Calibration Model Building for On-line Monitoring of the Granule

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Calibration Model Building for On-line Monitoring of the Granule Moisture Content during Fluidized Bed Drying by NIR Spectroscopy Guoqing Mu, Tao Liu, Jingxiang Liu, Liangzhi Xia, and Caiyuan Yu Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b05043 • Publication Date (Web): 29 Mar 2019 Downloaded from http://pubs.acs.org on March 31, 2019

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Industrial & Engineering Chemistry Research

Calibration Model Building for On-line Monitoring of the Granule Moisture Content during Fluidized Bed Drying by NIR Spectroscopy Guoqing Mu†, Tao Liu†, *, Jingxiang Liu†, Liangzhi Xia‡, Caiyuan Yu§

† ‡

Institute of Advanced Control Technology, Dalian University of Technology, Dalian, 116024, China School of Chemical Machinery and Safety Engineering, Dalian University of Technology, Dalian, 116024,

China § School of Chemical Engineering, Dalian University of Technology, Dalian, 116024, China *

Corresponding author. Tel: +86-411-84706465; Fax: +86-411-84706706; E-mail: [email protected]

Abstract: For monitoring the granule moisture content during a fluidized bed drying (FBD) process, a calibration model building method is proposed for in-situ measurement using the near infrared (NIR) spectroscopy. It is found that the FBD operating conditions such as the chamber temperature and heating power have a nonnegligible impact on the NIR model prediction of granule moisture. By combining these operating variables with the measured NIR spectra for model calibration, the prediction accuracy for on-line measurement of the granule moisture content under different process conditions could be evidently improved compared to only using the measured NIR spectra for model calibration. To determine the optimal number of factors for establishing a partial-least-squares (PLS) regression model for predicting the granule moisture content, it is proposed to combine the leave-one-out cross validation (LOOCV) approach with the median absolute percentage error (MdAPE) index to deal with measurement outliers often involved with practical applications, based on a comparative study with the well-known K-fold cross validation (KCV) and Monte Carlo cross validation (MCCV) methods. Experimental results on monitoring the silica gel granule moisture under different FBD operating conditions demonstrate the effectiveness of the proposed spectral calibration method. Keywords: In-situ monitoring, calibration model building, near-infrared (NIR) spectroscopy, fluidized bed drying, moisture content, partial least-squares regression.

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1 Introduction Owing to that the moisture content has an important impact on chemical and pharmaceutical granule product attributes such as stability and compressibility, the fluidized bed drying (FBD) technology has been widely used to exclude the moisture of granule products in engineering applications 1-3. It is therefore necessary to measure the moisture content of granules for control of an FBD process. This, however, has been primarily conducted by off-line measurement methods, such as the Karl–Fischer titration (KFT) for analyzing water content of lyophilized product 4, and the loss on drying (LOD) method for measuring the moisture content of albendazole 5, which may bring unexpected time delay and artificial errors. Although analyzer-based techniques were developed based on the above KFT or LOD for quickly measuring the product moisture, they were still applied off line and limited to a small amount of samples taken from the drying process 2. Note that preparing the samples for measurement could lead to unexpected artificial errors 6. For on-line measurement, the acoustic emission technology was proposed to measure the granule moisture content based on acoustic signal analysis 7, which, however, could not provide good accuracy under different operating conditions of FBD processes, owing to the fact that acoustic emission signals are likely affected by the fluidizing air flow rate, the construction materials of FBD, and environmental acoustic sources 2. In contrast, the near infrared (NIR) spectroscopy has been increasingly explored for in-situ monitoring the moisture contents of various products in the recent years 8-11, owing to the non-invasive and/or non-destructive measurement style along with no need for sample preparation. For using the NIR spectroscopy to conduct in-situ measurement of the moisture content of granule products, only a few spectral calibration methods were presented in the existing references. A partial least-squares (PLS) model for the NIR spectra calibration was proposed in the reference12 to predict the end point of an FBD process. Barla et al.13 compared the spectral calibration models established by the multiple linear regression (MLR), partial least-squares (PLS), principal component regression (PCR), and support vector machine (SVM), finding that the PLS regression model could guarantee good prediction under different process conditions. In addition, it was demonstrated in the reference14 that using the PLS based NIR spectra calibration for measuring the granule moisture in conjunction with the chamber humidity and temperature could facilitate improving monitoring and control performance of FBD processes. Note that the process operating - 1 -


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conditions were almost not considered in the existing PLS spectral calibration methods for using the NIR or mid-infrared (MIR) spectroscopy to perform in-situ measurement 2, 8, 15. Meanwhile, the root-mean-square-of-cross-validation (RMSECV) index was primarily used to determine the number of retained factors in such a PLS model, which indeed could be sensitive to measurement outliers such as improper samples (unrepresentative of the granules under drying by FBD) taken for off-line measurement by the LOD method as often encountered in engineering applications. The resulting model prediction accuracy could be intangibly degraded under different operating conditions such as the FBD chamber temperature and heating energy. In this paper, a modified spectral calibration method is proposed for using the NIR spectroscopy to in-situ measure the moisture content of granule products in real time, based on experimental studies on the FBD process for silica gel granules. The operating conditions of the FBD chamber temperature and heating energy are explicitly taken into account for spectral calibration model building. The median-absolute-percentage-error (MdAPE) index is used to deal with measurement outliers when performing leave-one-out cross validation (LOOCV) to determine the optimal factor number of a PLS-based calibration model. Experimental results demonstrate that the proposed calibration model building method could effectively predict the granule moisture content with good accuracy under different FBD operating conditions.

2 Experimental setup 2.1 In-situ monitoring system by NIR spectroscopy In this study, an in-situ FBD monitoring system built for experiment is shown in Figure 1(a), consisting of a 5-liter rectangle chamber with a steel glass window for observation, an air blower with power of 3 KW, an electric heater with power of 6 KW, a 1L materials feeder, a 2L storage tank, a high intensity light, a temperature sensor, a granule sampler, an immersion type NIR probe, and a NIR spectroscopy (Production No. FTPA2000-260, made by ABB Company) with a diffuse reflection probe. A schematic diagram of the drying process with in-situ measurement of the moisture content by NIR spectroscopy is shown in Figure 1 (b). For operating the FBD machine, the ambient air is firstly absorbed by the air blower and heated by the electric heater. Then the heated inlet air is distributed uniformly through the air distribution plate installed at the bottom of the fluidized bed chamber. Subsequently, the hot air - 2 -



fluidizes the granules in the chamber for drying, and finally rises up to the top of fluidized bed for discharge. The chamber temperature is measured by a thermometer of Pt100 and regulated by a programmable logic controller (PLC) commanded by a monitoring computer. The head of the above NIR probe with a focal length of 250 mm is placed above the air distribution plate in the chamber, at the same height of the granule sampler set for off-line measurement (LOD method) of the granule moisture content for comparative study. The LOD method is conducted to measure the moisture content by the difference in granule weight before and after drying in an air oven with operating temperature of 105 oC for 6 hours. 2.2 Drying materials Silica gel granules composed of silica (SiO2) are taken as the drying materials as studied in the literature 16, owing to that they are amorphous and highly adsorbent to water, possessing stable and nonflammable chemical properties. In this study, the mean size of silica gel granules is about 100 µm. For a batch run of the drying process, silica gel granules of 1650 g with 2% moisture content are initially mixed with distilled water of 450 g via an electric mixer of 5L to ensure even distribution of the moisture content about 40%. At the end of each batch run, these granules are dried to the moisture content about 2%. 2.3 Experimental data collection For all the experiments, the FBD inlet air flow was fixed at 0.5564 m3/s. The chamber temperature was controlled by PLC in a range from 25 to 70 ℃. Throughout each experiment, the chamber temperature were measured and the accumulated heating energy were counted per second. The NIR spectra were sampled per 23 seconds, considering that almost 20 seconds were spent to collect a spectrum of the NIR spectroscopy with resolution of 8 cm-1. Each spectrum was collected based on an average of 32 individual scans to reduce measurement noise. Note that there is almost no change of the granule moisture content in 30 seconds. During each batch run of the drying process, granule samples were manually collected by the sampler at a time interval longer than 23 seconds for off-line measuring the moisture content by the LOD method, such that two sequential samples definitely corresponds to two different NIR spectra collected for measurement, and therefore can be used for comparative study. A sample taken for off-line measurement was assigned to the NIR spectra with a closer time index if the sample was taken between two subsequently collected NIR spectra. Note that the chamber temperature varied in different batch runs, and the - 3 -


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final moisture content was maintained identically about 2% for all batch runs. Taking the second batch for example, a number of the collected NIR spectra are shown in Figure 2. It can be seen that the absorption spectra of NIR spectroscopy change with the variation of moisture content along the time. The NIR spectroscopy exhibits intense absorption at the wavenumbers of 6900 and 5180 cm−1, which arises from the stretching and deformation of the OH group in water17, 18. In view of that the wavelength range of 4800–10,000 cm−1 is subject to a low noise to signal ratio, it is used for calibration model building. Note that there are 1349 spectral variables in this range of each spectrum. Table 1 lists the number of sampled spectra in each batch used for model calibration. Totally 174 samples are collected from three batch experiments. Owing to experimental errors made by taking improper samples (unrepresentative of the majority of granules under drying) for off-line measurement by the LOD method, two samples not complying with a descending trend of the granule moisture content are viewed as measurement outliers, as shown in Figure 3 (a). Figure 3 shows different operating conditions of three batch runs, including the heating power, chamber temperature, and heating energy, together with the corresponding variation of granule moisture content measured by the LOD method. Note that the heating energy is a cumulative sum of heat consumption at each sampling point. It is seen from Figure 3 (a) that the granule moisture contents of three batches monotonically decrease to a steady value in the end. Meanwhile, it is seen from Figure 3 (b), (c) and (d) that the chamber temperature of three batches gradually increases to a steady value, while the heating energy and heating power of each batch are different from each other. It should be mentioned that the common constant-rate and falling-rate regimes of drying dynamics 19 for different solid materials were observed in these batch experiments of drying silica gel granules, as shown in Figure 3(a). This means that the investigation results in this study can be generally applied to different solid materials. Note that the granule moisture content is negatively correlated with the time, and in contrast, the heating energy is positively correlated with the time, as shown in Figure 3. Meanwhile, the granule moisture content is negatively correlated with the chamber temperature. Hence, there exists a coupling relationship between the granule moisture content, chamber temperature and heating energy. The Pearson correlation analysis is therefore conducted to investigate the coupling relationship between these operating variables and the granule moisture content. The results are - 4 -



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listed in Table 2. It is seen that both correlation coefficeints of chamber temperature and heating energy are close to one for different batches, therefore indicating very strong correlation between these two variables and the granule moisture content. This motivated this study to take these operational variables as the modeling variables together with the spectral variables. Since the units of these operational variables are different, they are normalized together with the measured NIR spectra for calibration model building.

3 Calibration model building for in-situ measurement by NIR spectroscopy 3.1 PLS regression model

S [ s1 , s2 , …, sn ]T ∈ℜ Denote by=

n×ns

the input data set of spectra, where n is the total

number of sampled spectra, and ns is the number of spectral variables included in each sample of spectrum. The granule moisture contents measured by the off-line LOD method for each sample constitute the output data set denoted by Y ∈ ℜn×1 . Since there is n = 174 based on three batch experiments, as listed in Table 1, while the number of spectral variables is ns = 1349 in each sampled spectrum for calibration, the corresponding model building belongs to a small sample size problem associated with collinearity among the spectral variables. The well-known PLS regression method could be used to deal with such modeling problem. Taking into account the impact of process operating conditions on the spectral calibration model, two operating variables, namely chamber temperature and heating energy, are introduced into the PLS model, by defining

X [ x1 , x2 ,…, xn ]T ∈ℜn×( ns +2) where xi = [ si , λi , µi ] denotes the input variables, si the spectral = variables included in each spectrum,

λi the corresponding chamber temperature, and µi the

corresponding heating energy. Hence, the proposed PLS-based calibration model is given by  X = TP T + E  Y = TB + F

(1)

where Τ ∈ ℜn×h is the score matrix with h factor numbers, P ∈ ℜns ×h is the loading matrix,

B ∈ ℜh×1 is the regression coefficient vector of T , E ∈ ℜn×ns and F ∈ ℜn×1 are the residuals of S and Y , respectively.

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For real-time measurement, the predicted moisture content denoted by yˆ is computed by using the new input data denoted by xnew ∈ℜ( ns +2)×1 that include the newly sampled spectrum and the corresponding operational variables of chamber temperature and heating energy, i.e., T yˆ = xnew BPLS

(2)

Note that the PLS regression coefficients BPLS ∈ℜ( ns + 2)×1 can be easily determined by a PLS algorithm package 20. 3.2 Choice of the factor number for model fitting For establishing a PLS model, it is crucial to determine the factor number denoted by h. The well-known cross validation methods for determining the optimal factor number include LOOCV, K-fold cross validation (KCV) and Monte Carlo cross validation (MCCV) 21. Note that the number of groups (K) for using KCV is experientially specified as 5 in the literature 22, and the Monte Carlo procedure is generally conducted 1000 times for using MCCV. In practical applications, there usually exist measurement outliers which could badly affect the parameter estimation for model fitting. Typically, the RMSECV index is used for choosing the factor number in a PLS model, which is defined by RMSECV =

n cv 2 1 ˆ y y ( ) ∑ i i ncv i=1

(3)

where ncv is the number of samples in the cross validation set; ˆyi and yi are the predicted and measured moisture content of the i -th sample, respectively. For the presence of measurement outliers, it was recognized that the prediction accuracy of a PLS model built by the RMSECV index could be inferior to that by using the median absolute percentage error (MdAPE) 23. Hence, the MdAPE index is adopted to choosing the factor number of a PLS model for NIR spectra calibration, taking into account the unwelcome outliers appearing in measuring the moisture contents by the LOD method in practice. The MdAPE index is herein defined by MdAPE = median

yˆ i - yi yi

where ‘median’ denotes an operator of taking the intermediate value of (ˆyi - yi ) / yi . - 6 -


(4)


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For demonstration, a comparison of choosing the factor number for PLS model building with and without outliers is shown in Table 2. It is seen that the number of factors chosen by using the RMSECV index is fixed at 8 no matter if the outliers are used for computation or not. In contrast, the number of factors is changed by using the MdAPE index with respect to not only the outliers but also the input variables. This change can facilitate building a more accurate model, as will be further demonstrated in the later section 4. 3.3 Model evaluation To assess the effectiveness and accuracy of the established models, three criteria are adopted, including the coefficient of determination (R2), the root mean square error for prediction (RMSEP) and the ratio of performance to standard deviation (RPD). Specifically, R2 and RMSEP are used to evaluate the model accuracy, while RPD is used to evaluate the model resolution. Herein define RMSEP =

1 n 2 ˆ y − y ( ) ∑ i i n i =1

(5)

where n is the number of test samples by the off-line LOD method, yi denotes the moisture content in each sample, yˆi is the correspondingly predicted moisture content by the NIR spectroscopy based on the established calibration model. R2 is defined as n

R2 = 1−

∑ ( yˆ − y )

2

∑( y − y )

2

i

i =1 n

i

i =1

i

(6)

i

where y is the mean of yi = ( i 1,2 ,… , n ). RPD is estimated by

RPD =

1 n ∑ ( yi − yi )2 1 n i =1 = n 1 2 1− R2 ˆ y − y ( ) ∑ i i n i =1

(7)

which in general should be greater than 2 to indicate good resolution 24. Besides, the variable importance in projection (VIP) score can be used to estimate the - 7 -


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contribution of each variable in the PLS model projection 25. The VIP score for the j th variable is computed by  w p ∑ ( ba2 taT ta )  a , j  wj a =1    h

VIPj =

   

2

   

(8)

h

∑ (b t

2 T a a a

t )

a =1

where h is the factor number, p the number of variables of X , wa , j the loading weight of the j th variable in the a th factor number, and ta , wa , ba the ath column vectors of T , W , b , respectively. According to the “greater than one rule” for variable selection

26

, the variables

with VIP scores greater than one should be considered for model building.

4 Results and discussion Table 4 lists the model fitting effects denoted by R2 in terms of using different input variables for the above PLS modeling, where the data marked in blue and red indicate the best and worst result, respectively, while ‘-’ denotes no computation according to the choices of factor number in Table 3. It can be seen from Table 4 in combination with Table 3, all values of R2 obtained by using the RMSECV and MdAPE index are close to 1, indicating the effectiveness of model fitting by PLS regression. Meanwhile, it is seen that under the same factor number, the R2 values of modeling without measurement outliers are evidently higher than those by using these outliers, indicating that it is necessary to exclude these outliers for calibration model building. Figure 4 gives an illustration of PLS modeling in terms of the factor number of 7 based on only using the spectra. Four representative spectra picked from Figure 2 are shown in Figure 4(a). It can be seen that the characteristic peak of water (-OH) was obviously shifted during the drying process. In particular for the final drying stage denoted by the moment of 3460 seconds, the water peak was almost overlaid by the characteristic peak of silica gel (-SiOH). A PLS model can capture these changes effectively and exclude useless information, as shown in Figure 4(b) of loading and Figure 4(c) of regression coefficients. Figure 4(d) shows the score plot of the leading three factors. Note that these factors take up 49.17%, 27.41% and 2.04% of the moisture content variance, respectively, therefore reflecting the major modeling feature. However, it remains unclear if the rest factors should be used to ensure the model accuracy and reliability. Cross-validation is - 8 -



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therefore adopted for optimal factor selection. To demonstrate the model prediction performance, two more batch experiments on monitoring the FBD process were performed. The fourth batch used the similiar operating conditions with the above three batches for model building, and in contrast, the fifth batch used an open-loop control strategy to monontonically increasing the chamber temperature. It can be seen from Figure 5 that the moisture content is negatively correlated with the chamber temperature and heating energy, similar to those in Figure 3. This implies that using these two operating variables for spectral model calibration could faciliate improving the prediction accuracy. Note that the Pearson correlation coefficients of heating energy for the fourth and fifth batches are computed as -0.9429 and -0.9844, respectively, obviously larger than those of chamber temperature (-0.8044 and -0.8893, respectively). This indicates that heating energy takes a bigger role for the proposed spectral model calibration than chamber temperature. Tables 5-7 list the values of R2, RMSEP and RPD for model prediction using different variables and factor numbers, respectively. For comparison, all the results are listed in terms of model building based on the sampled data of batches 1-3 without or with measurement outliers, respectively. It is seen that the best predicted results (corresponding to the maxima of R2 and RPD and the minimum of RMSEP, marked in blue) are obtained when taking the spectral variables together with the heating energy and chamber temperature as the modeling variables with respect to the factor number of 7, regardless of using the outliers in batches 1-3 for computation or not. The prediction accuracy (reflected by RMSEP in Table 6) is evidently improved in comparison with only using the spectral variables for model calibration, therefore demonstrating that it is necessary to add the heating energy and chamber temperature into the modeling variables for improving the prediction performance. Note that using a single operating variable of the heating energy to combine with the spectral variables for model calibration slightly improves the prediction accuracy compared to only using the spectral variables, in case the measurement outliers are excluded for model building. However, only using the chamber temperature to combine with the spectral variables for model calibration could not improve the prediction accuracy, as shown in Tables 5 and 6. From Tables 5-7, it should be noted that the optimal factor number is 7 for model building, which results in the maxima of R2 and RPD together with the minimum of RMSEP marked in blue. - 9 -


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Such a choice of factor number is determined by using LOOCV in combination with the MdAPE index, as indicated in Table 3. This demonstrates the advantage of combining both indices for choosing the optimal factor number for PLS modeling, compared to only using the well-known indices such as LOOCV, KCV, and MCCV, respectively. Note that although the combination of MCCV and MdAPE can also determine the optimal factor number of 7, as shown in Table 3, the computation effort is much higher than that of combining LOOCV with MdAPE, e.g., the computation times for determining the above factor number of 7 are 2.9630 seconds and 23.9503 seconds, respectively, based on a computer with a dual core CPU of 3.40 GHz and a memory size of 8 GB. Hence, it is not preferred to use the combination of MCCV and MdAPE for choosing the optimal factor number. Figure 6 shows a comparison of moisture content prediction for the fourth batch under similar operating conditions, by using the proposed calibration model built by two operating variables together with the spectral variables and another model built only by the spectral variables, respectively. Note that both models are built by using the measurement outliers in the above three batches, for illustration of practical application without excluding measurement outliers. It is seen that the proposed calibration model gives evidently improved prediction of the real granule moisture content measured by the LOD method as marked by black line, especially when the granule moisture content is relatively high in the initial phase. Figure 7 shows the VIP score of each variable in the proposed calibration model. It is seen that both operating variables of chamber temperature and heating energy have scores greater than one, indicating apparently larger contribution compared to any of the spectral variables. This demonstrates the importance of both operating variables for model building, according to the “greater than one rule” for variable selection 26. Meanwhile, it is seen that the VIP score of heating energy is larger than that of chamber temperature, indicating that heating energy has a larger contribution to the calibration model than chamber temperature. Table 8 lists a comparison of moisture content prediction for the fifth batch under different operating conditions, by using the proposed calibration model built by two operating variables together with the spectral variables and another model built only by the spectral variables, respectively. Note that both models are also built by using the measurement outliers in the above three batches for illustration. It can be seen that the prediction result is obviously superior to that - 10 -



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by only using the spectral variables, once again demonstrating that it is necessary to add the heating energy and chamber temperature to the modeling variables for improving the prediction performance under different FBD operating conditions. Figure 8 shows the obviously improved prediction by the proposed calibration model with respect to the real granule moisture content measured by the LOD method as marked by black line. The prediction given by the model built only by the spectral variables becomes further degraded compared to that shown in Figure 6 for similar operating conditions, due to no consideration of variations in the FBD operating conditions.

5 Conclusions For in-situ measurement of the granule moisture content of a FBD process using NIR spectroscopy, an improved spectral calibration model building method has been proposed for practical applications. A PLS regression model is established by taking the measured NIR spectral variables together with two important FBD operating conditions of chamber temperature and heating energy as the modeling variables, where the optimal number of retained factors is determined by using the cross validation method of LOOCV in combination with the MdAPE index. Comparative studies demonstrate that the proposed strategy for choosing the number of retained factors can be well used to deal with measurement outliers as often encountered in engineering applications, in contrast to the well-known KCV and MCCV methods. Experimental results demonstrate that the proposed calibration model can guarantee good prediction accuracy for online monitoring the granule moisture content under different FBD operating conditions. Meanwhile, it is revealed that only using the spectral variables for calibration model building could give unexpected inferior prediction results under different operating conditions. In contrast, if only one FBD operating condition (either chamber temperature or heating energy) is considered for building the spectral calibration model, it is demonstrated by experimental results that only slight or even no improvement could be obtained for model prediction. Besides, it is also demonstrated that using a prior knowledge to exclude the measurement outliers for calibration model building can facilitate improving the prediction accuracy compared to the direct use of all the measured NIR spectra including outliers. It should be noted that the nonlinearity of NIR spectra with respect to chamber temperature and drying materials

27

is not specifically addressed in this study, which deserves

further exploration in the future work for application to a very different temperature range or other drying materials with distinct moisture properties. - 11 -


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Acknowledgement This work was supported in part by the NSF China Grant 61633006 and the Fundamental Research Funds for the Central Universities of China.

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processes in fluidized beds - a review. Adv Powder Technol. 2014, 25, (1), 195-210. 10. Kauppinen, A.; Toiviainen, M.; Korhonen, O.; Aaltonen, J.; Järvinen, K.; Paaso, J.; Juuti, M.; Ketolainen, J., Inline multipoint near-infrared spectroscopy for moisture content quantification during freeze-drying. Anal Chem. 2013, 85, (4), 2377-2384. 11. Sales, K. C.; Rosa, F.; Sampaio, P. N.; Fonseca, L. P.; Lopes, M. B.; Calado, C. R. C., In situ near-infrared (NIR) versus high-throughput mid-infrared (MIR) spectroscopy to monitor biopharmaceutical production. Appl Spectrosc. 2015, 69, (6), 760-772. 12. Peinado, A.; Hammond, J.; Scott, A., Development, validation and transfer of a near infrared method to determine in-line the end point of a fluidised drying process for commercial production batches of an approved oral solid dose pharmaceutical product. J Pharm Biomed Anal. 2011, 54, (1), 13-20. 13. Barla, V. S.; Kumar, R.; Nalluri, V. R.; Gandhi, R. R.; Venkatesh, K., A practical evaluation of qualitative and quantitative chemometric models for real-time monitoring of moisture content in a fluidised bed dryer using near infrared technology. J near Infrared Spec. 2014, 22, (3), 221-228. 14. Kona, R.; Qu, H.; Mattes, R.; Jancsik, B.; Fahmy, R. M.; Hoag, S. W., Application of in-line near infrared spectroscopy and multivariate batch modeling for process monitoring in fluid bed granulation. Int J Pharm. 2013, 452, (1-2), 63-72. 15. Rodrigues, K. C. S.; Sonego, J. L. S.; Bernardo, A.; Ribeiro, M. P. A.; Cruz, A. J. G.; Badino, A. C., Real-time monitoring of bioethanol fermentation with industrial musts using mid-infrared spectroscopy. Ind Eng Chem Res. 2018, 57, (32), 10823-10831. 16. Takeuchi, H.; Nagira, S.; Yamamoto, H.; Kawashima, Y., Solid dispersion particles of amorphous indomethacin with fine porous silica particles by using spray-drying method. Int J Pharm. 2005, 293, (1-2), 155-64. 17. Yamauchi, H.; Kondo, S., The structure of water and methanol adsorbed on silica gel by FT-NIR spectroscopy. Colloid Polym Sci. 1988, 266, (9), 855-861. 18. Socrates, G., Infrared and Raman characteristic group frequencies table and charts. J. Wiley: 2004; p 1-347. 19. Kunii, D.; Levenspiel, O., Fluidization engineering. Elsevier: 2013. - 13 -


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20. Li, H. D.; Xu, Q. S.; Liang, Y. Z., libPLS: An integrated library for partial least squares regression and linear discriminant analysis. Chemometr Intell Lab. 2018, 176, 34-43. 21. Xu, Q. S.; Liang, Y. Z., Monte Carlo cross validation. Chemometr Intell Lab. 2001, 56, (1), 1-11. 22. Demsar, J., Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res. 2006, 7, 1-30. 23. Hyndman, R. J.; Koehler, A. B., Another look at measures of forecast accuracy. Int J Forecasting. 2006, 22, (4), 679-688. 24. Assis, C.; Ramos, R. S.; Silva, L. A.; Kist, V.; Barbosa, M. H. P.; Teofilo, R. F., Prediction of lignin content in different parts of sugarcane using near-infrared spectroscopy (NIR), ordered predictors selection (OPS), and partial least squares (PLS). Appl Spectrosc. 2017, 71, (8), 2001-2012. 25. Gosselin, R.; Rodrigue, D.; Duchesne, C., A bootstrap-VIP approach for selecting wavelength intervals in spectral imaging applications. Chemometr Intell Lab. 2010, 100, (1), 12-21. 26. Chong, I.-G.; Jun, C.-H., Performance of some variable selection methods when multicollinearity is present. Chemometr Intell Lab. 2005, 78, (1-2), 103-112. 27. Igne, B.; Ciurczak, E. W., Pharmaceutical and medical applications of near-infrared spectroscopy. CRC Press: 2014.

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Page 16 of 28

List of Table and Figure Captions Table 1. Numbers of sampled spectra for calibration model building Table 2. Correlation results between the operating variables and granule moisture Table 3. Comparison of the factor number choices by using the measurement outliers or not Table 4. The R2 values for model calibration with different factor numbers Table 5. The R2 values for model calibration with different factor numbers Table 6. Comparison of RMSEP for model prediction using different variables and factor numbers Table 7. Comparison of RPD for model prediction using different variables and factor numbers Table 8. Comparison of prediction results under different operating conditions by two different calibration models built with measurement outliers

Figure 1. Experimental set-up of an in-situ FBD monitoring system: (a) external view; (b) schematic diagram Figure 2. Plot of sampled NIR spectra of silica gel granules during an FBD process Figure 3. Experimental data for calibration model building: (a) moisture content; (b) heating power; (c) chamber temperature; (d) heating energy Figure 4. Illustration of PLS modeling: (a) sampled spectra at four moments; (b) regression coefficients; (c) loading; (d) scores of the leading three PLS factors Figure 5. Experimental data for model verification: (a) moisture content; (b) heating power; (c) chamber temperature; (d) heating energy Figure 6. Comparison of moisture content prediction under similar operating conditions by two different calibration models built with measurement outliers Figure 7. VIP scores of the modeling variables Figure 8. Comparison of moisture content prediction under different operating conditions by two different calibration models built with measurement outliers

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Page 17 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 1.

Numbers of sampled spectra for calibration model building Batch 1 2 3

Number of samples 67 53 54

Outliers 0 1 1

Table 2. Correlation results between the operating variables and granule moisture

Variables Chamber temperature Heating energy

Pearson correlation coefficient Batch 1 Batch 2 Batch 3 -0.9636 -0.6706

-0.9018 -0.9927

-0.8702 -0.9934

Total of Batches 1-3 -0.8652 -0.8381

Table 3. Comparison of the factor number choices by using the measurement outliers or not

Variables

Spectra Spectra, chamber temperature Spectra, heating energy Spectra, chamber temperature, heating energy

Cross validation method LOOCV KCV MCCV LOOCV KCV MCCV LOOCV KCV MCCV LOOCV KCV MCCV

Factor number No outliers With outliers RMSECV MdAPE RMSECV MdAPE 8 8 8 9 8 8 8 8 8 7 8 8 8 6 8 8 8 9 8 9 8 8 8 7 8 5 8 7 8 7 8 7 8 7 8 7 8 7 8 7 8 9 8 5 8 7 8 7

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Table 4.

Page 18 of 28

The R2 values for model calibration with different factor numbers

Variables Spectra

Spectra, chamber temperature

Spectra, heating energy

Spectra, heating energy, chamber temperature

Factor number 7 8 9 6 7 8 9 5 7 8 5 7 8 9

No outliers

With outliers

0.9904 0.9908 0.9895 0.9908 0.9904 0.9882 0.9906 0.9910 0.9906 0.9909 0.9906

0.9891 0.9887 0.9887 0.9891 0.9885 0.9865 0.9889 0.9893 0.9865 0.9889 0.9892 -

Table 5. Comparison of R2 for model prediction using different variables and factor numbers Variables Spectra



Spectra, chamber temperature, heating energy

Factor number 7 8 9 6 7 8 9 5 7 8 5 7 8 9

No outliers

With outliers

0.9867 0.9806 0.9771 0.9563 0.9540 0.9821 0.9862 0.9869 0.9881 0.9851 0.9812

0.9863 0.9827 0.9817 0.9735 0.9666 0.9848 0.9837 0.9874 0.9879 0.9865 -

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Table 6. Comparison of RMSEP for model prediction using different variables and factor numbers Variables Spectra



Spectra, chamber temperature, heating energy

Factor number 7 8 9 6 7 8 9 5 7 8 5 7 8 9

No outliers

With outliers

0.9354 1.1307 1.2295 1.6978 1.7421 1.0849 0.9540 0.9302 0.8868 0.9896 1.1133

0.9508 1.0668 1.0980 1.3222 1.4831 0.9996 1.0373 0.9111 0.8929 0.9441 -

Table 7. Comparison of RPD for model prediction using different variables and factor numbers

Variables Spectra



Spectra, heating energy, chamber temperature

Factor number 7 8 9 6 7 8 9 5 7 8 5 7 8 9

No outliers 8.6796 7.1805 6.6034 4.7821 4.6604 7.4837 8.5106 8.7286 9.1554 8.2040 7.2926

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With outliers 8.5389 7.6100 7.3943 6.1403 5.4742 8.1224 7.8272 8.9112 9.0928 8.5994 -


Table 8.

Page 20 of 28

Comparison of prediction results under different operating conditions by two different calibration models built with measurement outliers

Variables

R2

RMSEP

RPD

Spectra

0.9290

2.1228

3.7518

Spectra, heating energy, chamber temperature 0.9665

1.4570

5.4666

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Page 21 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(a )

Materials feeder High intensity light Pt100 thermometer Chamber NIR probe Sampler

NIR spectroscopy acquisition computer

PLC control panel

(b)

Materials feeder Chamber Observation window High intensity light

NIR probe

Distribution plate Granule sampler

PLC control panel

Temperature sensor Heater Air blower Storage tank

Figure 1. Experimental set-up of an in-situ FBD monitoring system: (a) external view; (b) schematic diagram - 20 -



2

1.5

Intensity

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 28

1352 seconds 3460 seconds

1

0.5 60

40

0 second Samples

20

0

10000

8000

9000

7000

5000

4000

-1

Wavenumber (cm

Figure 2.

6000

)

Plot of sampled NIR spectra of silica gel granules during an FBD process

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Page 23 of 28

1.3

50

(a)

(b)

Batch 1

Batch 2 1

Batch 3

Outliers

Heating power (KW)

Moisture content (%)

Batch 1

Batch 2

40

30

20

Batch 3

0.5

10

0

0 0

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(c)

30

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Samples

Samples 200

65

(d)

Batch 1 Batch 2

60

Batch 3

150

55

Heating energy (KJ)

Chamber temperature (℃)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


50 45 40

Batch 1 Batch 2 Batch 3 100

50

35 30 25

0 0

10

20

30

40

50

60

70

0

10

Samples

Figure 3.

20

30

40

50

60

Samples

Experimental data for calibration model building: (a) moisture content; (b) heating power; (c) chamber temperature; (d) heating energy

- 22 -


70


(a)

Intensity

2

-OH

1.5

-OH

-SiOH

0 second

1352 seconds

2450 seconds

3460 seconds

1 0.5 4700

5000

5500

6000

6500

7000

7500

8000

8500

9000

9500

10000

8500

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-1

(c)

Loading

(b)

Regression coefficients

Wavenumbers (cm

)

15 10 0 -10 4700

5000

5500

6000

6500

7000

7500

8000 -1

Wavenumbers (cm

)

0.3 0.2

1st

2nd

3th

0 -0.2 4700

5000

5500

6000

6500

7000

7500

8000

8500

9000

9500

10000

-1

Wavenumbers (cm

)

100 1st: 49.17%

(d)

Scores

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 28

50

2nd: 27.41%

3rd: 2.00%

0 -40 0

20

40

60

80

100

120

140

160

174

Samples

Figure 4.

Illustration of PLS modeling: (a) sampled spectra at four moments; (b) regression coefficients; (c) loading; (d) scores of the leading three PLS factors

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Page 25 of 28

40

1.4

(a)

(b)

Batch 4

35

Batch 4

1.2

Batch 5

Batch 5 1

25

Heating power (KW)

Moisture content (%)

30

20 15 10

0.8

0.6

0.4

0.2

5 0

0 0

10

20

30

40

50

0

10

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Samples

(c)

30

40

50

Samples

70

250

(d)

65

Batch 4

Batch 4

Batch 5 200

Batch 5

60 55

Heating energy (KJ)

Chamber temperature (℃)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


50 45 40 35

150

100

50 30 25

0 0

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Figure 5.

20

30

40

50

Samples

Experimental data for model verification: (a) moisture content; (b) heating power; (c) chamber temperature; (d) heating energy

- 24 -



35 Modeling by only the spectral variables Modeling by both operating and spectral variables 30

25

Predicted moisture content (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 28

20

15

10

5

0 0

5

10

15

20

25

30

Real moisture content measured by LOD (%)

Figure 6. Comparison of moisture content prediction under similar operating conditions by two different calibration models built with measurement outliers

Heating energy Chamber temperature

Figure 7. VIP scores of the modeling variables

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Page 27 of 28

30 Modeling by only the spectral variables Modeling by both opearting and spectral variables 25

20

Predicted moisture content (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


15

10

5

0 0

5

10

15

20

25

30

Real moisture content measured by LOD (%)

Figure 8. Comparison of moisture content prediction under different operating conditions by two different calibration models built with measurement outliers

- 26 -


Industrial & Engineering Chemistry Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 - 27 -


Page 28 of 28

Calibration Model Building for On-line Monitoring of the Granule

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