3c3-a57d-5d48bc2510c9.jpg width=32.1099992752075 height=30.6849992752075  /> are of dimensions, , , and, respectively.

Since the latent variable model will be developed using transformed variables, let’s define the transformed inputs as follows:

(7)

where is the latent input variable, and is the input loading vector, which is of dimension.

3.2.1. Principal Component Regression (PCR)

PCR accounts for collinearity in the input variables by reducing their dimension using principal component analysis (PCA), which utilizes singular value decomposition (SVD) to compute the latent variables or principal components. Then, it constructs a simple linear model between the latent variables and the output using ordinary least square (OLS) regression [5,10]. Therefore, PCR can be formulated as two consecutive estimation problems. First, the loading vectors are estimated by maximizing the variance of the estimated principal components as follows:

(8)

which, since the data are mean centered, can also be expressed in terms of the input covariance matrix as follows:

(9)

The solution of the optimization problem (9) can be obtained using the method of Lagrangian multiplier, which results in the following eigenvalue problem (see proof in Appendix A):

(10)

which means that the estimated loading vectors are the eigenvectors of the matrix.

Secondly, after the principal components (PCs) are estimated, a subset (or all) of these PCs (which correspond to the largest eigenvalues) are used to construct a simple linear model, that relates these PCs to the output, using OLS. Let the subset of PCs used to construct the model be defined as, where, then the model relating these PCs to the output can be estimated as follows:

(11)

which has the following solution,

(12)

Note that if all the estimated principal components are used in constructing the inferential model (i.e.,), then PCR reduces to OLS. Note also that all principal components in PCR are estimated at the same time (using Equation (10)) and without taking the model output into account. Other methods that consider the input-output relationship into consideration when estimating the principal components include partial least squares (PLS) and regularized canonical correlation analysis (RCCA), which are presented next.

3.2.2. Partial Least Square (PLS)

PLS computes the input loading vectors, , by maximizing the covariance between the estimated latent variable and model output, , i.e., [20,21]:

(13)

where,. Since and the data are mean centered, equation (13) can also be expressed in terms of the covariance matrix as follows:

(14)

The solution of the optimization problem 14 can be obtained using the method of Lagrangian multiplier, which leads to the following eigenvalue problem (see proof in Appendix B):

(15)

which means that the estimated loading vectors are the eigenvectors of the matrix.

Note that PLS utilizes an iterative algorithm [20,22] to estimate the latent variables used in the model, where one latent variable or principal component is added iteratively to the model. After the inclusion of a latent variable, the input and output residuals are computed and the process is repeated using the residual data until a cross validation error criterion is minimized [5,10,22,23].

3.2.3. Regularized Canonical Correlation Analysis (RCCA)

RCCA is an extension of a method called canonical correlation analysis (CCA), which was first proposed in [13]. CCA reduces the dimension of the model input space by exploiting the correlation among the input and output variables. The assumption behind CCA is that the input and output data contain some joint information that can be represented by the correlation between these variables. Thus, CCA computes the model loading vectors by maximizing the correlation between the estimated principal components and the model output [13-16], i.e.,

(16)

where,. Since the correlation between two variables is the covariance divided by the product of the variances of the individual variables, Equation (16) can be written in terms of the covariance between and subject to the following two additional constraints: and. Thus, the CCA formulation can be expressed as follows,

(17)

Note that the constraint is omitted from Equation (17) because it is satisfied by scaling the data to have a zero mean and a unit variance as described in Section 3.2. Since the data are mean centered, Equation (17) can be written in terms of the covariance matrix as follows:

(18)

The solution of the optimization problem (18) can be obtained using the method of Lagrangian multiplier, which leads to the following eigenvalue problem (see proof in Appendix C):

(19)

which means that the estimated loading vector is the eigenvector of the matrix.

Equation (19) shows that CCA requires inverting the matrix to obtain the loading vector,. In the case of collinearity in the model input space, the matrix becomes nearly singular, which results in poor estimation of the loading vectors, and thus a poor model. Therefore, a regularized version of CCA (called RCCA) has been developed in [20] to account for this drawback of CCA. The formulation of RCCA can be expressed as follows:

(20)

The solution of Equation (20) can be obtained using the method of Lagrangian multiplier, which leads to the following eigenvalue problem (see proof in Appendix D):

(21)

which means that the estimated loading vectors are the eigenvectors of the matrix

.

Note from Equation (21) that RCCA deals with possible collinearity in the model input space by inverting a weighted sum of the matrix and the identity matrix, i.e.,

instead of inverting the matrix itself. However, this requires knowledge of the weighting or regularizetion parameter. We know, however, that when, the RCCA solution (Equation (21)) reduces to the CCA solution (Equation (19)). On the other hand, when, the RCCA solution (Equation (21)) reduces to the PLS solution (Equation (15)) since is a scalar.

3.2.4. Optimizing the RCCA Regularization Parameter

The above discussion shows that depending on the value of, where, RCCA provides a solution that converges to CCA or PLS at the two end points, 0 or 1, respectively. The authors in [20] showed that RCCA can provide better results than PLS for some intermediate values of between 0 and 1. This observation motivated us to enhance the prediction ability of RCCA even further by optimizing its regularization parameter. To do that, in this section, we propose the following nested optimization problem to solve for the optimum value of:

(22)

The inner loop of the optimization problem shown in Equation (22) solves for the RCCA model prediction given the value of the regularization parameter, and the outer loop selects the value of that provides the least cross validation mean square error using unseen testing data. The advantages of optimizing the regularization parameter in RCCA will be demonstrated through simulated examples in Section 4.

Note that RCCA solves for the latent variable regression model in an iterative fashion similar to PLS, where one latent variables is estimated in each iteration [20]. Then, the contributions of the latent variable and its corresponding model prediction are subtracted from the input and output data, and the process is repeated using the residual data until an optimum number of principal components or latent variables are used according to some cross validation error criterion. More details about the selection of optimum number of principal components are provided through the illustrative examples in the next section, which will provide some insight about the relative performances of the various inferential modeling methods and some of the practical issues associated with implementing these methods.

4. Illustrative Examples

In this section, the performances of the inferential modeling techniques described in Section 3 and the advantages of optimizing the regularization parameters in RCCA are illustrated through two simulated examples. In the first example, models relating ten inputs and one output of synthetic data are estimated and compared using the various model estimation techniques. In the second example, on the other hand, inferential models predicting distillation column composition are estimated from measurements of other variables, such as temperature, flow rates, and reflux. In both examples, the estimated models are optimized and compared using cross validation, by minimizing the output prediction mean square error (MSE) using unseen testing data as follow,

(23)

where and are the measured and predicted outputs at time step, and n is the total number testing measurements. Also, the number of retained latent variables (or principal components) by the various LVR modeling techniques (PCR, PLS, and RCCA) is optimized using cross validation. Finally, the data (inputs and output) are scaled (by subtracting the mean and dividing by the standard deviation) before constructing the models to enhance their prediction abilities. More details about the advantages of data scaling are presented in Section 4.1.3.

4.1. Example 1: Inferential Modeling of Synthetic Data

In this example, the performances of the various inferential modeling techniques are compared by modeling synthetic data consisting of ten input variables and one output.

4.1.1. Data Generation

The data are generated as follows. The first two input variables are “block” and “heavy-sine” signals, and the other input variables are computed as linear combinations of the first two inputs as follows:

which means that the input matrix X is of rank 2. Then, the output is computed as a weighed sum of all inputs as follows:

(24)

where,

for. The total number of generated data samples is 128. All variables, inputs and output, are assumed to be noise-free, which are then contaminated with additive zero mean Gaussian noise. Different levels of noise, which correspond to signal-to-noise ratios (SNR) of 10, 20, and 50, are used to illustrate the performances of the various methods at various noise contributions. The SNR is defined as the variance of the noise-free data divided by the variance of the contaminating noise. A sample of the output data, where SNR = 20 is shown in Figure 1.

Figure 1. A sample output data set used in the synthetic example for the case where SNR = 20 (solid line: noise-free data; dots: noisy data).

4.1.2. Simulation Results

The simulated data are split into two sets: training and testing. The training data are used to estimate inferential models using the various modeling methods, and the testing data are used to compute the model prediction MSE (as shown in Equation (24)) using unseen data. To make statistically valid conclusions about the performances of the various modeling techniques, a Monte Carlo simulation of 1000 realizations is performed and the results are shown in Table 1 and Figure 2. These results show that the performance of RR is better than that of OLS, and that the performances of the LVR modeling techniques (PCR, PLS, and RCCA) clearly outperform the performances of the full rank models (OLS and RR). This is, in part, due to the fact that in LVR modeling, a portion of the noise in the input variables is removed with the neglected principal components, which enhances the model prediction. This is not the case in full rank models (OLS and RR) where all inputs are used to predict the model output. The results also show that the performances of PCR and PLS are comparable. These results agree with those reported in the literature [24,25], where the number of principal components is freely optimized for each model using cross validation and the models predictions are compared using unseen testing data. The optimum numbers of principal components used by the various LVR models for the case where are shown in Figures 3(a), (c) and (e), which show that the optimum number of principal components used in PCR is usually more than what is used in PLS and RCCA to achieve a comparable prediction accuracy. The results in Table 1 and Figure 2 also show that RCCA provides a slight advantage over PCR and PLS when the optimum value of the regularization parameter is used. The value of is optimized using cross validation as shown in the RCCA problem formulation given in Equation (22). The optimization of for one realization is shown in Figure 4, in which is optimized by minimizing the cross validation MSE of the estimated RCCA model with respect to the testing data. Note also from Figures 3(a), (c) and (e), which compare the number of principal components used by the various

Table 1. Comparison between the prediction MSE’s obtained by the various modeling methods with respect to the noise-free testing data.

Figure 2. Histograms comparing the prediction MSE’s for the various modeling techniques and at different signal-tonoise ratios.

modeling methods, that RCCA is capable of providing this improvement using a smaller number of principal components than PCR and PLS.

4.1.3. Effect of Scaling the Data on the Predictions and Dimensions of Estimated Models

As mentioned earlier, scaled input and output data are used in this example to estimate the various inferential models. To illustrate the advantages of scaling the data (over using the raw data), the prediction and the number of principal components used (in the case of LVR models) are compared for the various model estimation techniques. To do that, a Monte Carlo simulation of 1000 realizations is performed to conduct this comparison, and the results are shown in Figures 3 and 5. Figure 5, which compares the MSE for the various modeling techniques using scaled and raw data, shows a clear advantage for data scaling on the models’ predictive abilities.

(a)(b)(c)

Figure 3. Histograms comparing the optimum number of principal components used by the various modeling techniques for scaled and raw data for the case where SNR = 20.

Figure 4. Optimization of the RCCA regularization parameter using cross validation with respect to the testing data.

Figure 3, on the other hand, which compares the effect of scaling on the optimum number of principal components (for PCR, PLS, and RCCA), shows that when scaled data are used, smaller numbers of PCs are needed for all model estimation techniques, and that RCCA uses the least number of PCs among all techniques.

4.2. Example 2: Inferential Modeling of Distillation Column Compositions

In this example, the various modeling techniques are compared when they are used to model the distillate and bottom stream compositions of a distillation column from other easily measured variables.

4.2.1. Process Description

The column used in this example, which is simulated using Aspen Plus, consists of 32 theoretical stages (including the reboiler and a total condenser). The feed stream, which is a binary mixture of propane and isobutene, enters the column at stage 16 as a saturated liquid. The feed stream has a flow rate of 1 kmol/s, a temperature of 322 K, and a propane composition of 0.4. The nominal steady state operating conditions of the column are presented in the Table 2.

4.2.2. Data Generation

The data used in this modeling problem are generated by perturbing the flow rates of the feed and the reflux streams from their nominal operating conditions. First, step changes of magnitudes ±2% in the feed flow rate around its nominal condition are introduced, and in each case, the process is allowed to settle to a new steady state. After attaining the nominal conditions again, similar step changes of ±2% in the reflux flow rate around its nominal condition are introduced. These perturbations are used to generate training and testing data (each consisting of 64 data points) to be used in developing the various models. These perturbations (for the training and testing data sets) are shown in Figures 6(e)-(h).

In this simulated modeling problem, the input variables consist of ten temperatures at different trays of the column, in addition to the flow rates of the feed and reflux streams. The output variables, on the other hand, are the compositions of the light component (propane) in the

Figure 5. Comparison between the prediction MSE’s of the various modeling techniques using scaled and raw data for the case where SNR = 20.

Table 2. Steady state operating conditions of the distillation column.

(a)(b)(c)(d)

Figure 6. Sample data sets showing the changes in the feed and reflux flow rates and the resulting dynamic changes in the distillate and bottom stream compositions; For the composition data—solid line: noise-free data, dots: noisy data, SNR = 20.

distillate and bottom streams (i.e., and, respectively). The dynamic temperature and composition data generated using the Aspen simulator (due to the perturbations in the feed and reflux flow rates) are assumed to be noise-free, which are then contaminated with zeros mean Gaussian noise. To assess the robustness of the various modeling techniques to different noise contributions, different levels of noise (which correspond to signal-to-noise ratios of 10, 20 and 50) are used. Sample training and testing data sets showing the effect of the perturbations on the column compositions are shown in Figures 6(a)-(d) for the case where the signal-to-noise ratio is 20.

4.2.3. Simulation Results

The simulated distillation column data (training data and testing) used in this example are scaled as discussed in Example 1. The training data set are used to estimate the model, while the testing data are used to optimize and validate the quality of the estimated models. As performed in example 1, the number of principal components (in the case of LVR techniques, i.e., PCR, PLS, and RCCA) and other parameters (such as the regularization parameters, i.e., λ in RR or in RCAA) are determined by minimizing the cross validation MSE for the unseen testing data.

To obtain statistically valid conclusions about the performances of the various modeling techniques, a Monte Carlo simulation of 1000 realizations is performed, and the results are presented in Figure 7 and Table 3. These results show that, in general, the LVR modeling methods (PCR, PLS, and RCCA) outperform the full rank methods (OLS and RR). The results also show that the performances of PCR and PLS are comparable, and that by optimizing its regularization parameter, RCCA can provide an improvement over these techniques. The value of is optimized using cross validation as shown in the RCCA problem formulation given in Equation (22). The optimization of for one realization for the output is shown in Figure 8. Finally, the results show that the prediction abilities of all modeling techniques degrade for larger noise contents, i.e., for smaller signal-to-noise ratios. The results obtained in this distillation column example agree with the results obtained in Example 1.

5. Conclusion

Inferential models are very commonly used in practice to estimate variables which are difficult to measure from other easier-to-measure variables. This paper presents a theoretical review, an extension to optimize RCCA for

Figure 7. Histograms chart comparing the prediction MSE’s of the various modeling techniques using the distillation column data.

Table 3. Comparison between the prediction MSE’s (with respect to the noise-free testing data) of the distillate and bottom stream compositions for the various modeling techniques and at different signal-to-noise ratios.

enhanced prediction, as well as a comparative analysis for various inferential modeling techniques, which include ordinary least square (OLS) regression, ridge regression (RR), principal component regression (PCR), partial least square (PLS), and regularized canonical correlation analysis (RCCA). The theoretical review shows that the loading vectors used in LVR modeling can be computed by solving eigenvalue problems. For RCCA, it is shown that it can be optimized (to provide enhanced prediction ability) by optimizing its regularization parameter, which can be performed by solving a nested optimization problem. The various inferential modeling techniques are compared through two examples, one using synthetic data and the other using simulated distillation column data, where the distillate and bottom stream compositions are estimated using other easily measured variables. Both examples show that the latent variable regression (LVR) techniques (i.e., PCR, PLS, and RCCA) outperform the full rank techniques (i.e., OLS and RR). This is due to their ability to improve the conditioning of the model by neglecting principal components with small eigenvalues, and thus reducing the effect of noise on the model prediction. The obtained results also show that the performances of PCR and PLS are comparable when the number of principal components used are freely optimized using cross validation. Finally, it is shown that by optimizing its regularization parameter, RCCA can provide an improvement (in terms of its prediction MSE) over PCR and PLS using a smaller number of principal components.

6. Acknowledgements

This work was made possible by NPRP grant NPRP

Figure 8. Optimization of the RCCA regularization parameter using cross validation with respect to the testing data.

09-530-2-199 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

REFERENCES

1. J. V. Kresta, T. E. Marlin and J. F. McGregor, “Development of Inferential Process Models Using PLS,” Computers & Chemical Engineering, Vol. 18, No. 7, 1994, pp. 597-611. doi:10.1016/0098-1354(93)E0006-U
2. R. Weber and C. B. Brosilow, “The Use of Secondary Measurement to Improve Control,” AIChE Journal, Vol. 18, No. 3, 1972, pp. 614-623. doi:10.1002/aic.690180323
3. B. Joseph and C. B. Brosilow, “Inferential Control Processes,” AIChE Journal, Vol. 24, No. 3, 1978, pp. 485-509. doi:10.1002/aic.690240313
4. M. Morari and G. Stephanopoulos, “Optimal Selection of Secondary Measurements within the Framework of State Estimationin the Presence of Persistent Unknown Disturbances,” AIChE Journal, Vol. 26, No. 2, 1980, pp. 247- 259. doi:10.1002/aic.690260207
5. I. Frank and J. Friedman, “A Statistical View of Some Chemometric Regression Tools,” Technometrics, Vol. 35, No. 2, 1993, pp. 109-148. doi:10.1080/00401706.1993.10485033
6. A. Hoerl and R. Kennard, “Ridge Regression Based Estimation for Nonorthogonal Problems,” Technometrics, Vol. 8, 1970, pp. 27-52.
7. J. McGregor, T. Kourti and J. Kresta, “Multivariate Identification: A Study of Several Methods,” IFAC ADCHEM Proceedings, Toulouse, Vol. 4, 1991, pp. 145-156.
8. M. N. Nounou, “Dealing with Collinearity in Fir Models Using Bayesian Shrinkage,” Industrial and Engineering Chemistry Research, Vol. 45, 2006, pp. 292-298. doi:10.1021/ie048897m
9. B. R. Kowalski and M. B. Seasholtz, “Recent Developments in Multivariate Calibration,” Journal of Chemometrics, Vol. 5, 1991, pp. 129-145. doi:10.1002/cem.1180050303
10. M. Stone and R. J. Brooks, “Continuum Regression: Cross-Validated Sequentially Constructed Prediction Embracing Ordinaryleast Squares, Partial Least Squares and Principal Components Regression,” Journal of the Royal Statistical Society B, Vol. 52, No. 2, 1990, pp. 237-269.
11. S. Wold, “Soft Modeling: The Basic Design and Some Extensions, Systems under Indirect Observations,” Elsevier, Amsterdam, 1982.
12. E. Malthouse, A. Tamhane and R. Mah, “Non-Linear Partial Least Squares,” Computers and Chemical Engineering, Vol. 21, 1997, pp. 875-890. doi:10.1016/S0098-1354(96)00311-0
13. H. Hotelling, “Relations between Two Sets of Variables,” Biometrika, Vol. 28, 1936, pp. 321-377.
14. F. R. Bach and M. I. Jordan, “Kernel Independent Component Analysis,” Journal of Machine Learning Research, Vol. 3, No. 1, 2002, pp. 1-48.
15. S. S. D. R. Hardoon and J. Shawetaylor, “Canonical Correlation Analysis: An Overview with Application to Learning Methods,” Neural Computation, Vol. 16, No. 12, 2004, pp. 2639-2664. doi:10.1162/0899766042321814
16. M. Borga, T. Landelius and H. Knutsson, “A Unified Approach to PCA, PLS, MLR and CCA, Technical Report,” Technical Report, Linkoping University, 1997.
17. T. Mejdell and S. Skogestad, “Estimation of Distillation Compositions from Multiple Temperature Measurements Using Partial Least Squares Regression,” Industrial & Engineering Chemistry Research, Vol. 30, 1991, pp. 2543-2555. doi:10.1021/ie00060a007
18. M. kano, K. Miyazaki, S. Hasebe and I. Hashimoto, “Inferential Control System of distillation Compositions Using Dynamicpartial Least Squares Regression,” Journal of Process Control, Vol. 10, No. 2, 2000, pp. 157-166. doi:10.1016/S0959-1524(99)00027-X
19. T. Mejdell and S. Skogestad, “Composition Estimator in a Pilot-Plant Distillation Column,” Industrial & Engineering Chemistry Research, Vol. 30, 1991, pp. 2555-2564. doi:10.1021/ie00060a008
20. Y. Hiroyuki, Y. B. Hideki, F. C. E. O. Hiromu and F. Hideki, “Canonical Correlation Analysis for Multivariate Regression and Its Application to Metabolic Fingerprinting,” Biochemical Engineering Journal, Vol. 40, No. 2, 2008, pp. 199-204.
21. R. Rosipal and N. Kramer, “Overview and Recent Advances in Partial Least Squares. Subspace, Latent Structure and Feature Selection Techniques,” Lecture Notes in Computer Science, Vol. 3940, 2006, pp. 34-51. doi:10.1007/11752790_2
22. P. Geladi and B. R. Kowalski, “Partial Least Square Regression: A Tutorial,” Analytica Chimica Acta, Vol. 185, No. 1, 1986, pp. 1-17. doi:10.1016/0003-2670(86)80028-9
23. S. Wold, “Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models,” Technometrics, Vol. 20, No. 4, 1978, p. 397. doi:10.1080/00401706.1978.10489693
24. O. Yeniay and A. Goktas, “A Comparison of Partial Least Squares Regression with Other Prediction Methods,” Hacettepe Journal of Mathematics and Statistics, Vol. 31, 2002, pp. 99-111.
25. P. D. Wentzell and L. V. Montoto, “Comparison of Principal Components Regression and Partial Least Square Regression through Generic Simulations of Complex Mixtures,” Chemometrics and Intelligent Laboratory Systems, Vol. 65, 2003, pp. 257-279. doi:10.1016/S0169-7439(02)00138-7

Starting with the optimization problem shown in Equation (9), i.e.,

(A.1)

the Lagrangian function for this optimization problem can be written as:

(A.2)

Taking the partial derivative of with respect to and equating it to, we get,

(A.3)

which gives the following eigenvalue problem:

(A.4)

i.e., the loading vectors used in PCR are the eigenvectors of the covariance matrix.

Starting with the optimization problem shown in Equation (14), i.e.,

(B.1)

the Lagrangian function can be written as follows:

(B.2)

Taking the partial derivative of with respect to and equating it to we get,

(B.3)

which gives the following eigenvalue problem,

(B.4)

where,. Multiplying Equation (B.4) by and enforcing the constraint (), we get,

(B.5)

Taking the transpose of Equation (B.5), we get,

(B.6)

Combing Equations (B.4) and (B.6), we get the following eigenvalue problem:

(B.7)

Starting with the optimization problem shown in Equation (18), i.e.,

(C.1)

the Lagrangian function can be written as:

(C.2)

Taking the partial derivative of with respect to and equating it to we get,

(C.3)

which gives the following solution,

(C.4)

where Multiplying Equation (C.4) by and enforcing the constraint (i.e.,), we get,

(C.5)

Taking the transpose of Equation (C.5), we get,

(C.6)

Combing Equations (C.4) and (C.6), we get the following eigenvalue problem:

(C.7)

Starting with the optimization problem shown in Equation (20), i.e.,

(D.1)

the Lagrangian multiplier function can be written as follows:

(D.2)

Taking the partial derivative of with respect to and equating it to, we get,

(D.3)

which gives the following solution:

(D.4)

where. Multiplying Equation (D.4) by

and enforcing the constraint

(i.e.,), we get:

(D.5)

Taking the transpose of Equation (D.5), we get,

(D.6)

Combining Equations (D.4) and (D.6), we get the following eigenvalue problem:

(D.7)

NOTES

*Corresponding author.