Under the background of increasingly scarce ore worldwide and increasingly fierce market competition,developing the mining industry could be strongly restricted.Intelligent ore sorting equipment not only improves ore ...Under the background of increasingly scarce ore worldwide and increasingly fierce market competition,developing the mining industry could be strongly restricted.Intelligent ore sorting equipment not only improves ore use and enhances the economic benefits of enterprises but also increases the ore grade and lessens the grinding cost and tailings production.However,long-term research on intelligent ore sorting equipment found that the factors affecting sorting efficiency mainly include ore information identification technology,equipment sorting actuator,and information processing algorithm.The high precision,strong anti-interference capability,and high speed of these factors guarantee the separation efficiency of intelligent ore sorting equipment.Color ore sorter,X-ray ore transmission sorter,dual-energy X-ray transmission ore sorter,X-ray fluorescence ore sorter,and near-infrared ore sorter have been successfully developed in accordance with the different characteristics of minerals while ensuring the accuracy of equipment sorting and improving the equipment sorting efficiency.With the continuous improvement of mine automation level,the application of online element rapid analysis technology with high speed,high precision,and strong anti-interference capability in intelligent ore sorting equipment will become an inevitable trend of equipment development in the future.Laser-induced breakdown spectroscopy,transientγneutron activation analysis,online Fourier transform infrared spectroscopy,and nuclear magnetic resonance techniques will promote the development of ore sorting equipment.In addition,the improvement and joint application of additional high-speed and high-precision operation algorithms(such as peak area,principal component analysis,artificial neural network,partial least squares,and Monte Carlo library least squares methods)are an essential part of the development of intelligent ore sorting equipment in the future.展开更多
X-ray fluorescence(XRF)sensor-based ore sorting enables efficient beneficiation of heterogeneous ores,while intraparticle heterogeneity can cause significant grade detection errors,leading to misclassifications and hi...X-ray fluorescence(XRF)sensor-based ore sorting enables efficient beneficiation of heterogeneous ores,while intraparticle heterogeneity can cause significant grade detection errors,leading to misclassifications and hindering widespread technology adoption.Accurate classification models are crucial to determine if actual grade exceeds the sorting threshold using localized XRF signals.Previous studies mainly used linear regression(LR)algorithms including simple linear regression(SLR),multivariable linear regression(MLR),and multivariable linear regression with interaction(MLRI)but often fell short attaining satisfactory results.This study employed the particle swarm optimization support vector machine(PSO-SVM)algorithm for sorting porphyritic copper ore pebble.Lab-scale results showed PSO-SVM out-performed LR and raw data(RD)models and the significant interaction effects among input features was observed.Despite poor input data quality,PSO-SVM demonstrated exceptional capabilities.Lab-scale sorting achieved 93.0%accuracy,0.24%grade increase,84.94%recovery rate,57.02%discard rate,and a remarkable 39.62 yuan/t net smelter return(NSR)increase compared to no sorting.These improvements were achieved by the PSO-SVM model with optimized input combinations and highest data quality(T=10,T is XRF testing times).The unsuitability of LR methods for XRF sensor-based sorting of investigated sample is illustrated.Input element selection and mineral association analysis elucidate element importance and influence mechanisms.展开更多
The breeding and large-scale application of hybrid rice contribute significantly to the food supply worldwide.Currently,hybrid seed production uses cytoplasmic male sterile(CMS)lines or photoperiod/thermo-sensitive ge...The breeding and large-scale application of hybrid rice contribute significantly to the food supply worldwide.Currently,hybrid seed production uses cytoplasmic male sterile(CMS)lines or photoperiod/thermo-sensitive genic male sterile(PTGMS)lines as female parent.Despite huge successes,both systems have intrinsic problems.CMS systems are mainly restricted by the narrow restorer resources that make it difficult to breed superior hybrids,while PTGMS systems are limited by conditional sterility of the male sterile lines that makes the propagation of both PTGMS seeds and hybrid seeds vulnerable to unpredictable climate changes.Recessive nuclear male sterile(NMS)lines insensitive to environmental conditions are widely distributed and are ideal for hybrid rice breeding and production,but the lack of effective ways to propagate the pure NMS lines in a large scale renders it impossible to use them for hybrid rice production.The development of"the third-generation hybrid rice technology"enables efficient propagation of the pure NMS lines in commercial scale.This paper discusses the establishment of"the thirdgeneration hybrid rice technology"and further innovations.This new technology breaks the limitations of CMS and PTGMS systems and will bring a big leap forward in hybrid rice production.展开更多
基金supported by the National Science and Technology Support Program of China(No.2012BAC11B07)the Jiangxi Science and Technology Innovation Base Plan(No.20212BCD42017)。
文摘Under the background of increasingly scarce ore worldwide and increasingly fierce market competition,developing the mining industry could be strongly restricted.Intelligent ore sorting equipment not only improves ore use and enhances the economic benefits of enterprises but also increases the ore grade and lessens the grinding cost and tailings production.However,long-term research on intelligent ore sorting equipment found that the factors affecting sorting efficiency mainly include ore information identification technology,equipment sorting actuator,and information processing algorithm.The high precision,strong anti-interference capability,and high speed of these factors guarantee the separation efficiency of intelligent ore sorting equipment.Color ore sorter,X-ray ore transmission sorter,dual-energy X-ray transmission ore sorter,X-ray fluorescence ore sorter,and near-infrared ore sorter have been successfully developed in accordance with the different characteristics of minerals while ensuring the accuracy of equipment sorting and improving the equipment sorting efficiency.With the continuous improvement of mine automation level,the application of online element rapid analysis technology with high speed,high precision,and strong anti-interference capability in intelligent ore sorting equipment will become an inevitable trend of equipment development in the future.Laser-induced breakdown spectroscopy,transientγneutron activation analysis,online Fourier transform infrared spectroscopy,and nuclear magnetic resonance techniques will promote the development of ore sorting equipment.In addition,the improvement and joint application of additional high-speed and high-precision operation algorithms(such as peak area,principal component analysis,artificial neural network,partial least squares,and Monte Carlo library least squares methods)are an essential part of the development of intelligent ore sorting equipment in the future.
基金supported by State Key Laboratory of Mineral Processing (No.BGRIMM-KJSKL-2022-16)China Postdoctoral Science Foundation (No.2021M700387)+1 种基金National Natural Science Foundation of China (No.G2021105015L)Ministry of Science and Technology of the People’s Republic of China (No.2022YFC2904502)。
文摘X-ray fluorescence(XRF)sensor-based ore sorting enables efficient beneficiation of heterogeneous ores,while intraparticle heterogeneity can cause significant grade detection errors,leading to misclassifications and hindering widespread technology adoption.Accurate classification models are crucial to determine if actual grade exceeds the sorting threshold using localized XRF signals.Previous studies mainly used linear regression(LR)algorithms including simple linear regression(SLR),multivariable linear regression(MLR),and multivariable linear regression with interaction(MLRI)but often fell short attaining satisfactory results.This study employed the particle swarm optimization support vector machine(PSO-SVM)algorithm for sorting porphyritic copper ore pebble.Lab-scale results showed PSO-SVM out-performed LR and raw data(RD)models and the significant interaction effects among input features was observed.Despite poor input data quality,PSO-SVM demonstrated exceptional capabilities.Lab-scale sorting achieved 93.0%accuracy,0.24%grade increase,84.94%recovery rate,57.02%discard rate,and a remarkable 39.62 yuan/t net smelter return(NSR)increase compared to no sorting.These improvements were achieved by the PSO-SVM model with optimized input combinations and highest data quality(T=10,T is XRF testing times).The unsuitability of LR methods for XRF sensor-based sorting of investigated sample is illustrated.Input element selection and mineral association analysis elucidate element importance and influence mechanisms.
基金supported by the National Natural Science Foundation of China(U1901203)Natural Science Foundation of Guangdong Province(2018B030308008 and 2019A1515110671)+2 种基金Major Program of Guangdong Basic and Applied Research(2019B030302006)Shenzhen Commission on Innovation and Technology Programs(JCYJ20180507181837997)China Postdoctoral Science Foundation(2019M662957)。
文摘The breeding and large-scale application of hybrid rice contribute significantly to the food supply worldwide.Currently,hybrid seed production uses cytoplasmic male sterile(CMS)lines or photoperiod/thermo-sensitive genic male sterile(PTGMS)lines as female parent.Despite huge successes,both systems have intrinsic problems.CMS systems are mainly restricted by the narrow restorer resources that make it difficult to breed superior hybrids,while PTGMS systems are limited by conditional sterility of the male sterile lines that makes the propagation of both PTGMS seeds and hybrid seeds vulnerable to unpredictable climate changes.Recessive nuclear male sterile(NMS)lines insensitive to environmental conditions are widely distributed and are ideal for hybrid rice breeding and production,but the lack of effective ways to propagate the pure NMS lines in a large scale renders it impossible to use them for hybrid rice production.The development of"the third-generation hybrid rice technology"enables efficient propagation of the pure NMS lines in commercial scale.This paper discusses the establishment of"the thirdgeneration hybrid rice technology"and further innovations.This new technology breaks the limitations of CMS and PTGMS systems and will bring a big leap forward in hybrid rice production.
