摘要
在实现煤炭高效利用过程中,煤矸分选是一个非常重要的步骤,但现有的煤矸分选技术存在资源浪费,效率较低等问题。可见-近红外光谱识别技术具有快速可靠的优点,在煤矸识别领域已有一定的研究基础,但大多数研究并未结合实际工况进行有效分析。首先,在实验室中搭建可见-近红外光谱采集装置,模拟实际环境下不同探测角度(0°、10°、20°、30°)、探测距离(10、15、20、25 cm)、光照角度(15°、25°、35°、45°)三种工况,并分别在单因素条件以及正交试验设计的多因素条件下,采集山西西铭煤矿的煤和矸石样本在可见-近红外波段的光谱数据。其次,对采集的光谱数据进行分析,并先后经过标准正态变量变换和Savitzky-Golay卷积平滑,以减少噪音和误差对数据的影响。最后,在单因素试验中,结合预处理算法并基于决策树(DT)、K近邻(KNN)、偏最小二乘判别分析(PLS-DA)、支持向量机(SVM)、AdaBoost五种机器学习模型对光谱数据进行训练。单因素试验结果表明,AdaBoost算法具有较强的学习能力,在不同工况下对煤和矸石的识别准确率均为100%,优于其他识别模型。在正交试验中,支持向量机(SVM)作为识别模型进行训练,结果表明,在原始数据和预处理后的数据中,三种工况对煤矸识别准确率的影响程度不同,影响次序从大到小为不同光照角度、探测距离、探测角度。同时,对比实验结果可以得出,选用合适的预处理和建模方法可以降低不同工况对识别准确率的影响。预处理后的数据中,最优的工况组合为探测角度0°、探测距离20、光照角度35°。随机选取一组条件与最优组进行三次重复对照试验,结果表明最优组的识别表现优于随机对照组。研究结果对煤矸识别最优工况条件的寻找具有借鉴意义,并为可见-近红外光谱技术在煤矸识别领域的实际应用提供了理论基础。
In the process of realizing the efficient utilization of coal,coal and gangue separation is a very important step,but the existing coal and gangue separation technology has the problems of resource waste and low efficiency.It can be seen that Vis-NIR(visible near-infrared)spectroscopy identification technology has the advantages of being fast and reliable and has a certain research foundation in the field of coal and gangue recognition,but most of the studies have not been effectively analyzed in combination with actual conditions.Firstly,this paper set up a Vis-NIR spectrum acquisition device in the laboratory to simulate three conditions in the actual environment:different detection angles(0°,10°,20°,30°),different detection distances(10,15,20 and 25 cm),and different illumination angles(15°,25°,35°,45°).The spectral data of coal and gangue samples from Ximing Coal Mine in Shanxi are collected in the Vis-NIR spectrum band under the single-factorandmulti-factor conditions of orthogonal experimental design.Secondly,the collected spectral data were analyzed and successively underwent standard normal variable transformation and Savitzky-Golay convolution smoothing to reduce the impact of noise and error on the data.Finally,in the single factor experiment,the spectral data were trained based on five machine learning models,including decision tree(DT),k-nearest neighbor(KNN),partial least squares discriminant analysis(PLS-DA),support vector machine(SVM)and AdaBoost,combined with the preprocessing algorithm.The results of the single factor experiment show that the AdaBoost algorithm adopted in this paper has strong learning ability,and the recognition accuracy of coal and gangue under different working conditions is 100%,which is better than other recognition models.In orthogonal experiments,a support vector machine(SVM)is used as the recognition model for training.The results show that,in the rawand preprocessed data,the three conditions have different degrees of influence on the recognition accuracy of coal
作者
刘涛
李博
夏蕊
李瑞
王学文
LIU Tao;LI Bo;XIA Rui;LI Rui;WANG Xue-wen(Shanxi Key Laboratory of Fully Mechanized Coal Mining Equipment,College of Mechanical and Vehicle Engineering,Taiyuan University of Technology,Taiyuan 030024,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2024年第3期821-828,共8页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(51804207,51875386)
山西省“1331”工程项目
山西省基础研究计划项目(202103021223080)资助。
关键词
可见-近红外光谱
不同工况
煤矸识别
ADABOOST
正交试验
Vis-NIR spectroscopy
Different working conditions
Coal and gangue identification
AdaBoost
Orthogonal experiment
