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Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

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International Journal of Minerals,Metallurgy and Materials 2024年第3期31卷 508-517页

作者： Runhao Zhang Jian Yang Han Sun Wenkui Yang State Key Laboratory of Advanced Special Steel School of Materials Science and EngineeringShanghai UniversityShanghai 200444China

The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.

关键词： basic oxygen furnace steelmaking machine learning lime utilization ratio dephosphorization online sequential extreme learning machine forgetting mechanism

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Ionic liquid-based electrolytes for CO2 electroreduction and CO2 electroorganic transformation

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National Science Review 2022年第4期9卷 168-185页

作者： Xingxing Tan Xiaofu sun Buxing han Beijing National Laboratory for Molecular Sciences CAS Key Laboratory of Colloid and Interface and ThermodynamicsCAS Research/Education Center for Excellence in Molecular SciencesInstitute of ChemistryChinese Academy of Sciences School of Chemistry and Chemical Engineering University of Chinese Academy of Sciences Shanghai Key Laboratory of Green Chemistry and Chemical Processes School of Chemistry and Molecular EngineeringEast China Normal University

CO2is an abundant and renewable C1 feedstock.Electrochemical transformation of CO2can integrate CO2fixation with renewable electricity storage,providing an avenue to close the anthropogenic carbon cycle.As a new type of green and chemically tailor able solvent,ionic liquids(ILs) have been proposed as highly promising alternatives for conventional electrolytes in electrochemical CO2conversion.This review summarizes major advances in the electrochemical transformation of CO2into value-added carbonic fuels and chemicals in IL-based media in the past several years.Both the direct CO2electroreduction(CO2ER)and CO2-involved electro organic transformation(CO2EOT) are discussed,focusing on the effect of electrocatalysts,IL components,reactor configurations and operating conditions on catalytic activity,selectivity and reusability.The reasons for the enhanced CO2conversion performance by ILs are also discusse d,providing guidance for the rational design of novel IL-based electro chemical processes for CO2conversion.Finally,the critical challenges remaining in this research area and promising directions for future research are proposed.

关键词： ionic liquid carbon dioxide electrocatalysis green synthesis value-added fuels and chemicals

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High leaf area index expands the contrasting effect of climate warming on Western Siberia taiga forests activity before and after 2000

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Journal of Geographical Sciences 2024年第1期34卷 131-145页

作者： sun han WANG Xiangping State Key Laboratory of Efficient Production of Forest Resources Beijing Forestry UniversityBeijing 100083China School of Ecology and Nature Conservation Beijing Forestry UniversityBeijing 100083China

The taiga vegetation in Western Siberia has been seriously threatened by climate warming in recent decades.However,how vegetation in different growing states and climate conditions responds to climate changes differently is still unclear.Here we explore the vegetation activity trends in Western Siberia taiga forests using the annual rate of change in leaf area index(LAI)during 1982–2018 so as to answer two questions:(1)how did climate warming affect taiga vegetation activity in the recent last decades?(2)Did the growing state of taiga forest affect its response to climate warming?Our results revealed that climate warming promoted taiga vegetation activity in Western Siberia before 2000.However,continuous warming caused excessive evapotranspiration and led to decreased vegetation activity after 2000.Moreover,the intensity of vegetation growth response to warming was positively related to canopy height and LAI,indicating that both the positive and negative effects of warming were more significant in taiga forests in better growing state.Since these forests generally have higher productivity and play more important roles in ecosystem functioning(e.g.,carbon sink and biodiversity conservation),our results highlight their vulnerability to future climate change that need more research attention.

关键词： climate warming taiga forest leaf area index(LAI) vegetation activity Western Siberia

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Effects of climate,biotic factors,and phylogeny on allometric relationships:testing the metabolic scaling theory in plantations and natural forests across China

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Forest Ecosystems 2020年第4期7卷 672-685页

作者： Han Sun Xiangping Wang Dayong Fan School of Ecology and Nature Conservation Beijing Forest UniversityBeijingChina College of Forestry Beijing Forest UniversityBeijingChina

Background:Metabolic scaling theory(MST)is still in debate because observed allometric exponents often deviate from MST predictions,and can change significantly depending on environment,phylogeny,and disturbance.We assembled published scaling exponents from literatures for three allometric relationships linked to biomass allocation:leaf biomassdiameter(LD),stem biomassdiameter(SD),and root biomassdiameter(RD).We used data from natural forests and plantations across China to test the following hypotheses:1)the allometric relationships of trees support the predictions of MST on a broad scale;2)the observed deviations from MST predictions are caused by climate,biotic factors,and/or phylogeny;3)abiotic and biotic factors influence allometric relationships in plantations and natural forests differently,and different allometric relationships(i.e.LD,SD,and RD)are affected differently.We related these scaling exponents to geographic climate gradient,successional stage,stand density,leaf form and phenology,and phylogeny.We used mixedeffect models to examine the major factors affecting tree allometries.Results:In natural forests,SD and RD scaling exponents were consistent with MST predictions in primary forests,but were significantly lower in secondary forests.Both SD and RD scaling exponents in plantations had a medium value that fell between those of the secondary and primary forests,despite plantations being similar in species characteristics and age to secondary forests.The SD and RD exponents were significantly affected by factors that are not yet considered in MST,including winter coldness which explained 2.76%–3.24%of variations,successional stage(7.91%–8.20%of variations),density(a surrogate for competition,5.86%–8.54%of variations),and especially phylogeny(45.86%–56.64%of variations explained).However,the LD scaling exponents conformed to MST predictions in primary,secondary,and plantation forests,and was not strongly explained by most factors.Conclusion:MST is only applicable to primary(steadystate)forests,and cl

关键词： Allometry Successional stage Climate Plantation Conifer Broadleaf trees Metabolic scaling theory

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Strategic Investment in Transmission and Energy Storage in Electricity Markets

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 179-191页

作者： Kunpeng Tian Weiqing sun Dong han Department of Control Science and Engineering University of Shanghai for Science and TechnologyShanghaiChina Department of Electrical Engineering University of Shanghai for Science and TechnologyShanghaiChina

The variability of renewable energy and transmission congestion provide opportunities for arbitrage by merchants in deregulated electricity markets.Merchants strategically invest to maximize their profits.This paper proposes a joint investment framework for renewable energy,transmission lines,and energy storage using the Stackelberg game model.At the upper level,merchants implement investment and operation strategies for deregulated transmission and energy storage to maximize profits.At the middle level,central planners seek to maximize social welfare through investments in centralized renewable energy and energy storage.At the lower level,independent system operators jointly optimize the energy and reserve markets to minimize the total operating costs.Merchants are remunerated through financial rights,which are a settlement method based on locational marginal price.The trilevel optimization problem is reformulated as a tractable single-level one using Karush-Kuhn-Tucker(KKT)conditions and strong duality theory.The interaction between merchants and central planners is studied with an example based on the IEEE 30-bus test system.The assignment of weight coefficients to the corresponding stochastic scenarios can help merchants avoid investment risk,and their effectiveness is verified with the IEEE 118-bus test system.

关键词： Strategic investment Stackelberg game model financial right locational marginal price merchant central planner

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Straight-Path Following and Formation Control of USVs Using Distributed Deep Reinforcement Learning and Adaptive Neural Network

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 572-574页

作者： Zhengqing han Yintao Wang Qi sun the School of Marine Science and Technology Northwestern Polytechnical UniversityXi’an 710072China

Dear Editor,This letter presents a distributed deep reinforcement learning(DRL) based approach to deal with the path following and formation control problems for underactuated unmanned surface vehicles(USVs). By constructing two independent actor-critic architectures,the deep deterministic policy gradient(DDPG) method is proposed to determine the desired heading and speed command for each USV.

关键词： Deep policy heading

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Prediction Model of Kinetic Energy Conversion of Tandem Dual-Oscillator Based on Flow-Induced Vibration Experiment

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China Ocean Engineering 2022年第5期36卷 707-719页

作者： BAI Xu sun Meng han Chuan-yu sun Hai School of Naval Architecture&Ocean Engineering Jiangsu University of Science and TechnologyZhenjiang 212003China Marine Renewable Energy Laboratory Department of Naval Architecture&Marine EngineeringUniversity of MichiganAnn Arbor MI 48109-2145USA College of Aerospace and Civil Engineering Harbin Engineering UniversityHarbin 150001China

Flow-induced vibration energy harvesting devices typically use an elastically supported body immersed in an oncoming flow to convert the sea and river current's hydrokinetic energy into electrical energy.The proportion of energy the device collects is greatly influenced by parameters such as the water flow velocity,spacing between device components,structure size,and damping coefficient.For parameter optimization and performance predictions of flow-induced vibration energy harvesting devices,we train a model of the power harvesting efficiency under different damping ratios,stiffnesses,spacing ratios,and reduced velocities based on experimental data.To improve the prediction accuracy,a feedforward network structure is optimized by using the topological evolutionary algorithm and a radial basis function network.Comparative analysis reveals that the radial basis function network model provides the best agreement with the experimental results and realizes accurate predictions of the power harvested by a dual-oscillator system in the vortex-induced vibration,transition region,and galloping.The prediction results show that the model's maximum power harvesting efficiency occurs in the vortex-induced vibration.The efficiency increases and then decreases with increasing stiffness and reduced velocity in this phase;an increase in the spacing ratio causes the effi-ciency to decrease and then increase;finally,increasing the damping ratio enhances the efficiency.The device achieves maximum power harvesting efficiency at a reduced velocity of U_(r)=4.11.The proposed model effectively predicts the maximum efficiency and the corresponding damping ratio and stiffness of the vortex-induced vibration and galloping,providing a new method for predicting tandem dual-oscillator hydrodynamic power conversion in flow-induced vibration.

关键词： flow-induced vibration tandem double-oscillator radial basis function prediction

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Univariate imputation method for recovering missing data in wastewater treatment process

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Chinese Journal of Chemical Engineering 2023年第1期53卷 201-210页

作者： Honggui han Meiting sun Huayun han Xiaolong Wu Junfei Qiao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China

High-quality data play a paramount role in monitoring,control,and prediction of wastewater treatment process(WWTP)and can effectively ensure the efficient and stable operation of system.Missing values seriously degrade the accuracy,reliability and completeness of the data quality due to network collapses,connection errors and data transformation failures.In these cases,it is infeasible to recover missing data depending on the correlation with other variables.To tackle this issue,a univariate imputation method(UIM)is proposed for WWTP integrating decomposition method and imputation algorithms.First,the seasonal-trend decomposition based on loess method is utilized to decompose the original time series into the seasonal,trend and remainder components to deal with the nonstationary characteristics of WWTP data.Second,the support vector regression is used to approximate the nonlinearity of the trend and remainder components respectively to provide estimates of its missing values.A self-similarity decomposition is conducted to fill the seasonal component based on its periodic pattern.Third,all the imputed results are merged to obtain the imputation result.Finally,six time series of WWTP are used to evaluate the imputation performance of the proposed UIM by comparing with existing seven methods based on two indicators.The experimental results illustrate that the proposed UIM is effective for WWTP time series under different missing ratios.Therefore,the proposed UIM is a promising method to impute WWTP time series.

关键词： Univariate Self-similarity Waste water Algorithm Integration

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Hierarchical Spinning of Janus Textiles with Anisotropic Wettability for Wound Healing

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Research 2024年第1期2023卷 97-105页

作者： han Zhang Lingyu sun Jiahui Guo Yuanjin Zhao Department of Rheumatology and Immunology Nanjing Drum Tower HospitalSchool of Biological Science and Medical EngineeringSoutheast UniversityNanjingJiangsu 210096China Oujiang Laboratory(Zhejiang Lab for Regenerative Medicine Vision and Brain Health)Wenzhou InstituteUniversity of Chinese Academy of SciencesWenzhouZhejiang 325001China

Wound healing and tissue repair are recognized as basic human health problems worldwide.Attempts to accelerate the reparative process are focused on developing functional wound dressings.Herein,we present novel Janus textiles with anisotropic wettability from hierarchical microfluidic spinning for wound healing.The hydrophilic hydrogel microfibers from microfluidics are woven into textiles for freeze-drying treatment,followed by the deposition of electrostatic spinning nanofibers composed of hydrophobic polylactic acid(PLA)and silver nanoparticles.The electrospun nanofiber layer can be well coupled with the hydrogel microfiber layer to generate Janus textiles with anisotropic wettability due to the roughness of the hydrogel textile surface and the incomplete evaporation of PLA solution when reaching the surface.For wound treatment with the hydrophobic PLA side contacting the wound surface,the wound exudate can be pumped from the hydrophobic to the hydrophilic side based on the wettability differential derived drainage force.During this process,the hydrophobic side of the Janus textile can prevent excess fluid from infiltrating the wound again,preventing excessive moisture and preserving the breathability of the wound.In addition,the silver nanoparticles contained in the hydrophobic nanofibers could impart the textiles with good antibacterial effect,which further promote the wound healing efficiency.These features indicate that the described Janus fiber textile has great application potential in the field of wound treatment.

关键词： silver spinning healing

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The Life Cycle of Knowledge in Big Language Models:A Survey

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Machine Intelligence Research 2024年第2期21卷 217-238页

作者： Boxi Cao Hongyu Lin Xianpei han Le sun Chinese Information Processing Laboratory Beijing 100190China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101408China

Knowledge plays a critical role in artificial intelligence.Recently,the extensive success of pre-trained language models(PLMs)has raised significant attention about how knowledge can be acquired,maintained,updated and used by language models.Despite the enormous amount of related studies,there is still a lack of a unified view of how knowledge circulates within language models throughout the learning,tuning,and application processes,which may prevent us from further understanding the connections between current progress or realizing existing limitations.In this survey,we revisit PLMs as knowledge-based systems by dividing the life circle of knowledge in PLMs into five critical periods,and investigating how knowledge circulates when it is built,maintained and used.To this end,we systematically review existing studies of each period of the knowledge life cycle,summarize the main challenges and current limitations,and discuss future directions.

关键词： Pre-trained language model knowledge acquisition knowledge representation knowledge probing knowledge editing knowledge application

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