Moisture content(MC)is an important quality metric of iron ore green pellets in pelletizing process in ironmaking industry.Current image-based methods for MC estimation may result in big errors for gray-scale pellet i...Moisture content(MC)is an important quality metric of iron ore green pellets in pelletizing process in ironmaking industry.Current image-based methods for MC estimation may result in big errors for gray-scale pellet images captured under various lighting conditions.We proposed a simple image-based method to improve the MC estimation accuracy by illumination correction and linear regression modeling.Firstly,the illumination of the pellet image was transformed into the reference illumination by use of a color checker chart and piecewise linear interpolation,so that the influence of different illuminations could be greatly reduced.By experimental analysis,it was found that MC is approximately adversely proportional to the average intensity of the transformed images.A simple model for MC prediction was then established by linear fitting.Experiments demonstrated that the proposed method has good robustness to different lighting conditions and achieves the best performance in metrics of mean square error,mean absolute error and maximum absolute error in comparison with five state-of-art MC estimating methods.Application on a working disk pelletizer shows that the proposed method can predict well the change of moisture content with time,and its computing efficiency can satisfy the requirement for online MC monitoring during the pelletizing process.展开更多
A procedure for evaluating the susceptibility of raw materials for the process of sintering of iron ore mixes is presented. The procedure relies on the evaluation of the amount and quality of the finest grain fraction...A procedure for evaluating the susceptibility of raw materials for the process of sintering of iron ore mixes is presented. The procedure relies on the evaluation of the amount and quality of the finest grain fraction. The method is based on determination of particular grain fractions. For the grain less than 0.15 mm, the determination of the a- mount is performed using an IPS （Infrared Particles Sizer） grain size analyzer and for the grain larger than 0.15 ram, the fraction is determined using the （wet and dry） screening methods. This allows for quantity assessment of the quality of material in terms of its susceptibility to self-pelletizing by calculating Total Ability for SelPPelletizing （TASP） index fT. The presented method, in combination with the grain size and chemical analyses, can serve for evaluation of suitability of raw material and mixes for the sintering process. Furthermore, the TASP index for 10 types of iron ores and concentrates was determined. The usability of the TASP index was verified by determination of its impact on yield of sintering process both in laboratory and in industry scale.展开更多
基金Financial supports from National Natural Science Foundation of China(Grant No.61973108)Natural Science Foundation of Fujian Province of China(Grant No.2022 J01528)Postgraduate Scientific Research Innovation Project of Hunan Province of China(CX20220397)are greatly appreciated.
文摘Moisture content(MC)is an important quality metric of iron ore green pellets in pelletizing process in ironmaking industry.Current image-based methods for MC estimation may result in big errors for gray-scale pellet images captured under various lighting conditions.We proposed a simple image-based method to improve the MC estimation accuracy by illumination correction and linear regression modeling.Firstly,the illumination of the pellet image was transformed into the reference illumination by use of a color checker chart and piecewise linear interpolation,so that the influence of different illuminations could be greatly reduced.By experimental analysis,it was found that MC is approximately adversely proportional to the average intensity of the transformed images.A simple model for MC prediction was then established by linear fitting.Experiments demonstrated that the proposed method has good robustness to different lighting conditions and achieves the best performance in metrics of mean square error,mean absolute error and maximum absolute error in comparison with five state-of-art MC estimating methods.Application on a working disk pelletizer shows that the proposed method can predict well the change of moisture content with time,and its computing efficiency can satisfy the requirement for online MC monitoring during the pelletizing process.
文摘A procedure for evaluating the susceptibility of raw materials for the process of sintering of iron ore mixes is presented. The procedure relies on the evaluation of the amount and quality of the finest grain fraction. The method is based on determination of particular grain fractions. For the grain less than 0.15 mm, the determination of the a- mount is performed using an IPS （Infrared Particles Sizer） grain size analyzer and for the grain larger than 0.15 ram, the fraction is determined using the （wet and dry） screening methods. This allows for quantity assessment of the quality of material in terms of its susceptibility to self-pelletizing by calculating Total Ability for SelPPelletizing （TASP） index fT. The presented method, in combination with the grain size and chemical analyses, can serve for evaluation of suitability of raw material and mixes for the sintering process. Furthermore, the TASP index for 10 types of iron ores and concentrates was determined. The usability of the TASP index was verified by determination of its impact on yield of sintering process both in laboratory and in industry scale.
