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From laboratory- to pilot-scale: moisture monitoring in fluidized bed granulation by a novel microwave sensor using multivariate calibration approaches

机译:从实验室到试验规模:使用多元校准方法的新型微波传感器流化床造粒中的水分监测

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摘要

Recently, microwave resonance technology (MRT) sensor systems operating at four resonances instead of a single resonance frequency were established as a process analytical technology (PAT) tool for moisture monitoring. The additional resonance frequencies extend the technologies' possible application range in pharmaceutical production processes remarkably towards higher moisture contents. In the present study, a novel multi-resonance MRT sensor was installed in a bottom-tangential-spray fluidized bed granulator in order to provide a proof-of-concept of the recently introduced technology in industrial pilot-scale equipment. The mounting position within the granulator was optimized to allow faster measurements and thereby even tighter process control. As the amount of data provided by using novel MRT sensor systems has increased manifold by the additional resonance frequencies and the accelerated measurement rate, it permitted to investigate the benefit of more sophisticated evaluation methods instead of the simple linear regression which is used in established single-resonance systems. Therefore, models for moisture prediction based on multiple linear regression (MLR), principal component regression (PCR), and partial least squares regression (PLS) were built and assessed. Correlation was strong (all R-2 0.988) and predictive abilities were rather acceptable (all RMSE 0.5%) for all models over the whole granulation process up to 16% residual moisture. While PCR provided best predictive abilities, MLR proofed as a simple and valuable alternative without the need of chemometric data evaluation.
机译:最近,在四个共振操作而不是单个共振频率的微波谐振技术(MRT)传感器系统是作为湿度监测的过程分析技术(PAT)工具。附加的谐振频率将技术可能的应用范围扩展到药物生产过程中,显着朝向更高的水分含量。在本研究中,一种新型多谐振MRT传感器安装在底部切向喷雾流化床造粒机中,以便在工业飞行员规模设备中提供最近引入的技术的概念。原粒机内的安装位置被优化以允许更快的测量,从而甚至更紧密的过程控制。由于使用新颖的MRT传感器系统提供的数据量通过附加的谐振频率和加速测量速率增加了流形,它允许研究更复杂的评估方法的益处,而不是建立单一的简单线性回归共振系统。因此,基于多元线性回归(MLR),主成分回归(PCR)和偏最小二乘回归(PLS)的湿度预测模型进行了建立和评估。相关性强烈(所有R-2≫ 0.988)和预测能力相当可接受(所有RMSE 0.5%)对于整个造粒过程中的所有型号,高达16%的残余水分。虽然PCR提供了最佳的预测能力,但MLR证明是一种简单而有价值的替代方案,无需化学计量数据评估。

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