Tonstein layers are found worldwide in the Permo-Carboniferous coal-bearing strata.This study investigates the geochronology,mineralogy,and geochemistry of four tonstein samples from the Permo-Carboniferous Benxi Form...Tonstein layers are found worldwide in the Permo-Carboniferous coal-bearing strata.This study investigates the geochronology,mineralogy,and geochemistry of four tonstein samples from the Permo-Carboniferous Benxi Formation,Ordos Basin,North China Craton(NCC).The typical features of the studied tonsteins include thin beds,lateral continuity,angular quartz grains,and euhedral zircons with similar U-Pb ages,indicating a significant pyroclastic origin.In addition,the tonstein samples have low TiO_(2)/Al_(2)O_(3)ratios(<0.02)and rare earth elements and yttrium(REY)concentrations with obvious negative Eu anomalies,indicating that the tonsteins have a felsic magma origin.Moreover,compared with the mean composition of clay shale,the studied tonsteins are characterized by high concentrations of the elements Nb and Ta,which may affect the concentration of the corresponding elements in surrounding coal seams.The zircon U-Pb ages of the tonsteins(293.9-298.8 Ma)provide a precise chronological framework on the Benxi Formation in the Ordos Basin,constraining the Gzhelian-Aselian stages.The tonsteins were probably sourced from arc volcanism along the western margin of the NCC during the early Permian,implying that the Alxa Terrane had not amalgamated with the NCC at that time.展开更多
锂离子电池荷电状态(State of Charge,SOC)直接影响着锂离子电池使用性能和效率。为了实现准确的SOC在线预测,提出一种粒子群优化最小二乘支持向量机软测量方法。该方法使用最小二乘支持向量机(Least Squares Support Vector Machine,LS...锂离子电池荷电状态(State of Charge,SOC)直接影响着锂离子电池使用性能和效率。为了实现准确的SOC在线预测,提出一种粒子群优化最小二乘支持向量机软测量方法。该方法使用最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)建立非线性系统模型,以锂离子电池工作电压、电流为输入量,电池SOC为输出量。建立软测量模型时,LSSVM正则化参数λ和径向基核宽度μ直接影响着模型的准确度,采用粒子群算法(Particle Swarm Optimization,PSO)对这两个关键参数进行优化。用型号为BTS6050C4的NBT电池测试系统进行样本数据采集,通过MATLAB仿真软件进行模型训练并校正。实验和仿真结果表明采用PSO-LSSVM优化算法精确度高、易实现,且在正常和过充工作环境下均可有效预测锂离子电池SOC。展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.41972170,42102127)Shandong Provincial Natural Science Foundation(Grant No.ZR2021QD087)+1 种基金Chinese Postdoctoral Science Foundation(Grant No.2021M702019)SDUST Research Fund(Grant No.2018TDJH101)。
文摘Tonstein layers are found worldwide in the Permo-Carboniferous coal-bearing strata.This study investigates the geochronology,mineralogy,and geochemistry of four tonstein samples from the Permo-Carboniferous Benxi Formation,Ordos Basin,North China Craton(NCC).The typical features of the studied tonsteins include thin beds,lateral continuity,angular quartz grains,and euhedral zircons with similar U-Pb ages,indicating a significant pyroclastic origin.In addition,the tonstein samples have low TiO_(2)/Al_(2)O_(3)ratios(<0.02)and rare earth elements and yttrium(REY)concentrations with obvious negative Eu anomalies,indicating that the tonsteins have a felsic magma origin.Moreover,compared with the mean composition of clay shale,the studied tonsteins are characterized by high concentrations of the elements Nb and Ta,which may affect the concentration of the corresponding elements in surrounding coal seams.The zircon U-Pb ages of the tonsteins(293.9-298.8 Ma)provide a precise chronological framework on the Benxi Formation in the Ordos Basin,constraining the Gzhelian-Aselian stages.The tonsteins were probably sourced from arc volcanism along the western margin of the NCC during the early Permian,implying that the Alxa Terrane had not amalgamated with the NCC at that time.
文摘锂离子电池荷电状态(State of Charge,SOC)直接影响着锂离子电池使用性能和效率。为了实现准确的SOC在线预测,提出一种粒子群优化最小二乘支持向量机软测量方法。该方法使用最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)建立非线性系统模型,以锂离子电池工作电压、电流为输入量,电池SOC为输出量。建立软测量模型时,LSSVM正则化参数λ和径向基核宽度μ直接影响着模型的准确度,采用粒子群算法(Particle Swarm Optimization,PSO)对这两个关键参数进行优化。用型号为BTS6050C4的NBT电池测试系统进行样本数据采集,通过MATLAB仿真软件进行模型训练并校正。实验和仿真结果表明采用PSO-LSSVM优化算法精确度高、易实现,且在正常和过充工作环境下均可有效预测锂离子电池SOC。
