电选粉煤灰颗粒图像识别与烧失量预测模型

Image recognition and ignition loss prediction model for triboelectrostatic beneficiation of fly ash particles

在相同光照条件下,运用MATLAB软件控制工业相机获取不同烧失量粉煤灰的图像信息,根据粉煤灰中不同组分对于光反射的差异性,提取脱炭粉煤灰不同组分的图像特征参数,利用极限学习机神经网络建立烧失量与图像特征的数学模型,对比烧失量的预测效果获得最佳的激活函数,实现脱炭粉煤灰烧失量的在线快速检测。结果表明,极限学习机建立的预测模型能够准确识别电选粉煤灰的图像特征,快速获得粉煤灰烧失量数据,准确度高,可用于工业生产中电选粉煤灰烧失量的快速在线检测。

Under the same lighting conditions,the MATLAB software was used to control the industrial camera in order to obtain the image information of the different ignition loss for fly ash.Taking into account the light reflectance differences of different components in the fly ash,extraction of different components of the triboelectrostatic beneficiation fly ash Image feature parameters,the extreme learning machine neural network was used to establish a mathematical model between the ignition loss and image characteristics,and the best activation function was got by comparing the prediction effect of loss on ignition,and the on-line rapid detection of the ignition loss was realized.The results show that the prediction model established by extreme learning machine can accurately identify the image characteristics,and quickly obtain the the ignition loss of fly ash.The extreme learning machine is high precision,and it provides a technical reference for the rapid online testing of the fly ash ignition loss in industrial production.