1978–2018.The overall trend was a gradual increase in 1978–2010 with withdrawal beginning to decline in 2010–2018.The results of Getis-Ord G_(i)^(*)statistical calculations showed spatially contiguous clusters of total water withdrawal in the Huaihe River watershed during1978–2010,and the spatial agglomeration weakened from 2010 to 2018.This study provides a data-driven framework for assessing water withdrawals to enable a deeper understanding of competing water use among economic sectors as well as water withdrawal modelled with proper data resource and method.
基金This work was supported by the National Natural Science Foundation of China(Grant No.42050104)the Science Foundation of SINOPEC Group(Grant No.P20030).
文摘A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors.In fact,each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models.Inspired by the idea,we propose a hybrid data-driven analysis framework in this study,where the contributions of dominant factors are quantitatively evaluated,the productions are precisely forecasted,and the development optimization suggestions are comprehensively generated.More specifically,game theory and machine learning models are coupled to determine the dominating geological and engineering factors.The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors.A multi-model-fused stacked model is trained for production forecast,which provides the basis for derivative-free optimization algorithms to optimize the development plan.The complete workflow is validated with actual production data collected from the Fuling shale gas field,Sichuan Basin,China.The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization.Comparing with traditional and experience-based approaches,the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.
基金Special Fund for Teacher Development Research Program of University of Shanghai for Science and Technology(Project No.:CFTD2025YB28)。
文摘Against the backdrop of the national innovation strategy and the digital transformation of education,the traditional“extensive”training model for innovation and entrepreneurship talents struggles to meet the personalized development needs of students,making an urgent shift toward precision and intelligence necessary.This study constructs a four-dimensional integrated framework centered on data,“Goal-Data-Intervention-Evaluation”,and proposes a data-driven training model for innovation and entrepreneurship talents in universities.By collecting multi-source data such as learning behaviors,competency assessments,and practical projects,the model conducts in-depth analysis of students’individual characteristics and development potential,enabling precise decision-making in goal setting,teaching intervention,and practical guidance.Based on data analysis,a supportive system for personalized teaching and practical activities is established.Combined with process-oriented and summative evaluations,a closed-loop feedback mechanism is formed to improve training effectiveness.This model provides a theoretical framework and practical path for the scientific,personalized,and intelligent development of innovation and entrepreneurship education in universities.
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Under hydrothermal and solvothermal conditions,two novel cobalt-based complexes,{[Co_(2)(CIA)(OH)(1,4-dtb)]·3.2H_(2)O}n(HU23)and{[Co_(2)(CIA)(OH)(1,4-dib)]·3.5H2O·DMF}n(HU24),were successfully constructed by coordinatively assembling the semi-rigid multidentate ligand 5-(1-carboxyethoxy)isophthalic acid(H₃CIA)with the Nheterocyclic ligands 1,4-di(4H-1,2,4-triazol-4-yl)benzene(1,4-dtb)and 1,4-di(1H-imidazol-1-yl)benzene(1,4-dib),respectively,around Co^(2+)ions.Single-crystal X-ray diffraction analysis revealed that in both complexes HU23 and HU24,the CIA^(3-)anions adopt aκ^(7)-coordination mode,bridging six Co^(2+)ions via their five carboxylate oxygen atoms and one ether oxygen atom.This linkage forms tetranuclear[Co4(μ3-OH)2]^(6+)units.These Co-oxo cluster units were interconnected by CIA^(3-)anions to assemble into 2D kgd-type structures featuring a 3,6-connected topology.The 2D layers were further connected by 1,4-dtb and 1,4-dib,resulting in 3D pillar-layered frameworks for HU23 and HU24.Notably,despite the similar configurations of 1,4-dtb and 1,4-dib,differences in their coordination spatial orientations lead to topological divergence in the 3D frameworks of HU23 and HU24.Topological analysis indicates that the frameworks of HU23 and HU24 can be simplified into a 3,10-connected net(point symbol:(4^(10).6^(3).8^(2))(4^(3))_(2))and a 3,8-connected tfz-d net(point symbol:(4^(3))_(2)((4^(6).6^(18).8^(4)))),respectively.This structural differentiation confirms the precise regulatory role of ligands on the topology of metal-organic frameworks.Moreover,the ultraviolet-visible absorption spectra confirmed that HU23 and HU24 have strong absorption capabilities for ultraviolet and visible light.According to the Kubelka-Munk method,their bandwidths were 2.15 and 2.08 eV,respectively,which are consistent with those of typical semiconductor materials.Variable-temperature magnetic susceptibility measurements(2-300 K)revealed significant antiferromagnetic coupling in both complexes,with their effective magnetic moments decreasing markedly as the temperature lowered.CCDC:2457554,HU23;2457553,HU24.
基金supported by National Natural Science Foundation of China(32494793).
文摘Cellulose frameworks have emerged as promising materials for light management due to their exceptional light-scattering capabilities and sustainable nature.Conventional biomass-derived cellulose frameworks face a fundamental trade-off between haze and transparency,coupled with impractical thicknesses(≥1 mm).Inspired by squid’s skin-peeling mechanism,this work develops a peroxyformic acid(HCOOOH)-enabled precision peeling strategy to isolate intact 10-μm-thick bamboo green(BG)frameworks—100×thinner than wood-based counterparts while achieving an unprecedented optical performance(88%haze with 80%transparency).This performance surpasses delignified biomass(transparency<40%at 1 mm)and matches engineered cellulose composites,yet requires no energy-intensive nanofibrillation.The preserved native cellulose I crystalline structure(64.76%crystallinity)and wax-coated uniaxial fibril alignment(Hermans factor:0.23)contribute to high mechanical strength(903 MPa modulus)and broadband light scattering.As a light-management layer in polycrystalline silicon solar cells,the BG framework boosts photoelectric conversion efficiency by 0.41%absolute(18.74%→19.15%),outperforming synthetic anti-reflective coatings.The work establishes a scalable,waste-to-wealth route for optical-grade cellulose materials in next-generation optoelectronics.
文摘CO_(2)reduction technology can promote the resource utilization of carbon and help alleviate global warming and energy supply pressure.It is an effective way to achieve energy conversion and utilization.Covalent organic frameworks(COFs)are porous crystalline materials formed by connecting organic monomers through covalent bonds.They have the characteristics of functional diversity and rich chemical properties.Their advantages,such as high porosity,a wide range of visible light absorption,and excellent charge separation efficiency,give them good potential in CO_(2)capture,separation,and conversion.Currently,Cu is a key metal in the catalytic CO_(2)reduction reaction(CO_(2)RR)for the preparation of high-value-added chemicals.The preparation of highly stable and large-pore Cu-based COFs using COFs as an ideal sacrificial template for loading Cu can be used to develop high-performance electrocatalysts and photocatalysts.In this review,we discuss the latest advancements in this field,including the development of various Cu-based COFs and their applications as catalysts for CO_(2)RR.Here,we mainly introduce the synthesis strategies,some important characterization information,and the applications of electrocatalytic and photocatalytic CO_(2)conversion using these previously reported Cu-based COFs.
基金funded by National Natural Science Foundation of China(52004238)China Postdoctoral Science Foundation(2019M663561).
文摘Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to forecast shale gas production is still challenging due to complex fracture networks,dynamic fracture properties,frac hits,complicated multiphase flow,and multi-scale flow as well as data quality and uncertainty.This work develops an integrated framework for evaluating shale gas well production based on data-driven models.Firstly,a comprehensive dominated-factor system has been established,including geological,drilling,fracturing,and production factors.Data processing and visualization are required to ensure data quality and determine final data set.A shale gas production evaluation model is developed to evaluate shale gas production levels.Finally,the random forest algorithm is used to forecast shale gas production.The prediction accuracy of shale gas production level is higher than 95%based on the shale gas reservoirs in China.Forty-one wells are randomly selected to predict cumulative gas production using the optimal regression model.The proposed shale gas production evaluation frame-work overcomes too many assumptions of analytical or semi-analytical models and avoids huge computation cost and poor generalization for numerical modelling.
基金the Science and Technology Major Project of China(No.2019ZX04020001-004,2017ZX04007001)。
文摘In the manufacturing of thin wall components for aerospace industry,apart from the side wall contour error,the Remaining Bottom Thickness Error(RBTE)for the thin-wall pocket component(e.g.rocket shell)is of the same importance but overlooked in current research.If the RBTE reduces by 30%,the weight reduction of the entire component will reach up to tens of kilograms while improving the dynamic balance performance of the large component.Current RBTE control requires the off-process measurement of limited discrete points on the component bottom to provide the reference value for compensation.This leads to incompleteness in the remaining bottom thickness control and redundant measurement in manufacturing.In this paper,the framework of data-driven physics based model is proposed and developed for the real-time prediction of critical quality for large components,which enables accurate prediction and compensation of RBTE value for the thin wall components.The physics based model considers the primary root cause,in terms of tool deflection and clamping stiffness induced Axial Material Removal Thickness(AMRT)variation,for the RBTE formation.And to incorporate the dynamic and inherent coupling of the complicated manufacturing system,the multi-feature fusion and machine learning algorithm,i.e.kernel Principal Component Analysis(kPCA)and kernel Support Vector Regression(kSVR),are incorporated with the physics based model.Therefore,the proposed data-driven physics based model combines both process mechanism and the system disturbance to achieve better prediction accuracy.The final verification experiment is implemented to validate the effectiveness of the proposed method for dimensional accuracy prediction in pocket milling,and the prediction accuracy of AMRT achieves 0.014 mm and 0.019 mm for straight and corner milling,respectively.
基金supported by the National Key Research and Development Program of China(2021 YFB 4000500,2021 YFB 4000501,and 2021 YFB 4000502)。
文摘Steam cracking is the dominant technology for producing light olefins,which are believed to be the foundation of the chemical industry.Predictive models of the cracking process can boost production efficiency and profit margin.Rapid advancements in machine learning research have recently enabled data-driven solutions to usher in a new era of process modeling.Meanwhile,its practical application to steam cracking is still hindered by the trade-off between prediction accuracy and computational speed.This research presents a framework for data-driven intelligent modeling of the steam cracking process.Industrial data preparation and feature engineering techniques provide computational-ready datasets for the framework,and feedstock similarities are exploited using k-means clustering.We propose LArge-Residuals-Deletion Multivariate Adaptive Regression Spline(LARD-MARS),a modeling approach that explicitly generates output formulas and eliminates potentially outlying instances.The framework is validated further by the presentation of clustering results,the explanation of variable importance,and the testing and comparison of model performance.
基金supported by the European Research Council Executive Agency by the Consolidator Grant project AROMA-CFD“Advanced Reduced Order Methods with Applications in Computational Fluid Dynamics”--GA 681447,H2020-ERC CoG 2015 AROMA-CFD and INdAM-GNCS 2020 project“Tecniche Numeriche Avanzate per Applicazioni Industriali”。
文摘Reduced order modeling(ROM)techniques are numerical methods that approximate the solution of parametric partial differential equation(PED)by properly combining the high-fidelity solutions of the problem obtained for several configurations,i.e.for several properly chosen values of the physical/geometrical parameters characterizing the problem.By starting from a database of high-fidelity solutions related to a certain values of the parameters,we apply the proper orthogonal decomposition with interpolation(PODI)and then reconstruct the variables of interest for new values of the parameters,i.e.different values from the ones included in the database.Furthermore,we present a preliminary web application through which one can run the ROM with a very user-friendly approach,without the need of having expertise in the numerical analysis and scientific computing field.The case study we have chosen to test the efficiency of our algorithm is represented by the aortic blood flow pattern in presence of a left ventricular(LVAD)assist device when varying the pump flow rate.
文摘(2E,6E)-4-methyl-2,6-bis(pyridin-3-ylmethylene)cyclohexan-1-one(L_(1))and 4-methyl-2,6-bis[(E)-4-(pyridin-4-yl)benzylidene]cyclohexan-1-one(L_(2))were synthesized and combined with isophthalic acid(H_(2)IP),then under solvothermal conditions,to react with transition metals achieving four novel metal-organic frameworks(MOFs):[Zn(IP)(L_(1))]_(n)(1),{[Cd(IP)(L_(1))]·H_(2)O}_(n)(2),{[Co(IP)(L_(1))]·H_(2)O}_(n)(3),and[Zn(IP)(L_(2))(H_(2)O)]_(n)(4).MOFs 1-4 have been characterized by single-crystal X-ray diffraction,powder X-ray diffraction,thermogravimetry,and elemental analysis.Single-crystal X-ray diffraction shows that MOF 1 crystallizes in the monoclinic crystal system with space group P2_(1)/n,and MOFs 2-4 belong to the triclinic system with the P1 space group.1-3 are 2D sheet structures,2 and 3 have similar structural characters,whereas 4 is a 1D chain structure.Furthermore,1-3 exhibited certain photocatalytic capability in the degradation of rhodamine B(Rh B)and pararosaniline hydrochloride(PH).4could be used as a heterogeneous catalyst for the Knoevenagel reaction starting with benzaldehyde derivative and malononitrile.4 could promote the reaction to achieve corresponding products in moderate yields within 3 h.Moreover,the catalyst exhibited recyclability for up to three cycles without significantly dropping its activity.A mechanism for MOF 4 catalyzed Knoevenagel condensation reaction of aromatic aldehyde and malononitrile has been initially proposed.CCDC:2356488,1;2356497,2;2356499,3;2356498,4.
文摘Sulfur-doped iron-cobalt tannate nanorods(S-FeCoTA)derived from metal-organic frameworks(MOFs)as electrocatalysts were synthesized via a one-step hydrothermal method.The optimized S-FeCoTA was interlaced by loose nanorods,which had many voids.The S-FeCoTA catalysts exhibited excellent electrochemical oxygen evolution reaction(OER)performance with a low overpotential of 273 mV at 10 mA·cm^(-2)and a small Tafel slope of 36 mV·dec^(-1)in 1 mol·L^(-1)KOH.The potential remained at 1.48 V(vs RHE)at 10 mA·cm^(-2)under continuous testing for 15 h,implying that S-FeCoTA had good stability.The Faraday efficiency of S-FeCoTA was 94%.The outstanding OER activity of S-FeCoTA is attributed to the synergistic effects among S,Fe,and Co,thus promoting electron transfer,reducing the reaction kinetic barrier,and enhancing the OER performance.
基金The research work was financially supported by the National Natural Science Foundation of China(Grant Nos.51979238 and 52301338)the Sichuan Science and Technology Program(Grant Nos.2023NSFSC1953 and 2023ZYD0140).
文摘Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate prediction of displacement and position of VIV in flexible risers remains challenging under actual marine conditions.This study presents a data-driven model for riser displacement prediction that corresponds to field conditions.Experimental data analysis reveals that the XGBoost algorithm predicts the maximum displacement and position with superior accuracy compared with Support vector regression(SVR),considering both computational efficiency and precision.Platform displacement in the Y-direction demonstrates a significant positive correlation with both axial depth and maximum displacement magnitude.The fourth point displacement exhibits the highest contribution to model prediction outcomes,showing a positive influence on maximum displacement while negatively affecting the axial depth of maximum displacement.Platform displacement in the X-and Y-directions exhibits competitive effects on both the riser’s maximum displacement and its axial depth.Through the implementation of XGBoost algorithm and SHapley Additive exPlanation(SHAP)analysis,the model effectively estimates the riser’s maximum displacement and its precise location.This data-driven approach achieves predictions using minimal,readily available data points,enhancing its practical field applications and demonstrating clear relevance to academic and professional communities.
基金supported by the National Natural Science Foundation of China(Nos.52373280,52177014 and 52273257).
文摘Exploring efficient microwave absorbing materials(MAMs)has gradually become a hot topic in recent years because it is crucial in both civil and military fields.Metal-organic framework(MOF)has great potential due to its unique composition and bonding mode,which has advantages such as large specific surface area,high porosity,adjustable structure,and designable composition.Herein,MOF-derived MAMs are highlighted based on morphology and structure.The synthesis strategies of MOF-derived MAMs of different dimensions are discussed.On this basis,the structure-activity relationships can be deeply explored through the precise control of material structure and property by atomic engineering.Finally,perspectives are given for the existing problems of MOF-derived MAMs,which will open a new horizon and promote the development of MAMs.
基金supported by the Natural Science Research Project of the Anhui Educational Committee,China(No.2022AH050827)the Open Research Fund Program of Anhui Province Key Laboratory of Specialty Polymers,Anhui University of Science and Technology,China(No.AHKLSP23-12)the Joint National-Local Engineering Research Center for Safe and Precise Coal Mining Fund,China(No.EC2022020)。
文摘The preparation of carbon-based electromagnetic wave(EMW)absorbers possessing thin matching thickness,wide absorption bandwidth,strong absorption intensity,and low filling ratio remains a huge challenge.Metal-organic frameworks(MOFs)are ideal self-sacrificing templates for the construction of carbon-based EMW absorbers.In this work,bimetallic FeMn-MOF-derived MnFe_(2)O_(4)/C/graphene composites were fabricated via a two-step route of solvothermal reaction and the following pyrolysis treatment.The results re-veal the evolution of the microscopic morphology of carbon skeletons from loofah-like to octahedral and then to polyhedron and pomegran-ate after the adjustment of the Fe^(3+)to Mn^(2+)molar ratio.Furthermore,at the Fe^(3+)to Mn^(2+)molar ratio of 2:1,the obtained MnFe_(2)O_(4)/C/graphene composite exhibited the highest EMW absorption capacity.Specifically,a minimum reflection loss of-72.7 dB and a max-imum effective absorption bandwidth of 5.1 GHz were achieved at a low filling ratio of 10wt%.In addition,the possible EMW absorp-tion mechanism of MnFe_(2)O_(4)/C/graphene composites was proposed.Therefore,the results of this work will contribute to the construction of broadband and efficient carbon-based EMW absorbers derived from MOFs.
基金This paper is the research result of“Research on Innovation of Evidence-Based Teaching Paradigm in Vocational Education under the Background of New Quality Productivity”(2024JXQ176)the Shandong Province Artificial Intelligence Education Research Project(SDDJ202501035),which explores the application of artificial intelligence big models in student value-added evaluation from an evidence-based perspective。
文摘Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods.The research adopts the method of combining theoretical analysis and practical application,and designs the evidence-based value-added evaluation framework,which includes the core elements of a multi-source heterogeneous data acquisition and processing system,a value-added evaluation agent based on a large model,and an evaluation implementation and application mechanism.Through empirical research verification,the evaluation system has remarkable effects in improving learning participation,promoting ability development,and supporting teaching decision-making,and provides a theoretical reference and practical path for educational evaluation reform in the new era.The research shows that the evidence-based value-added evaluation system based on data-driven can reflect students’actual progress more fairly and objectively by accurately measuring the difference in starting point and development range of students,and provide strong support for the realization of high-quality education development.
基金the financial support provided by the National Natural Science Foundation of China(Nos.22175094,21971113)。
文摘Covalent organic frameworks(COFs)have great potential as adsorbents due to their customizable functionality,low density and high porosity.However,COFs powder exists with poor processing and recycling performance.Moreover,due to the accumulation of COFs nanoparticles,it is not conducive to the full utilization of their surface functional groups.Currently,the strategy of COFs assembling into aerogel can be a good solution to this problem.Herein,we successfully synthesize composite aerogels(CSR)by in-situ self-assembly of two-dimensional COFs and graphene based on crosslinking of sodium alginate.Sodium alginate in the composite improves the mechanical properties of the aerogel,and graphene provides a template for the in-situ growth of COFs.Impressively,CSR aerogels with different COFs and sizes can be prepared by changing the moiety of the ligand and modulating the addition amount of COFs.The prepared CSR aerogels exhibit porous,low density,good processability and good mechanical properties.Among them,the density of CSR-N-1.6 is only 5 mg/cm3,which is the lowest density among the reported COF aerogels so far.Due to these remarkable properties,CSR aerogels perform excellent adsorption and recycling properties for the efficient and rapid removal of organic pollutants(organic dyes and antibiotics)from polluted water.In addition,it is also possible to visually recognize the presence of antibiotics by fluorescence detection.This work not only provides a new strategy for synthesizing COF aerogels,but also accelerates the practical application of COF aerogels and contributes to environmental remediation.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
