Wind energy is one of the widely applied renewable energies in the world. Wind turbine as the main wind energy converter at present has very complex technical system containing a huge number of components,actuators an...Wind energy is one of the widely applied renewable energies in the world. Wind turbine as the main wind energy converter at present has very complex technical system containing a huge number of components,actuators and sensors. However, despite of the hardware redundancy, sensor faults have often affected the wind turbine normal operation and thus caused energy generation loss. In this paper, aiming at the wind turbine hydraulic pitch system, data-driven design of process monitoring(PM) and diagnosis has been realized in the wind turbine benchmark. Fault tolerant control(FTC) strategies focused on sensor faults have also been presented here, where with the implementation of soft sensor the sensor fault can be handled and the performance of the system is improved. The performance of this method is demonstrated with the wind turbine benchmark provided by Math Works.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
Recent reports suggest that aging is not solely a physiological process in living beings;instead, it should be considered a pathological process or disease(Amorim et al., 2022). Consequently, this process involves a w...Recent reports suggest that aging is not solely a physiological process in living beings;instead, it should be considered a pathological process or disease(Amorim et al., 2022). Consequently, this process involves a wide range of factors, spanning from genetic to environmental factors, and even includes the gut microbiome(GM)(Mayer et al., 2022). All these processes coincide at some point in the inflammatory process, oxidative stress, and apoptosis, at different degrees in various organs and systems that constitute a living organism(Mayer et al., 2022;AguilarHernández et al., 2023).展开更多
Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley a...Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.展开更多
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
Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance ...Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH estimation methods,offering valuable insights for the development of advanced battery management systems and embedded application research.展开更多
Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the p...Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental factors.This cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.展开更多
The performance and corresponding applications of polymer nanocomposites are highly dominated by the choice of base material,type of fillers,and the processing ways.Carbon black-filled rubber composites(CRC)exemplify ...The performance and corresponding applications of polymer nanocomposites are highly dominated by the choice of base material,type of fillers,and the processing ways.Carbon black-filled rubber composites(CRC)exemplify this,playing a crucial role in various industries.However,due to the complex interplay between these factors and the resulting properties,a simple yet accurate model to predict the mechanical properties of CRC,considering different rubbers,fillers,and processing techniques,is highly desired.This study aims to predict the dispersion of fillers in CRC and forecast the resultant mechanical properties of CRC by leveraging machine learning.We selected various rubbers and carbon black fillers,conducted mixing and vulcanizing,and subsequently measured filler dispersion and tensile performance.Based on 215 experimental data points,we evaluated the performance of different machine learning models.Our findings indicate that the manually designed deep neural network(DNN)models achieved superior results,exhibiting the highest coefficient of determination(R^(2))values(>0.95).Shapley additive explanations(SHAP)analysis of the DNN models revealed the intricate relationship between the properties of CRC and process parameters.Moreover,based on the robust predictive capabilities of the DNN models,we can recommend or optimize CRC fabrication process.This work provides valuable insights for employing machine learning in predicting polymer composite material properties and optimizing the fabrication of high-performance CRC.展开更多
Recently published in Joule,Feng Liu and colleagues from Shanghai Jiaotong University reported a record-breaking 20.8%power conversion efficiency in organic solar cells(OSCs)with an interpenetrating fibril network act...Recently published in Joule,Feng Liu and colleagues from Shanghai Jiaotong University reported a record-breaking 20.8%power conversion efficiency in organic solar cells(OSCs)with an interpenetrating fibril network active layer morphology,featuring a bulk p-in structure and proper vertical segregation achieved through additive-assisted layer-by-layer deposition.This optimized hierarchical gradient fibrillar morphology and optical management synergistically facilitates exciton diffusion,reduces recombination losses,and enhances light capture capability.This approach not only offers a solution to achieving high-efficiency devices but also demonstrates the potential for commercial applications of OSCs.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
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
Fenton and Fenton-like processes,which could produce highly reactive species to degrade organic contaminants,have been widely used in the field of wastewater treatment.Therein,the chemistry of Fenton process including...Fenton and Fenton-like processes,which could produce highly reactive species to degrade organic contaminants,have been widely used in the field of wastewater treatment.Therein,the chemistry of Fenton process including the nature of active oxidants,the complicated reactions involved,and the behind reason for its strongly pH-dependent performance,is the basis for the application of Fenton and Fenton-like processes in wastewater treatment.Nevertheless,the conflicting views still exist about the mechanism of the Fenton process.For instance,reaching a unanimous consensus on the nature of active oxidants(hydroxyl radical or tetravalent iron)in this process remains challenging.This review comprehensively examined the mechanism of the Fenton process including the debate on the nature of active oxidants,reactions involved in the Fenton process,and the behind reason for the pH-dependent degradation of contaminants in the Fenton process.Then,we summarized several strategies that promote the Fe(Ⅱ)/Fe(Ⅲ)cycle,reduce the competitive consumption of active oxidants by side reactions,and replace the Fenton reagent,thus improving the performance of the Fenton process.Furthermore,advances for the future were proposed including the demand for the high-accuracy identification of active oxidants and taking advantages of the characteristic of target contaminants during the degradation of contaminants by the Fenton process.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
The Hualien M 7.3 earthquake on April 3,2024,was a significant and strong earthquake in Taiwan,China in the past two decades.The rupture process of the main shock and strong aftershocks is of great significance to the...The Hualien M 7.3 earthquake on April 3,2024,was a significant and strong earthquake in Taiwan,China in the past two decades.The rupture process of the main shock and strong aftershocks is of great significance to the subsequent seismic activity and seismogenic tectonic research.Based on local strong-motion data,we used the IDS(Iterative Deconvolution and Stacking)method to obtain the rupture process of the mainshock and two strong aftershocks on the 23rd.The rupture of the mainshock was mainly unilateral,lasting 31 s,with a maximum slip of 2m,and the depth of the large slip zone is about 41–49 km.There is a clear difference between the rupture depth of the main shock and the two strong aftershocks.The depths of the large slip zones of the latter two are 3–9 km and 8–10 km,respectively.There is also a significant difference in the seismogenic fault between the mainshock and the aftershocks,and we believe that there are two seismogenic fault zones in the study area,the deep and the shallow fault zone.The slip of the deep faults activates the shallow faults.展开更多
In general,the rapid growth of α-Fe clusters is a challenge in high Fe-content Fe-based amorphous alloys,negatively affecting their physical properties.Herein,we introduce an efficient and rapid post-treatment techni...In general,the rapid growth of α-Fe clusters is a challenge in high Fe-content Fe-based amorphous alloys,negatively affecting their physical properties.Herein,we introduce an efficient and rapid post-treatment technique known as ultrasonic vibration rapid processing(UVRP),which enables the formation of high-density strong magnetic α-Fe clusters,thereby enhancing the soft magnetic properties of Fe_(78)Si(13)B_(9) amorphous alloy ribbon.展开更多
Purpose–The precast concrete slab track(PST)has advantages of fewer maintenance frequencies,better smooth rides and structural stability,which has been widely applied in urban rail transit.Precise positioning of prec...Purpose–The precast concrete slab track(PST)has advantages of fewer maintenance frequencies,better smooth rides and structural stability,which has been widely applied in urban rail transit.Precise positioning of precast concrete slab(PCS)is vital for keeping the initial track regularity.However,the cast-in-place process of the self-compacting concrete(SCC)filling layer generally causes a large deformation of PCS due to the water-hammer effect of flowing SCC,even cracking of PCS.Currently,the buoyancy characteristic and influencing factors of PCS during the SCC casting process have not been thoroughly studied in urban rail transit.Design/methodology/approach–In this work,a Computational Fluid Dynamics(CFD)model is established to calculate the buoyancy of PCS caused by the flowing SCC.The main influencing factors,including the inlet speed and flowability of SCC,have been analyzed and discussed.A new structural optimization scheme has been proposed for PST to reduce the buoyancy caused by the flowing SCC.Findings–The simulation and field test results showed that the buoyancy and deformation of PCS decreased obviously after adopting the new scheme.Originality/value–The findings of this study can provide guidance for the control of the deformation of PCS during the SCC construction process.展开更多
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
NJmat is a user-friendly,data-driven machine learning interface designed for materials design and analysis.The platform integrates advanced computational techniques,including natural language processing(NLP),large lan...NJmat is a user-friendly,data-driven machine learning interface designed for materials design and analysis.The platform integrates advanced computational techniques,including natural language processing(NLP),large language models(LLM),machine learning potentials(MLP),and graph neural networks(GNN),to facili-tate materials discovery.The platform has been applied in diverse materials research areas,including perovskite surface design,catalyst discovery,battery materials screening,structural alloy design,and molecular informatics.By automating feature selection,predictive modeling,and result interpretation,NJmat accelerates the development of high-performance materials across energy storage,conversion,and structural applications.Additionally,NJmat serves as an educational tool,allowing students and researchers to apply machine learning techniques in materials science with minimal coding expertise.Through automated feature extraction,genetic algorithms,and interpretable machine learning models,NJmat simplifies the workflow for materials informatics,bridging the gap between AI and experimental materials research.The latest version(available at https://figshare.com/articles/software/NJmatML/24607893(accessed on 01 January 2025))enhances its functionality by incorporating NJmatNLP,a module leveraging language models like MatBERT and those based on Word2Vec to support materials prediction tasks.By utilizing clustering and cosine similarity analysis with UMAP visualization,NJmat enables intuitive exploration of materials datasets.While NJmat primarily focuses on structure-property relationships and the discovery of novel chemistries,it can also assist in optimizing processing conditions when relevant parameters are included in the training data.By providing an accessible,integrated environment for machine learning-driven materials discovery,NJmat aligns with the objectives of the Materials Genome Initiative and promotes broader adoption of AI techniques in materials science.展开更多
With the emergence of general foundational models,such as Chat Generative Pre-trained Transformer(ChatGPT),researchers have shown considerable interest in the potential applications of foundation models in the process...With the emergence of general foundational models,such as Chat Generative Pre-trained Transformer(ChatGPT),researchers have shown considerable interest in the potential applications of foundation models in the process industry.This paper provides a comprehensive overview of the challenges and opportunities presented by the use of foundation models in the process industry,including the frameworks,core applications,and future prospects.First,this paper proposes a framework for foundation models for the process industry.Second,it summarizes the key capabilities of industrial foundation models and their practical applications.Finally,it highlights future research directions and identifies unresolved open issues related to the use of foundation models in the process industry.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
基金the National Natural Science Foundation of China(No.51205018)the Fundamental Research Funds for the Central Universities of China(No.FRF-TP-14-121A2)the Research Project of State Key Laboratory of Mechanical System and Vibration(No.MSV-2014-09)
文摘Wind energy is one of the widely applied renewable energies in the world. Wind turbine as the main wind energy converter at present has very complex technical system containing a huge number of components,actuators and sensors. However, despite of the hardware redundancy, sensor faults have often affected the wind turbine normal operation and thus caused energy generation loss. In this paper, aiming at the wind turbine hydraulic pitch system, data-driven design of process monitoring(PM) and diagnosis has been realized in the wind turbine benchmark. Fault tolerant control(FTC) strategies focused on sensor faults have also been presented here, where with the implementation of soft sensor the sensor fault can be handled and the performance of the system is improved. The performance of this method is demonstrated with the wind turbine benchmark provided by Math Works.
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.
基金funded by CONAHCYT grant(252808)to GFCONAHCYT’s“Estancias Posdoctorales por México”program(662350)to HTB。
文摘Recent reports suggest that aging is not solely a physiological process in living beings;instead, it should be considered a pathological process or disease(Amorim et al., 2022). Consequently, this process involves a wide range of factors, spanning from genetic to environmental factors, and even includes the gut microbiome(GM)(Mayer et al., 2022). All these processes coincide at some point in the inflammatory process, oxidative stress, and apoptosis, at different degrees in various organs and systems that constitute a living organism(Mayer et al., 2022;AguilarHernández et al., 2023).
基金supported by the General Program of the National Natural Science Foundation of China(No.52274326)the China Baowu Low Carbon Metallurgy Innovation Foundation(No.BWLCF202109)the Seventh Batch of Ten Thousand Talents Plan of China(No.ZX20220553).
文摘Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.
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 National Natural Science Foundation of China (No.62173281,52377217,U23A20651)Sichuan Science and Technology Program (No.24NSFSC0024,23ZDYF0734,23NSFSC1436)+2 种基金Dazhou City School Cooperation Project (No.DZXQHZ006)Technopole Talent Summit Project (No.KJCRCFH08)Robert Gordon University。
文摘Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH estimation methods,offering valuable insights for the development of advanced battery management systems and embedded application research.
基金supported by the National Natural Science Foundation of China(51767017)the Basic Research Innovation Group Project of Gansu Province(18JR3RA133)the Industrial Support and Guidance Project of Universities in Gansu Province(2022CYZC-22).
文摘Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental factors.This cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.
基金supported by the National Key R&D Program of China(No.2022YFB3707303)the National Natural Science Foundation of China(No.52293471).
文摘The performance and corresponding applications of polymer nanocomposites are highly dominated by the choice of base material,type of fillers,and the processing ways.Carbon black-filled rubber composites(CRC)exemplify this,playing a crucial role in various industries.However,due to the complex interplay between these factors and the resulting properties,a simple yet accurate model to predict the mechanical properties of CRC,considering different rubbers,fillers,and processing techniques,is highly desired.This study aims to predict the dispersion of fillers in CRC and forecast the resultant mechanical properties of CRC by leveraging machine learning.We selected various rubbers and carbon black fillers,conducted mixing and vulcanizing,and subsequently measured filler dispersion and tensile performance.Based on 215 experimental data points,we evaluated the performance of different machine learning models.Our findings indicate that the manually designed deep neural network(DNN)models achieved superior results,exhibiting the highest coefficient of determination(R^(2))values(>0.95).Shapley additive explanations(SHAP)analysis of the DNN models revealed the intricate relationship between the properties of CRC and process parameters.Moreover,based on the robust predictive capabilities of the DNN models,we can recommend or optimize CRC fabrication process.This work provides valuable insights for employing machine learning in predicting polymer composite material properties and optimizing the fabrication of high-performance CRC.
基金Technology Development Program of Jilin Province(YDZJ202201ZYTS640)the National Key Research and Development Program of China(2022YFB4200400)funded by MOST+4 种基金the National Natural Science Foundation of China(52172048 and 52103221)Shandong Provincial Natural Science Foundation(ZR2021QB024 and ZR2021ZD06)Guangdong Basic and Applied Basic Research Foundation(2023A1515012323,2023A1515010943,and 2024A1515010023)the Qingdao New Energy Shandong Laboratory open Project(QNESL OP 202309)the Fundamental Research Funds of Shandong University.
文摘Recently published in Joule,Feng Liu and colleagues from Shanghai Jiaotong University reported a record-breaking 20.8%power conversion efficiency in organic solar cells(OSCs)with an interpenetrating fibril network active layer morphology,featuring a bulk p-in structure and proper vertical segregation achieved through additive-assisted layer-by-layer deposition.This optimized hierarchical gradient fibrillar morphology and optical management synergistically facilitates exciton diffusion,reduces recombination losses,and enhances light capture capability.This approach not only offers a solution to achieving high-efficiency devices but also demonstrates the potential for commercial applications of OSCs.
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supported by the National Natural Science Foundation of China(Nos.22206050 and 52270047).
文摘Fenton and Fenton-like processes,which could produce highly reactive species to degrade organic contaminants,have been widely used in the field of wastewater treatment.Therein,the chemistry of Fenton process including the nature of active oxidants,the complicated reactions involved,and the behind reason for its strongly pH-dependent performance,is the basis for the application of Fenton and Fenton-like processes in wastewater treatment.Nevertheless,the conflicting views still exist about the mechanism of the Fenton process.For instance,reaching a unanimous consensus on the nature of active oxidants(hydroxyl radical or tetravalent iron)in this process remains challenging.This review comprehensively examined the mechanism of the Fenton process including the debate on the nature of active oxidants,reactions involved in the Fenton process,and the behind reason for the pH-dependent degradation of contaminants in the Fenton process.Then,we summarized several strategies that promote the Fe(Ⅱ)/Fe(Ⅲ)cycle,reduce the competitive consumption of active oxidants by side reactions,and replace the Fenton reagent,thus improving the performance of the Fenton process.Furthermore,advances for the future were proposed including the demand for the high-accuracy identification of active oxidants and taking advantages of the characteristic of target contaminants during the degradation of contaminants by the Fenton process.
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.
基金sponsored by the Earthquake Spark Technology Project(XH23051B)。
文摘The Hualien M 7.3 earthquake on April 3,2024,was a significant and strong earthquake in Taiwan,China in the past two decades.The rupture process of the main shock and strong aftershocks is of great significance to the subsequent seismic activity and seismogenic tectonic research.Based on local strong-motion data,we used the IDS(Iterative Deconvolution and Stacking)method to obtain the rupture process of the mainshock and two strong aftershocks on the 23rd.The rupture of the mainshock was mainly unilateral,lasting 31 s,with a maximum slip of 2m,and the depth of the large slip zone is about 41–49 km.There is a clear difference between the rupture depth of the main shock and the two strong aftershocks.The depths of the large slip zones of the latter two are 3–9 km and 8–10 km,respectively.There is also a significant difference in the seismogenic fault between the mainshock and the aftershocks,and we believe that there are two seismogenic fault zones in the study area,the deep and the shallow fault zone.The slip of the deep faults activates the shallow faults.
基金supported by the Major Science and Technology Project of Zhongshan City(No.2022AJ004)the Key Basic and Applied Research Program of Guangdong Province(Nos.2019B030302010 and 2022B1515120082)Guangdong Science and Technology Innovation Project(No.2021TX06C111).
文摘In general,the rapid growth of α-Fe clusters is a challenge in high Fe-content Fe-based amorphous alloys,negatively affecting their physical properties.Herein,we introduce an efficient and rapid post-treatment technique known as ultrasonic vibration rapid processing(UVRP),which enables the formation of high-density strong magnetic α-Fe clusters,thereby enhancing the soft magnetic properties of Fe_(78)Si(13)B_(9) amorphous alloy ribbon.
文摘Purpose–The precast concrete slab track(PST)has advantages of fewer maintenance frequencies,better smooth rides and structural stability,which has been widely applied in urban rail transit.Precise positioning of precast concrete slab(PCS)is vital for keeping the initial track regularity.However,the cast-in-place process of the self-compacting concrete(SCC)filling layer generally causes a large deformation of PCS due to the water-hammer effect of flowing SCC,even cracking of PCS.Currently,the buoyancy characteristic and influencing factors of PCS during the SCC casting process have not been thoroughly studied in urban rail transit.Design/methodology/approach–In this work,a Computational Fluid Dynamics(CFD)model is established to calculate the buoyancy of PCS caused by the flowing SCC.The main influencing factors,including the inlet speed and flowability of SCC,have been analyzed and discussed.A new structural optimization scheme has been proposed for PST to reduce the buoyancy caused by the flowing SCC.Findings–The simulation and field test results showed that the buoyancy and deformation of PCS decreased obviously after adopting the new scheme.Originality/value–The findings of this study can provide guidance for the control of the deformation of PCS during the SCC construction process.
基金supported by the 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 Jiangsu Provincial Science and Technology Project Basic Research Program(Natural Science Foundation of Jiangsu Province)(No.BK20211283).
文摘NJmat is a user-friendly,data-driven machine learning interface designed for materials design and analysis.The platform integrates advanced computational techniques,including natural language processing(NLP),large language models(LLM),machine learning potentials(MLP),and graph neural networks(GNN),to facili-tate materials discovery.The platform has been applied in diverse materials research areas,including perovskite surface design,catalyst discovery,battery materials screening,structural alloy design,and molecular informatics.By automating feature selection,predictive modeling,and result interpretation,NJmat accelerates the development of high-performance materials across energy storage,conversion,and structural applications.Additionally,NJmat serves as an educational tool,allowing students and researchers to apply machine learning techniques in materials science with minimal coding expertise.Through automated feature extraction,genetic algorithms,and interpretable machine learning models,NJmat simplifies the workflow for materials informatics,bridging the gap between AI and experimental materials research.The latest version(available at https://figshare.com/articles/software/NJmatML/24607893(accessed on 01 January 2025))enhances its functionality by incorporating NJmatNLP,a module leveraging language models like MatBERT and those based on Word2Vec to support materials prediction tasks.By utilizing clustering and cosine similarity analysis with UMAP visualization,NJmat enables intuitive exploration of materials datasets.While NJmat primarily focuses on structure-property relationships and the discovery of novel chemistries,it can also assist in optimizing processing conditions when relevant parameters are included in the training data.By providing an accessible,integrated environment for machine learning-driven materials discovery,NJmat aligns with the objectives of the Materials Genome Initiative and promotes broader adoption of AI techniques in materials science.
基金supported by the National Natural Science Foundation of China(62225302,623B2014,and 62173023).
文摘With the emergence of general foundational models,such as Chat Generative Pre-trained Transformer(ChatGPT),researchers have shown considerable interest in the potential applications of foundation models in the process industry.This paper provides a comprehensive overview of the challenges and opportunities presented by the use of foundation models in the process industry,including the frameworks,core applications,and future prospects.First,this paper proposes a framework for foundation models for the process industry.Second,it summarizes the key capabilities of industrial foundation models and their practical applications.Finally,it highlights future research directions and identifies unresolved open issues related to the use of foundation models in the process industry.
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.
