Determination of chemical elements assay plays an important role in mineral processing operations.This factor is used to control process accuracy,recovery calculation and plant profitability.The new assaying methods i...Determination of chemical elements assay plays an important role in mineral processing operations.This factor is used to control process accuracy,recovery calculation and plant profitability.The new assaying methods including chemical methods,X-ray fluorescence and atomic absorption spectrometry are advanced and accurate.However,in some applications,such as on-line assaying process,high accuracy is required.In this paper,an algorithm based on Kalman Filter is presented to predict on-line XRF errors.This research has been carried out on the basis of based the industrial real data collection for evaluating the performance of the presented algorithm.The measurements and analysis for this study were conducted at the Sarcheshmeh Copper Concentrator Plant located in Iran.The quality of the obtained results was very satisfied;so that the RMS errors of prediction obtained for Cu and Mo grade assaying errors in rougher feed were less than 0.039 and 0.002 and in final flotation concentration less than 0.58 and 0.074,respectively.The results indicate that the mentioned method is quite accurate to reduce the on-line XRF errors measurement.展开更多
Frozen soils or those with permafrost cover large areas of the earth's surface and support unique vegetative ecosystems. Plants growing in such harsh conditions have adapted to small niches, which allow them to su...Frozen soils or those with permafrost cover large areas of the earth's surface and support unique vegetative ecosystems. Plants growing in such harsh conditions have adapted to small niches, which allow them to survive. In northern Alaska, USA, both moist acidic and non-acidic tundra occur, yet determination of frozen soil p Hs currently requires thawing of the soil so that electrometric pH methods can be utilized. Contrariwise, a portable X-ray fluorescence(PXRF) spectrometer was used in this study to assess elemental abundances and relate those characteristics to soil pH through predictive multiple linear regressions. Two operational modes, Soil Mode and Geochem Mode, were utilized to scan frozen soils in-situ and under laboratory conditions, respectively, after soil samples were dried and ground. Results showed that lab scanning produced optimal results with adjusted coefficient of determination(R^2) of 0.88 and 0.33 and root mean squared errors(RMSEs) of 0.87 and 0.34 between elemental data and lab-determined pH for Soil Mode and Geochem Mode, respectively. Even though the presence of ice attenuated fluoresced radiation under field conditions, adjusted R^2 and RMSEs between the datasets still provided reasonable model generalization(e.g., 0.73 and 0.49 for field Geochem Mode). Principal component analysis qualitatively separated multiple sampling sites based on elemental data provided by PXRF, reflecting differences in the chemical composition of the soils studied. Summarily, PXRF can be used for in-situ determination of soil pH in arctic environments without the need for sample modification and thawing. Furthermore, use of PXRF for determination of soil pH may provide higher sample throughput than traditional eletrometric-based methods, while generating elemental data useful for the prediction of multiple soil parameters.展开更多
基金the support of the Department of Research and Development of Sarcheshmeh Copper Plants for this research
文摘Determination of chemical elements assay plays an important role in mineral processing operations.This factor is used to control process accuracy,recovery calculation and plant profitability.The new assaying methods including chemical methods,X-ray fluorescence and atomic absorption spectrometry are advanced and accurate.However,in some applications,such as on-line assaying process,high accuracy is required.In this paper,an algorithm based on Kalman Filter is presented to predict on-line XRF errors.This research has been carried out on the basis of based the industrial real data collection for evaluating the performance of the presented algorithm.The measurements and analysis for this study were conducted at the Sarcheshmeh Copper Concentrator Plant located in Iran.The quality of the obtained results was very satisfied;so that the RMS errors of prediction obtained for Cu and Mo grade assaying errors in rougher feed were less than 0.039 and 0.002 and in final flotation concentration less than 0.58 and 0.074,respectively.The results indicate that the mentioned method is quite accurate to reduce the on-line XRF errors measurement.
文摘Frozen soils or those with permafrost cover large areas of the earth's surface and support unique vegetative ecosystems. Plants growing in such harsh conditions have adapted to small niches, which allow them to survive. In northern Alaska, USA, both moist acidic and non-acidic tundra occur, yet determination of frozen soil p Hs currently requires thawing of the soil so that electrometric pH methods can be utilized. Contrariwise, a portable X-ray fluorescence(PXRF) spectrometer was used in this study to assess elemental abundances and relate those characteristics to soil pH through predictive multiple linear regressions. Two operational modes, Soil Mode and Geochem Mode, were utilized to scan frozen soils in-situ and under laboratory conditions, respectively, after soil samples were dried and ground. Results showed that lab scanning produced optimal results with adjusted coefficient of determination(R^2) of 0.88 and 0.33 and root mean squared errors(RMSEs) of 0.87 and 0.34 between elemental data and lab-determined pH for Soil Mode and Geochem Mode, respectively. Even though the presence of ice attenuated fluoresced radiation under field conditions, adjusted R^2 and RMSEs between the datasets still provided reasonable model generalization(e.g., 0.73 and 0.49 for field Geochem Mode). Principal component analysis qualitatively separated multiple sampling sites based on elemental data provided by PXRF, reflecting differences in the chemical composition of the soils studied. Summarily, PXRF can be used for in-situ determination of soil pH in arctic environments without the need for sample modification and thawing. Furthermore, use of PXRF for determination of soil pH may provide higher sample throughput than traditional eletrometric-based methods, while generating elemental data useful for the prediction of multiple soil parameters.
