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Ultrasonic spectrum for particle concentration measurement in multicomponent suspensions

机译:超声光谱用于多组分悬浮液中颗粒浓度的测量

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摘要

This paper studies the feasibility of applying the ultrasonic spectrum technique to the measurement of particle concentrations in multicomponent suspensions. A combination of the kernel partial least squares (KPLS) model and the interval selection methods is implemented to build the relationship between the ultrasonic spectra of the first reflected pulses and the particle concentrations. First of all, the interval selection methods are used to select optimal spectral interval(s) from full spectra. Then, the KPLS models with optimal spectral interval(s) are tuned, built and evaluated to obtain the optimal model. Finally, the optimal KPLS model is employed to measure the particle concentrations in the mixing process and its online prediction ability is evaluated. In comparison with the linear partial least squares (PLS) models, the optimal KPLS model shows the best performance. The results demonstrate that particle concentrations in multicomponent suspensions can be measured online by the ultrasonic spectrum technique, and the KPLS model with optimal spectral interval(s) shows the superiority in model calibration.
机译:本文研究了将超声光谱技术应用于多组分悬浮液中颗粒浓度测量的可行性。结合核偏最小二乘(KPLS)模型和区间选择方法,建立了第一反射脉冲的超声光谱与粒子浓度之间的关系。首先,间隔选择方法用于从全光谱中选择最佳光谱间隔。然后,对具有最佳频谱间隔的KPLS模型进行调整,构建和评估,以获得最佳模型。最后,采用最优的KPLS模型测量混合过程中的颗粒浓度,并对其在线预测能力进行了评估。与线性偏最小二乘(PLS)模型相比,最佳KPLS模型显示出最佳性能。结果表明,可以通过超声光谱技术在线测量多组分悬浮液中的颗粒浓度,并且具有最佳光谱间隔的KPLS模型显示出模型校准的优越性。

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