Real-Time Endpoint Detection of Fluidized Bed Drying Process Based

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Process Systems Engineering

110th Anniversary: Real-Time Endpoint Detection of Fluidized Bed Drying Process Based on a Switching Model of Near-Infrared Spectroscopy Guoqing Mu, Tao Liu, Junghui Chen, liangzhi xia, and Caiyuan Yu Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.9b02747 • Publication Date (Web): 19 Aug 2019 Downloaded from pubs.acs.org on August 25, 2019

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Industrial & Engineering Chemistry Research

110th Anniversary: Real-Time Endpoint Detection of Fluidized Bed Drying Process Based on a Switching Model of Near-Infrared Spectroscopy Guoqing Mu a,b, Tao Liu a,b,*, Junghui Chen c,*, Liangzhi Xia d, Caiyuan Yu d a Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian,116024, China. b Institute of Advanced Control Technology, Dalian University of Technology, Dalian, 116024, China c Department of Chemical Engineering, Chung-Yuan Christian University, Chung-Li District, Taoyuan, 32023, Taiwan d 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] (T. Liu); [email protected] (J. Chen)

Abstract: For timely detecting the drying endpoint of a fluidized bed drying (FBD) process, a switching model based monitoring method is proposed based on in-situ measurement of granule moisture content via near infrared (NIR) spectroscopy. The least-squares support vector classification (LSSVC) method is adopted to build a global model for monitoring the initial underdrying phase with relatively higher granule moisture content. Subsequently, the instance based learning (IBL) strategy is used to select similar samples from historical batches for building up a local model to check on each query sample in the current process, in order to detect whether the real drying endpoint comes or not. To solve the problem of selecting similar samples in highdimensional NIR spectral space, the t-distributed stochastic neighbor embedding (t-SNE) strategy is introduced into the IBL model building method to ensure efficiency of dimension reduction. For on-line monitoring of an FBD process, a model switch strategy is proposed between the above established global model and local models, such that good prediction performance can be obtained with significantly reduced computation effort. Experimental results on the FBD process of silica gel granules demonstrate well the effectiveness and merit of the proposed method in comparison with the existing global model or local model building methods. Keywords: Fluidized bed drying, endpoint detection, near-infrared (NIR) spectroscopy, switching model, instance based learning, least-squares support vector classification (LSSVC)

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1 Introduction Fluidized bed drying (FBD) processes have been widely established for excluding the moisture of granule products in chemical and pharmaceutical engineering applications 1-4, of which the drying endpoint has an important impact on granule product attributes such as stability and compressibility. It is therefore necessary to timely detect the drying endpoint for stopping an FBD process without underdrying or overdrying. The traditional endpoint detection of an FBD process is generally based on indirect parameter measurements, such as humidity and temperature of the outlet dry air, the product temperature or the pressure difference of the fluidized bed

2,5,6

. Since

such measurement does not directly represent the moisture content of granule products and is susceptible to environmental interference, it could not guarantee the prediction accuracy, especially for different FBD operations. Direct measurement on the moisture content of product via the nearinfrared (NIR) spectroscopy has been increasingly explored in the recent years 2,7-9, owing to the non-destructive measurement style along with no need for sample preparation. Because water produces strong absorption of NIR spectra, NIR spectroscopy can be effectively used for measuring the moisture content of granule products under drying, such that the drying endpoint could be reliably detected. Concerning the use of NIR spectroscopy for moisture measurement, spectral calibration model building should be properly conducted to ensure prediction accuracy for real applications, which has received increasing attention in the past decades 10,11. To enumerate a few, using the pure spectra information for calibration model building was studied to predict the moisture content for determining the drying endpoint in terms of specified FBD operation modes pioneering work by Frake et al.

13

12

, following a

where the NIR spectroscopy was firstly reported for real-time

measurement of granule moisture content during an FBD process. Combined spectra analysis was presented by Fonteyne et al. 14 to determine the drying endpoint, based on Raman and near-infrared spectroscopy to measure the moisture content and the status of granule products, respectively. The operational conditions of FBD were considered in the spectral model calibration4, such that the prediction accuracy on granule moisture content could be apparently improved under different FBD operations. It should be noted that the classical data-based modeling methods, such as partial leastsquares (PLS), support vector classification (SVC) and least-squares support vector classification (LSSVC) methods, were mainly adopted for building global spectral calibration models for - 1 -


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monitoring granule moisture content of FBD processes in the existing references as aforementioned. In fact, an FBD process may be generally decomposed into two operational phases, i.e., the underdrying phase and post-drying phase 1. Direct application of a global modeling method for such a multiphase process could be less effective 15, due to the difficulty for specifying a proper model structure to guarantee global prediction accuracy, in particular for the drying phase close to the endpoint. In contrast with a global model, local models could be alternatively established to describe a multiphase batch process

16, 17

. To build up local models with respect to the time

evolution, the instance based learning (IBL) method 18 was explored as an attractive alternative for monitoring multiphase batch processes 19-21. Compared with the traditional global learning strategy, IBL exhibits three main characteristics: firstly, the modeling stage is postponed until an output for a query sample is requested; then, the prediction for the query sample is computed by exploiting the stored samples in the database; thirdly, once the prediction is obtained, the model and any intermediate results are discarded. In the literature, a Euclidean distance-based similarity criterion was commonly utilized for IBL. However, the Euclidean distance selection may not work for highdimensional NIR spectral data, since it remains open for selecting similar samples in highdimensional space 22. Besides, the spectral characteristics of granules with high moisture content in the underdrying phase are obviously different from those of dried products, but this is not the case for granules with low moisture content. Hence, it remains difficult to distinguish the spectral characteristics of granules with low moisture content in the underdrying phase from those in the post-drying phase or dried products. In this paper, a switching model building method is proposed for using the NIR spectroscopy to monitor the drying endpoint of FBD in real time, based on experimental studies on the FBD process of silica gel granules. A global model is built by the LSSVC method for monitoring the initial underdrying phase with relatively higher granule moisture content. When two of three successive samples are checked to be the drying endpoint, the global model is switched to a local model by the IBL method for checking on each query sample in the current process, in order to detect whether the real drying endpoint comes or not. To address the problem of selecting similar samples in the high-dimensional NIR spectral space, the t-distributed stochastic neighbor embedding (t-SNE) strategy is introduced into the IBL local model building, which can ensure the efficiency of dimension reduction at low computation cost. Experimental study on the FBD process - 2 -



of silica gel granules is conducted to verify the effectiveness and advantage of the proposed method.

2 Experimental set-up 2.1 In-situ FBD monitoring system In this study, an in-situ FBD monitoring system built for experiment is shown in Figure 1, consisting of a 5 L rectangular chamber with a tempered glass window, 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 (LED flood light of 50 W, made by TaiYao Company), a temperature sensor, a granule sampler, an immersion type NIR probe (Diffuse reflector, made by ABB Company), and a NIR spectroscopy (Production No. FTPA2000-260, made by ABB Company), along with a data acquisition computer. A brief introduction of the working mechanism and offline measurement by the loss-on-drying (LOD) method for validation is referred to the previous work 4. 2.2 Drying materials Silica gel granules composed of silica (SiO2) are taken as the drying materials for experiments, owing to their high absorbance to water together with stable and nonflammable chemical properties suitable for drying experiments. In this study, the mean size of silica gel granules is about 100 µm. For each batch drying process, granules with moisture content about 40% are used, aiming at the final moisture content lower than 2%. 2.3 Experimental data collection For all the experiments, the FBD inlet air flow was fixed at 0.56 m3/s corresponding to the superficial air flow velocity of 4.38 m/s. The chamber temperature was controlled by a programmable logic controller (PLC). For spectral calibration model building, three batch tests numbered 1-3 under different operating conditions were performed. Each batch was conducted with different heating-up speeds from the room temperature (25 ℃) to a steady drying temperature about 50-60 ℃. For model verification and drying endpoint detection, two more batches were performed, numbered 4 and 5. The fourth batch was conducted under an operating condition similar to that of the second batch. The fifth batch used a constant heating power of 1.2 kW throughout the drying process. For each batch test, the chamber temperature was measured and the accumulated heating energy was counted per second. The NIR spectra were sampled per 23 seconds, considering that almost 20 seconds was spent to collect a spectrum of the NIR spectroscopy, based on an - 3 -


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average of 32 individual scans with a resolution of 8 cm-1. A standard reference collection module (Serial No. AS-01158-060, provided by ABB Company) was used to collect the background NIR spectrum before each experiment. All NIR spectra collected from a batch experiment were subtracted by the background spectrum for model calibration and on-line monitoring, so as to eliminate the influence from environmental conditions. More details about the experimental conditions and data collection are referred to the previous work 4. Figure 2 illustrates the sampled NIR spectra and granule moisture content from batch 1. It is seen from the plot of granule moisture content that the FBD process basically consists of two phases, i.e., underdrying and post-drying phases. Also it is seen that the wavelength range of 4800–10,000 cm−1 contains the absorption peak of water throughout the drying process, which is therefore used for calibration model building. Note that there are 1349 spectral variables in this range of each spectrum. From batches 1-3, there are totally 358 samples collected as the training set for modeling, of which 179 samples were taken in the underdrying phase (larger than 2% moisture content), labeled with 1, and the remaining samples from the post-drying phase were labeled with -1, to facilitate model building. Denote by X ∈ℜ N ×M and Y ∈ ℜ N ×1 the input spectral data and output data of label, respectively. Note that M = 1349 is the number of spectral variables in each spectrum for model calibration, N = 358 is the total number of sampled NIR spectra, and all the sampled spectra are normalized to reduce the influence from measurement noise. From batches 4 and 5 for model verification under similar and different operational conditions, there are 121 and 77 samples collected for testing, respectively. For batch 4, the initial 64 samples were taken from the underdrying phase, and the remaining samples were from the post-drying phase. For batch 5, the number of samples from the underdrying and post-drying phases were 50 and 27, respectively.

3 Problem description For running an FBD process, the moisture content is commonly referenced to determine whether the drying endpoint is reached, that is, whether the drying process should be stopped. The detection of drying endpoint is crucial for the quality of dried products. In fact, the endpoint detection can be regarded as a classification problem. The traditional global modeling methods - 4 -



such as PLS, SVC, and LSSVC could give good prediction accuracy when the granule moisture contents between samples are evidently different from each other, as studied in the previous work4. However, when the granule moisture content is close or smaller than 2%, such a global modeling method may not distinguish samples in the underdrying and post-drying phases accurately. Besides, nonlinear drying characteristics are usually involved with FBD processes in practical applications, hindering the prediction accuracy of linear modeling methods as aforementioned in the literature. Consequently, the drying endpoint could not be timely detected, provoking either the underdrying or overdrying problem associated with granule products. For multiphase processes, IBL is typically used to select similar samples from different batches to establish a local model, which can improve the prediction accuracy compared to a global model, and also can be used to deal with nonlinear problems arising from variations of operational conditions of FBD. Accordingly, it could be expected to determine the endpoint of an FBD process more accurately than a global model. However, the use of IBL needs to establish a new model for each sample, therefore requiring more computation effort than building a global model for overall monitoring of an FBD process. Since the dimension of the spectral space is 1349 ( M = 1349 ), it is inevitable to involve the problem of selecting similar samples in the high-dimensional spectral space. To procure accurate detection of the drying endpoint by using NIR spectroscopy for application, it is proposed to build up a switching model by taking the advantages of a global model (reflecting the overall trend of the process) and local IBL models (reflecting local details of the process). To deal with the problem of selecting similar samples in a high dimensional spectral space, a t-SNE strategy is introduced with respect to each query sample, as detailed in the next section.

4 Proposed switching model In the proposed switching model, a global model is firstly used to monitor the underdrying phase with high moisture content of an FBD process, based on using all samples in the training set for model building. When it approaches the post-drying phase, the global model is switched by an IBL model (built by using similar samples in the training set) to ensure timely detection of the drying endpoint with accuracy. The corresponding modeling methods are detailed in the following two subsections.

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4.1 Global model building To address the nonlinear spectral absorbance under different granule moisture contents and operating conditions of FBD, the LSSVC modeling method 23 is adopted herein to establish a global model for classification of underdrying and dried samples, based on using all the training data set from batches 1-3. Historical batch spectral data and labels are denoted by X ∈ℜ N ×M and Y ∈ ℜ N ×1 , respectively, where xi ∈ ℜ1×M is the ith spectrum of X and y i ∈ ℜ1×1 is a label for the ith sample of Y , denoted by 1 or -1. The classification problem for detection of drying endpoint is therefore formulated as the following optimization program of LSSVC,

1 1 N min J ( w, e ) = w T w + γ ∑ ei2 w ,e 2 2 i =1

(1)

subject to y i [w ϕ (xi ) + b] =1 − ei i =1, ,N T

where w ∈ℜ N ×1 and b are the normal vector and bias of the hyperplane of model fitting, respectively; ϕ (xi ) is a nonlinear function which maps the input space xi into a higher dimensional space; e [e1 , , ei , , e N ]T ∈ ℜ N ×1 is a slack variable for computation; γ is a = regularization parameter to make a trade-off between the fitting errors and the model complexity, which is chosen by a grid-search strategy in terms of a user specified range such as from 10-3 to 105 in combination with 10-fold cross-validation24. To solve the above optimization program, a Lagrange function is introduced as N

L(w, b, e= , α ) J(w, e) − ∑ α i {y i  w Tϕ (xi ) + b  − 1 + ei }

(2)

i =1

where α k ( k = 1, 2, , N ) are the Lagrange multipliers. Therefore, the optimal solution of (2) can be obtained by letting the partial derivative of the above function with respect to w , b , e , α k be zero. Accordingly, the resulting LSSVC model for on-line monitoring of a query sample xˆ k is established by N

ˆ k ) sign( ∑ αi y i Ψ ( xˆ k , x i ) + b) y ( x=

(3)

i =1

where Ψ ( xˆ k , x i ) is a kernel function, which is chosen as the commonly used Gaussian kernel for - 6 -



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simplicity. If y( xˆ k ) = 1 , it means that the kth sample is still ahead of the drying endpoint. Otherwise, the sample is labeled as y( xˆ k ) = −1 and therefore is recorded for determining whether the drying endpoint comes or not based on three successive samples with the same assessment. After the above model is built by 10-fold cross-validation, the model prediction accuracy can be evaluated by the training set. When the classification index of accuracy on the training set such as ACC 25 ( a binary classification index of accuracy, see the definition in (8)) is higher than 85 %, the resulting model is taken as the global model for on-line monitoring of an FBD process. 4.2 Local model building For timely detecting the drying endpoint in the post-drying phase, an IBL based modeling method is developed herein. Since the IBL strategy uses similar samples to build a local model, the choice of similarity has an important impact on the model prediction accuracy. A similarity function 18

adopted here is defined by

Similarity( x i,m ,xˆ k,m ) = −

M

∑ f (x m =1

i,m

, xˆ k,m )

(4)

where xi,m denotes the ith sample with M spectral variables in the historical data set, xˆ k,m is the kth examined sample from real-time measurement, and f is a function of inner production defined by f (x i,m , xˆ k,m ) = (x i,m , xˆ k,m ) 2 . To cope with the high-dimension problem of NIR spectral variables in each sample, the t-SNE method 26,27 is adopted to map the high-dimensional input data to a low-dimensional space of three dimensions, so that the Euclidean distance can be efficiently computed for similarity evaluation via (4). The t-SNE algorithm comprises two steps: Firstly, it computes the probabilities pij that are proportional to the similarity of samples xi and x j as below pji = p ij =

exp(− xi − x j

∑

k ≠i

2

exp(− xi − x k

2σ i2 ) 2

2σ i2 )

p j i + pi j 2N

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(5)

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The bandwidth of Gaussian kernels ( σ i ) is taken by using the bisection method in terms of a specified perplexity of the conditional distribution. As a result, the bandwidth is adapted to the density of the data, i.e., smaller values of σ i are used in denser parts of the data space. Secondly, t-SNE builds up a three-dimensional map r1 , r2 , , rN (with ri ∈ ℜd , d = 3 ) that reflects these similarities as much as possible. To this end, it measures the similarities denoted by q ij between two points in the map ri and r j . Specifically, q ij is defined as 2

q ij =

(1 + ri − r j ) −1

∑

(1 + rk − rl ) −1 k ≠l 2

(6)

Herein a heavy-tailed student-t distribution is used to measure similarities between lowdimensional points. The locations of points ri ( i = 1, 2, ,N ) in the map are determined by minimizing the Kullback–Leibler (KL) divergence of a distribution Q from another distribution P, that is, KL(P Q) = ∑ pij log i≠ j

pij q ij

(7)

The minimization of the KL divergence with respect to points ri ( i = 1, 2, ,N ) is performed in terms of gradient descent. The optimization result is in a low-dimensional space denoted by R = {r1 , r2 , , rN } , reflecting the similarities between high-dimensional input variables.

Note that the IBL based modeling method builds a model by the LSSVC method for each sample, therefore demanding notably higher computation effort compared to the use of a global model. 4.3 Implementation of the switching model To take the advantages of the aforementioned global model and local model for monitoring the drying endpoint, it is proposed to implement the proposed switching model as shown in Figure 3, which is summarized as follows. Step 1: Build up a global model by the LSSVC method for monitoring an FBD process with classification accuracy over 85%, and then use it for on-line monitoring if a query sample is the drying endpoint via (3). Step 2: When two of three successive samples are classified as drying endpoints, the global model - 8 -



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is switched to an IBL model for more accurate detection of the true drying endpoint. Step 3: When building the IBL model for a query sample, the t-SNE method is used to reduce the high dimension of NIR spectra and select similar samples from historical batches via (4)-(7). Step 4: When two successive samples are checked by the IBL model to be the drying endpoint, it should be confirmed that the true drying endpoint is reached.

5 Case study The proposed switching model is applied to predict the drying endpoints of batches 4 and 5 as mentioned in section 2.3. For comparison, global models built by PLS, SVC and LSSVC, respectively, are also used to predict the drying endpoint. Moreover, local models built by PLS, SVC and LSSVC in combination with t-SNE, denoted by t-SNE-IBL-PLS, t-SNE-IBL-SVC and tSNE-IBL-LSSVC, respectively, are used for these batches. For performance assessment, a binary classification index of accuracy named ACC 25 is adopted here, which is defined by

ACC =

TP+TN P+N

(8)

where TP and TN denote the numbers of positively-labeled and negatively-labeled samples rightly predicted by the model, respectively. P and N are the positively-labeled and negatively-labeled samples in the total samples, respectively. For assessment of computation time, the simulation computer is configured by Windows 10 (64 bit), CPU: Intel Core i7-6700 (3.4 GHz), RAM: 8 GB, equipped with the computation toolbox of MATLAB (version 2018a). 5.1 Monitoring batch 4 with similar operating conditions In this batch, the true drying endpoint was verified as the 65th sampling point (with moisture content of 1.84% measured by the LOD method). In other words, the initial 64 samples should be labeled with 1, and the remaining samples should be labeled with -1. The component numbers of the PCA and PLS algorithms are chosen as 5 and 3, which can explain 99.6% and 78.6% process data information, respectively. The parameter slack variable and kernel width parameter for SVC and LSSVC models are determined by minimizing the misclassification error in terms of the 10-fold cross-validation. The data size of IBL is taken as 20 for building up a local model for on-line detection of a query sample. - 9 -


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Table 1 lists the prediction results of three global models. It is seen that better prediction results are obtained by the SVC and LSSVC methods, owing to their capacity for dealing with nonlinear spectral properties. By comparison with SVC, the LSSVC method significantly reduces the computation time, owing to avoiding the quadratic programming optimization involved with SVC. Note that all of three global models give earlier predictions of the drying endpoint, as shown in Figure 4, where the classification points appeared before the real endpoint (the 65th sampling point). This will cause the moisture content of granule products to be predicted higher than the expected value, provoking the underdrying issue of drying production. In order to reduce the high dimensionality of spectral variables to facilitate selecting similar samples for using the IBL method to build up a local model, as mentioned in section 3, PCA and tSNE are applied here for comparison. Figure 5 shows the results of projection into threedimensional space by PCA and t-SNE, respectively. It can be seen that the spectral data after dimension reduction by PCA is very scattered, while some samples in the underdrying and postdrying phases are mixed together. In contrast, the spectral data of each phase after dimension reduction by t-SNE is more concentrated along with no overlapping between two phases. Therefore, it is preferred to use t-SNE for selecting similar samples from historical batch data. Table 2 lists the prediction results for batch 4 under similar operational conditions by the PCAIBL and t-SNE-IBL methods for local model building via PLS, SVC, and LSSVC, respectively. It is seen that the optimal ACC index is obtained by using the t-SNE-IBL method for selecting similar samples and the LSSVC method for local model building, while the computation time for building all local models and prediction is moderate among different combinations of the above methods. In contrast, local models built based on the proposed t-SNE-IBL method for selecting similar samples give better prediction as indicated by ACC than those by the PCA-IBL method. Figures 6 and 7 show the PCA-IBL and t-SNE-IBL based prediction results for batch 4 under similar operational conditions, based on different local model building via PLS, SVC and LSSVC, respectively. It is seen from either Figure 6 or Figure 7 that the IBL based local model building by PLS gives rise to many misclassification points in the post-drying phase compared with those by SVC and LSSVC, indicating that PLS is not capable of establishing an IBL based local model for detecting the drying endpoint. By comparison, the PCA-IBL based local model building by SVC or LSSVC gives evidently earlier prediction of the drying endpoint than that by the t-SNE-IBL - 10 -



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based local model building, well demonstrating the advantage of the proposed t-SNE-IBL method for selecting similar samples to build up a local model. Moreover, it can be seen from Figure 7 (c) that there is only one sample in the underdrying phase misclassified into the post-drying phase by using the proposed t-SNE-IBL plus LSSVC method for local model building, corresponding to the highest value of ACC (i.e. 0.9917) listed in Table 2. Note that a single misclassified sample will not affect on-line detection of the drying endpoint owing to the judgement condition of two successive samples checked to be the endpoint in the proposed method shown in Figure 3. Since Figure 4 has shown that a global model built by LSSVC can give good prediction of the underdrying phase with relatively higher moisture content, e.g. before the 60th sample, it can be easily verified that using the proposed model switch strategy from the global model to the t-SNEIBL based local model can significantly reduce the computation time to 229.52 seconds, compared to 339.25 seconds listed in Table 2 by the t-SNE-IBL plus LSSVC method for overall monitoring of the batch process. 5.2 Monitoring batch 5 with different operating conditions To further demonstrate the effectiveness and advantage of the proposed switching model, the fifth batch was tested under different operational conditions with batches 1-4. As mentioned in Section 2.3, a total of 77 samples were collected for test, of which the initial 50 samples were in the underdrying phase. The true drying endpoint was verified as the 51st sample (with moisture content of 1.95% measured by the LOD method). In other words, the initial 50 samples should be labeled with 1, and the remaining samples should be labeled with -1. Table 3 lists the prediction results of three global models. It is seen that LSSVC gives the best prediction compared to PLS and SVC. Note that all of three global models still give obviously earlier predictions of the drying endpoint (the 51st sampling point), as shown in Figure 8, similar to the case for batch 4 as shown in Figure 4. This will lead to the underdrying issue of drying production. Table 4 lists the prediction results for batch 5 under different operational conditions by the PCA-IBL and t-SNE-IBL methods for local model building via PLS, SVC, and LSSVC, respectively. It is once again seen that the optimal ACC index is obtained by using the t-SNE-IBL method for selecting similar samples and the LSSVC method for local model building, while the computation time is moderate among different combinations of the above methods. - 11 -


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Figures 9 and 10 show the PCA-IBL and t-SNE-IBL based prediction results for batch 5 under different operational conditions, based on different local model building via PLS, SVC and LSSVC, respectively. It is seen from Figure 9 that the PCA-IBL based local model building by PLS, SVC or LSSVC still gives evidently earlier prediction of the drying endpoint, compared with that of the t-SNE-IBL based local model building via SVC or LSSVC. Besides, it is seen from Figure 10 (a) that the t-SNE-IBL plus PLS local model building method also gives many incorrect predictions in the post-drying phase, possibly owing to the inefficiency of PLS in dealing with the nonlinear spectral characteristics of similar samples in the post-drying phase. Moreover, it can be seen from Figure 10 (c) that there is no sample in the underdrying phase misclassified into the post-drying phase by using the proposed t-SNE-IBL plus LSSVC method for local model building, corresponding to the highest value of ACC (i.e. 0.9774) listed in Table 4. Since Figure 7 has shown that a global model built by LSSVC can give good prediction of the underdrying phase with relatively higher moisture content under different operational conditions, e.g. before the 50th sample, it can be easily verified that using the proposed model switch strategy from the global model to the t-SNE-IBL based local model can significantly reduce the computation time to 147.79 seconds, compared to 213.15 seconds listed in Table 4 by the t-SNE-IBL plus LSSVC method for overall monitoring of the batch process.

6 Conclusions For on-line monitoring the moisture content of an FBD process using NIR spectroscopy, a switching model building method has been proposed to timely detect the drying endpoint. An important merit is that the established switching model can guarantee good accuracy owing to using the similar samples from historical batches in the post-drying phase for local model building, while consuming significantly reduced computation effort to build a global model for monitoring the underdrying phase with relatively higher moisture content. Moreover, the t-SNE strategy is introduced to solve the problem of selecting similar samples in high dimensional NIR spectral space. Experimental results on the FBD process of silica gel granules well demonstrate that the proposed t-SNE-IBL plus LSSVC model building method can give a good prediction of the drying endpoint under similar or different operational conditions. By comparison, a global model built by the existing methods such as PLS, SVC, or LSSVC, tends to give evidently earlier prediction of the drying endpoint, thus likely provoking the underdrying issue of drying production. Besides, the - 12 -



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PCA-IBL based local model building methods also cannot guarantee the prediction accuracy when it comes to the end of the underdrying phase. To facilitate practical applications with good accuracy and less computation effort, guidelines are provided for implementing the proposed switching model for on-line detection of the drying endpoint.

Acknowledgement This work was supported in part by the NSF China Grant 61633006 and the Fundamental Research Funds for the Central Universities of China (DUT18ZD201).

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infrared (NIR) spectroscopy: focus on parameters optimization and model interpretation. Chemometrics and Intelligent Laboratory Systems 2009, 96, (1), 27-33. 10. Ge, Z. Q., Review on data-driven modeling and monitoring for plant-wide industrial processes. Chemometrics and Intelligent Laboratory Systems 2017, 171, 16-25. 11. Zhang, F.; Liu, T.; Wang, X. Z.; Liu, J.; Jiang, X., Comparative study on ATR-FTIR calibration models for monitoring solution concentration in cooling crystallization. Journal of Crystal Growth 2017, 459, 50-55. 12. Paul Findlay, W.; Peck, G. R.; Morris, K. R., Determination of fluidized bed granulation end point using near‐ infrared spectroscopy and phenomenological analysis. Journal of Pharmaceutical Sciences 2005, 94, (3), 604-612. 13. Frake, P.; Greenhalgh, D.; Grierson, S. M.; Hempenstall, J. M.; Rudd, D. R., Process control and end-point determination of a fluid bed granulation by application of near infra-red spectroscopy. International Journal of Pharmaceutics 1997, 151, (1), 75-80. 14. Fonteyne, M.; Gildemyn, D.; Peeters, E., Moisture and drug solid-state monitoring during a continuous drying process using empirical and mass balance models. European Journal of Pharmaceutics and Biopharmaceutics 2014, 87, (3), 616-628. 15. Undey, C.; Cinar, A., Statistical monitoring of multistage, multiphase batch processes. IEEE Control Systems Magazine 2002, 22, (5), 40-52. 16. Liu, Y.; Gao, Z. L.; Li, P.; Wang, H. Q., Just-in-time kernel learning with adaptive parameter selection for soft sensor modeling of batch processes. Industrial & Engineering Chemistry Research 2012, 51, (11), 4313-4327. 17. Cheng, C.; Chiu, M. S., A new data-based methodology for nonlinear process modeling. Chemical Engineering Science 2004, 59, (13), 2801-2810. 18. Aha, D. W.; Kibler, D.; Albert, M. K., Instance-based learning algorithms. Machine Learning 1991, 6, (1), 37-66. 19. Ge, Z.; Song, Z., A comparative study of just-in-time-learning based methods for online soft sensor modeling. Chemometrics and Intelligent Laboratory Systems 2010, 104, (2), 306-317. 20. Ge, Z. Q.; Song, Z. H.; Gao, F. R., Review of recent research on data-based process monitoring. Industrial & Engineering Chemistry Research 2013, 52, (10), 3543-3562. - 14 -



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List of Table and Figure Captions Table 1.

Global model prediction results for batch 4 under similar operational conditions

Table 2. PCA-IBL and t-SNE-IBL based prediction results for batch 4 under similar operational conditions Table 3.

Global model prediction results for batch 5 under different operational conditions

Table 4.

PCA-IBL and t-SNE-IBL based prediction results for batch 5 under different operational

conditions

Figure 1.

Experimental set-up of an in-situ FBD monitoring system: (a) external view; (b)

schematic diagram Figure 2.

Illustration of sampled data from an FBD process: (a) NIR spectra; (b) granule moisture

content Figure 3.

Schematic of the proposed switching model for implementation

Figure 4.

Comparison of prediction results for batch 4 under similar operational conditions based

on a global model built by: (a) PLS; (b) SVC; (c) LSSVC Figure 5.

Graphic illustration of projection into three-dimensional space by (a) PCA; (b) t-SNE

Figure 6.


on local models built by PCA-IBL plus: (a) PLS; (b) SVC; (c) LSSVC Figure 7.


on local models built by t-SNE-IBL plus: (a) PLS; (b) SVC; (c) LSSVC Figure 8.

Comparison of prediction results for batch 5 under different operational conditions

based on a global model built by: (a) PLS; (b) SVC; (c) LSSVC Figure 9.


based on local models built by PCA-IBL plus: (a) PLS; (b) SVC; (c) LSSVC Figure 10.


based on local models built by t-SNE-IBL plus: (a) PLS; (b) SVC; (c) LSSVC

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Table 1.

Page 18 of 27

Global model prediction results for batch 4 under similar operational conditions

Table 2.

Index

PLS

SVC

LSSVC

ACC

0.9091

0.9587

0.9587

Computation time (second) 0.2271 59.0187

4.3369

PCA-IBL and t-SNE-IBL based prediction results for batch 4 under similar operational conditions

Index

PLS

ACC

SVC

LSSVC

0.7769 0.9421

0.9421


27.861

PCA-IBL ACC

0.7521 0.9752

0.9917


339.25

t-SNE-IBL

Table 3.

Global model prediction results for batch 5 under different operational conditions

Index

PLS

ACC

LSSVC

0.9481 0.8831

0.9481

0.192

4.1175

Computation times (second)

Table 4.

SVC

52.16

PCA-IBL and t-SNE-IBL based prediction results for batch 5 under different operational conditions

Index

PLS

SVC

LSSVC

ACC

0.7662

0.8961

0.8831

Computation time (second)

3.5698

609.73

11.807

ACC

0.8182

0.9774

0.9774

Computation time (second)

209.41

866.97

213.15

PCA-IBL

t-SNE-IBL

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Page 19 of 27 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


Materials feeder

(a)

High intensity light

Chamber NIR probe

Pt100 thermometer

Sampler

PLC control panel NIR spectroscopy acquisition computer

(b)

Figure 1.

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

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(a)

3255 seconds 5578 seconds

0 seconds

20

(b)

Batch1 15

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 20 of 27

10

Endpoint 5

0 0

10

20

30

40

50

67

Samples

Figure 2.

Illustration of sampled data from an FBD process: (a) NIR spectra; (b) granule moisture content

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Figure 3. Schematic of the proposed switching model for implementation

- 20 -



Drying status

(a)

1

Endpoint

0

Underdrying phase Post-drying phase

-1 0

20

40

60

80

100

121

(b)

Drying status

Samples 1

Endpoint

0


-1 0

20

40

80

60

100

121

Samples

(c)

Drying status

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 27

1 Underdrying phase

0

Endpoint

Post-drying phase

-1 0

20

40

60

80

100

121

Samples

Figure 4. Comparison of prediction results for batch 4 under similar operational conditions based on a global model built by: (a) PLS; (b) SVC; (c) LSSVC

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Page 23 of 27

(a)

The 3rd principal component

1


0.5 0 -0.5 6 4

5

2 0

0 -5

-2 -4

The 2nd principal component

-10

The 1st principal component

(b) 20


The 3rd principal component

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


10

0

-10 10 5

60 40

0

20 0

-5

The 2nd principal component

-10

-20 -40

The 1st principal component

Figure 5.

Graphic illustration of projection into three-dimensional space by (a) PCA; (b) t-SNE

- 22 -


(a)

Page 24 of 27

1 0

Endpoint


-1 0

20

40

60

80

100

121

(b)

Drying status

Samples 1

Endpoint

0


-1 0

20

40

60

80

100

121

(c)

Drying status

Samples 1 Underdrying phase

0

Endpoint

Post-drying phase

-1 0

20

40

60

80

100

121

Samples

(a)

Drying status

Figure 6. Comparison of prediction results for batch 4 under similar operational conditions based on local models built by PCA-IBL plus: (a) PLS; (b) SVC; (c) LSSVC

1 0

Endpoint


-1 0

20

40

60

80

100

121

Drying status

Samples

(b)

1 Underdrying phase

0

Endpoint

Post-drying phase

-1 0

20

40

60

80

100

121

Samples

(c)

Drying status

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

Drying status


1 Underdrying phase

0

Endpoint

Post-drying phase

-1 0

20

40

60

80

100

Samples

Figure 7. Comparison of prediction results for batch 4 under similar operational conditions based on local models built by t-SNE-IBL plus: (a) PLS; (b) SVC; (c) LSSVC

- 23 -


121

(a)

1 Underdrying phase

Endpoint

0

Post-drying phase

-1 0

10

20

30

40

50

60

77

(b)

Drying status


0

Endpoint

Post-drying phase

-1 0

10

20

30

40

50

60

77

(c)

Drying status


Endpoint

0

Post-drying phase

-1 0

10

20

30

40

50

60

77

Samples

(a)

Drying status

Figure 8. Comparison of prediction results for batch 5 under different operational conditions based on a global model built by: (a) PLS; (b) SVC; (c) LSSVC

1 0

Underdrying phase

Endpoint

Post-drying phase

-1 0

10

20

30

40

50

60

77

(b)

Drying status

Samples 1 0

Underdrying phase

Endpoint

Post-drying phase

-1 0

10

20

30

50

40

60

77

Samples

(c)

Drying status

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


Drying status

Page 25 of 27

1 0

Underdrying phase

Endpoint

Post-drying phase

-1 0

10

20

30

40

50

60

Samples

Figure 9. Comparison of prediction results for batch 5 under different operational conditions based on local models built by PCA-IBL plus: (a) PLS; (b) SVC; (c) LSSVC

- 24 -


77


1

Drying status

(a)

Underdrying phase

0

Endpoint

Post-drying phase

-1 0

10

20

30

40

50

60

77

Samples 1

Drying status

(b)

Underdrying phase

0

Post-drying phase

Endpoint

-1 0

10

20

30

40

50

60

77

Samples

(c)

1

Drying status

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 27

Underdrying phase

0

Post-drying phase

Endpoint

-1 0

10

20

30

40

50

60

77

Samples

Figure 10. Comparison of prediction results for batch 5 under different operational conditions based on local models built by t-SNE-IBL plus: (a) PLS; (b) SVC; (c) LSSVC

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Table of contents (TOC)

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Real-Time Endpoint Detection of Fluidized Bed Drying Process Based

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