{"title":"利用 X 射线荧光实时在线测定纸浆中钛铁矿品位的机理和数据驱动方法","authors":"","doi":"10.1016/j.mineng.2024.109002","DOIUrl":null,"url":null,"abstract":"<div><div>This article addresses the issues of low accuracy and time-consuming processes associated with traditional correction methods for matrix effect in the quantitative analysis of ilmenite grade by proposing a novel correction method based on a combination of mechanistic and data-driven approaches using X-ray fluorescence. By analyzing the modeling features of ilmenite grade, linear known terms and nonlinear unknown terms are identified. A comprehensive recognition of ilmenite grade is achieved by combining the least-squares method with a stochastic configuration network algorithm with block increments. Additionally, the Fisher information matrix is introduced to eliminate redundant nodes during the model establishment of block incremental, thereby reducing the network’s scale and achieving a lightweight model architecture. The practical industrial application demonstrates that the proposed method for estimating ilmenite grade achieves lower mean absolute error and root-mean-square error values, a coefficient of determination closer to 1, and a higher proportion of samples within an acceptable margin of error. These results are superior to other comparative algorithms, significantly enhancing the effectiveness of the ilmenite grade detection model.</div></div>","PeriodicalId":18594,"journal":{"name":"Minerals Engineering","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A mechanistic and data-driven approach for real-time online determination of ilmenite grade in pulp by X-ray fluorescence\",\"authors\":\"\",\"doi\":\"10.1016/j.mineng.2024.109002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This article addresses the issues of low accuracy and time-consuming processes associated with traditional correction methods for matrix effect in the quantitative analysis of ilmenite grade by proposing a novel correction method based on a combination of mechanistic and data-driven approaches using X-ray fluorescence. By analyzing the modeling features of ilmenite grade, linear known terms and nonlinear unknown terms are identified. A comprehensive recognition of ilmenite grade is achieved by combining the least-squares method with a stochastic configuration network algorithm with block increments. Additionally, the Fisher information matrix is introduced to eliminate redundant nodes during the model establishment of block incremental, thereby reducing the network’s scale and achieving a lightweight model architecture. The practical industrial application demonstrates that the proposed method for estimating ilmenite grade achieves lower mean absolute error and root-mean-square error values, a coefficient of determination closer to 1, and a higher proportion of samples within an acceptable margin of error. These results are superior to other comparative algorithms, significantly enhancing the effectiveness of the ilmenite grade detection model.</div></div>\",\"PeriodicalId\":18594,\"journal\":{\"name\":\"Minerals Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Minerals Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S089268752400431X\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Minerals Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S089268752400431X","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
摘要
本文针对钛铁矿品位定量分析中基体效应传统校正方法存在的精度低、过程耗时等问题,提出了一种基于机理和数据驱动相结合的 X 射线荧光校正新方法。通过分析钛铁矿品位的建模特征,确定了线性已知项和非线性未知项。通过将最小二乘法与分块增量随机配置网络算法相结合,实现了对钛铁矿品位的全面识别。此外,在分块递增的模型建立过程中,引入了费雪信息矩阵来消除冗余节点,从而减小了网络规模,实现了轻量级的模型架构。实际工业应用表明,所提出的钛铁矿品位估算方法获得了较低的平均绝对误差和均方根误差值,确定系数更接近于 1,并且在可接受误差范围内的样本比例更高。这些结果优于其他比较算法,大大提高了钛铁矿品位检测模型的有效性。
A mechanistic and data-driven approach for real-time online determination of ilmenite grade in pulp by X-ray fluorescence
This article addresses the issues of low accuracy and time-consuming processes associated with traditional correction methods for matrix effect in the quantitative analysis of ilmenite grade by proposing a novel correction method based on a combination of mechanistic and data-driven approaches using X-ray fluorescence. By analyzing the modeling features of ilmenite grade, linear known terms and nonlinear unknown terms are identified. A comprehensive recognition of ilmenite grade is achieved by combining the least-squares method with a stochastic configuration network algorithm with block increments. Additionally, the Fisher information matrix is introduced to eliminate redundant nodes during the model establishment of block incremental, thereby reducing the network’s scale and achieving a lightweight model architecture. The practical industrial application demonstrates that the proposed method for estimating ilmenite grade achieves lower mean absolute error and root-mean-square error values, a coefficient of determination closer to 1, and a higher proportion of samples within an acceptable margin of error. These results are superior to other comparative algorithms, significantly enhancing the effectiveness of the ilmenite grade detection model.
期刊介绍:
The purpose of the journal is to provide for the rapid publication of topical papers featuring the latest developments in the allied fields of mineral processing and extractive metallurgy. Its wide ranging coverage of research and practical (operating) topics includes physical separation methods, such as comminution, flotation concentration and dewatering, chemical methods such as bio-, hydro-, and electro-metallurgy, analytical techniques, process control, simulation and instrumentation, and mineralogical aspects of processing. Environmental issues, particularly those pertaining to sustainable development, will also be strongly covered.