Scale-Free Soft Sensor for Monitoring of Water Content in Fluid Bed Granulation Process

Yaginuma, Keita; Tanabe, Shigeru; Miyano, Takuya; Suzuki, Satoshi; Ando, Shuichi; Kano, Manabu

doi:10.1248/cpb.c20-00315

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…≤ ≤ θ θ θ (12) where e hm,n is the output error of the heat and mass balance model, and y n is the output measurement for the n-th sample. N is the number of samples in the calibration dataset.…”

Section: Fitting Parameters Optimizationmentioning

confidence: 99%

“…Some studies have demonstrated the applications of black-box models based on process parameters (PPs) and near-IR spectroscopy (NIRS) for water content monitoring in fluidized bed granulation or drying. [10][11][12][13][14] However, black-box models are less intuitive in nature, 4) making it difficult for industry operators to understand in detail.…”

Section: Introductionmentioning

confidence: 99%

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Gray-box Soft Sensor for Water Content Monitoring in Fluidized Bed Granulation

2022

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Soft sensors are powerful tools for the implementation of process analytical technology (PAT). They are categorized into white-box (first-principle), black-box (statistical), and gray-box models. Gray-box models integrate white-box and black-box models to address each drawback, i.e., prediction accuracy and intuitiveness. Although they have been applied to various industrial processes, their applicability to water content monitoring in fluidized bed granulation has not been reported. In this study, we evaluated three types of gray-box models, i.e., parallel, serial, and combined gray-box models, in terms of prediction accuracy using real operating data on a commercial scale with two formulations. The gray-box models were constructed by integrating the heat and mass balance model (white-box model) and locally weighted partial least squares regression (LW-PLSR) model (black-box model). LW-PLSR was utilized to cope with collinearity and nonlinearity. In the serial gray-box models, LW-PLSR models adjusted the fitting parameters of the white-box model depending on the process parameters for each query. In the parallel gray-box or combined gray-box models, LW-PLSR models compensated for the output error of the white-box or serial gray-box models, respectively. The results demonstrated that all three types of gray-box models improved the prediction accuracy of the white-box models regardless of the formulation. Besides, we proposed the assessment method based on Hotelling's T 2 and Q residual for gray-box models using LW-PLSR, which contributes decision support to select gray-box or white-box model. The accurate and descriptive gray-box models are expected to enhance process understanding and precise quality control in fluidized bed granulation.

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Section: Fitting Parameters Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gray-box Soft Sensor for Water Content Monitoring in Fluidized Bed Granulation

2022

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“…[8][9][10][11] Recently, nonlinear regression methods, such as locally weighted partial least squares regression (LW-PLSR), 12) have been applied in various industrial processes to increase prediction accuracy. [12][13][14][15][16][17] However, they have been rarely utilized in fluidized bed granulation processes. Our previous research 18) demonstrated that the LW-PLSR models based on process parameters (PPs) and near-infrared spectroscopy (NIRS) were better estimators of granule water content than the PLSR models.…”

Section: Introductionmentioning

confidence: 99%

Prediction Performance and Economic Efficiency of Soft Sensors for in-Line Water Content Monitoring in Fluidized Bed Granulation: PP-Based Model <i>vs.</i> NIRS-Based Model

Yaginuma

Tanabe

Sugiyama

et al. 2021

CHEMICAL & PHARMACEUTICAL BULLETIN

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Soft sensors play a crucial role as process analytical technology (PAT) tools. They are classified into physical models, statistical models, and their hybrid models. In general, statistical models are better estimators than physical models. In this study, two types of standard statistical models using process parameters (PPs) and near-infrared spectroscopy (NIRS) were investigated in terms of prediction accuracy and development cost. Locally weighted partial least squares regression (LW-PLSR), a type of nonlinear regression method, was utilized. Development cost was defined as the cost of goods required to construct an accurate model of commercial-scale equipment. Eleven granulation lots consisting of three laboratory-scale, two pilotscale, and six commercial-scale lots were prepared. Three commercial-scale granulation lots were selected as a validation dataset, and the remaining eight granulation lots were utilized as calibration datasets. The results demonstrated that the PP-based and NIRS-based LW-PLSR models achieved high prediction accuracy without using the commercial-scale data in the calibration dataset. This practical case study clarified that the construction of accurate LW-PLSR models requires the calibration samples with the following two features: 1) located near the validation samples on the subspace spanned by principal components (PCs), and 2) having a wide range of variations in PC scores. In addition, it was confirmed that the reduction in cost and mass fraction of active pharmaceutical ingredient (API) made the PP-based models more cost-effective than the NIRS-based models. The present work supports to build accurate models efficiently and save the development cost of PAT.

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