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PLS算法在激光诱导击穿光谱分析炉渣成分中的应用

Slag Quantitative Analysis Based on PLS Model by Laser-induced Breakdown Spectroscpy

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摘要

炉渣成分的实时在线检测是目前金属冶炼企业迫切需求的一项技术.本文利用激光诱导击穿光谱技术结合偏最小二乘回归模型对炉渣中的CaO、MgO、Al2O3和Fe进行定量分析.采用背景修正和基于等离子体成像强度的谱线归一化法对光谱进行预处理, 有效提高了光谱强度的准确性和稳定性.利用25块已知成分的炉渣样品建立偏最小二乘回归定量分析模型, 并用其预测另外5块样品成分.CaO、MgO、Al2O3和Fe预测结果的平均相对误差分别为4.7%、11.5%、17.9%和12.5%.实验结果表明, 激光诱导击穿光谱结合偏最小二乘回归方法可实现炉渣成分实时在线检测.

Abstract

On-line quantitative analysis of slag, which could greatly improve product quality and reduce energy consumption, is a highly demanded technique in metallurgic industry.Laser induced breakdown spectroscopy combined partial least squares regression model was proposed to determine the content of CaO, MgO, Al2O3 and Fe in slag.Background correction and spectral normalization, which used plasma intensity as reference signal, were applied to improve spectral signal stability.5 slag samples were analyzed by using the partial least squares regression model established with 25 slag elements-known samples.The mean prediction relative error of CaO, MgO, Al2O3 and Fe was 4.7%、11.5%、17.9% and 12.5%, respectively.The experimental results indicate that laser-induced breakdown spectroscopy combined PLS is a potential tool for on-line quantitative analysis of slag.

Newport宣传-MKS新实验室计划
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中图分类号:TN247

DOI:10.3788/gzxb20144309.0930002

基金项目:国家自然科学基金(No.11075184)资助

收稿日期:2014-01-22

修改稿日期:2014-02-28

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陈兴龙:合肥工业大学 仪器科学与光电工程学院, 合肥 230009中国科学院安徽光学精密机械研究所, 合肥 230031
董凤忠:中国科学院安徽光学精密机械研究所, 合肥 230031中国科学技术大学 环境科学与光电技术学院, 合肥 230026
王静鸽:中国科学院安徽光学精密机械研究所, 合肥 230031
倪志波:中国科学院安徽光学精密机械研究所, 合肥 230031
贺文干:中国科学院安徽光学精密机械研究所, 合肥 230031
付洪波:中国科学院安徽光学精密机械研究所, 合肥 230031
徐骏:上海卫星装备研究所, 上海 200240

联系人作者:陈兴龙(xlchen@mail.hfut.edu.cn)

备注:陈兴龙(1986-), 男, 博士研究生, 主要研究方向为光谱分析、光学检测技术.

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