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首页> 外文期刊>Powder Technology: An International Journal on the Science and Technology of Wet and Dry Particulate Systems >Acoustic chemometrics for material composition quantification in pneumatic conveying - The critical role of representative reference sampling
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Acoustic chemometrics for material composition quantification in pneumatic conveying - The critical role of representative reference sampling

机译:气动输送中物料成分定量的声学化学计量学-代表性参考样品的关键作用

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Reliable monitoring of pneumatically conveyed particulate materials is critical for on-line detection and controlling material composition changes in the regimen of Process Analytical Technology (PAT), e.g. as the case investigated here: determination of varying concentration levels of extraneous material in a source stream. Results are reported from an experimental test campaign on a pilot-scale pneumatic conveying facility. Optimal sensor deployment and material flow rates are decisive parameters for signal quality and prediction performance. The test campaign resulted in an optimal sensor location/flow rate combination, based on which we present a validated prediction model (Partial Least Squares Regression model) for prediction of extraneous material concentration levels, with a RMSEP_((rel)) of 17.7% (RMSEP: 0.92) and r~2 of 0.95. The present approach is based on acoustic chemometrics (a.c). The impact of nominal reference values vs. representative reference values used as response variables in prediction models is discussed. Optimal reference values were obtained through the use of representative sampling equipment (based on Theory of Sampling, TOS), specifically designed for pneumatic conveying systems, and compared with nominal concentration levels, allowed an improvement of the prediction model: (RMSEP_((rel))), of 15% (RMSEP: 0.85) and r2 0.95). Whilst the present experimental rig test resulted in relative minor quantitative improvements only, representative reference samples, required for prediction models, are essential when nominal concentration levels cannot be determined or are unknown, which is usually the case in enclosed pneumatic conveying systems, the target for this study. All prediction results are validated with independent data (test set validation).
机译:在过程分析技术(PAT)的方案中,可靠地监视气动输送的颗粒材料对于在线检测和控制材料成分变化至关重要。如此处调查的情况:确定源流中异物的变化浓度水平。结果来自中试规模的气力输送设施的实验测试活动。最佳的传感器部署和物料流速是信号质量和预测性能的决定性参数。测试活动产生了最佳的传感器位置/流速组合,在此基础上,我们提出了一个有效的预测模型(偏最小二乘回归模型),用于预测无关物质的浓度水平,RMSEP _((rel))为17.7%( RMSEP:0.92)和r〜2为0.95。本方法基于声学化学计量学(a.c)。讨论了在预测模型中用作响应变量的名义参考值与代表性参考值的影响。最佳参考值是通过使用代表性的采样设备(基于采样理论,TOS)获得的,该采样设备是专门为气动输送系统设计的,并且与标称浓度水平进行了比较,从而改进了预测模型:(RMSEP _((rel) ))的15%(RMSEP:0.85)和r2 0.95)。尽管目前的试验台试验仅带来了相对较小的定量改进,但当无法确定或未知标称浓度水平时,预测模型所需的代表性参考样品必不可少(通常在封闭式气动输送系统中是这种情况的目标)这项研究。所有预测结果均使用独立数据进行验证(测试集验证)。

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