Abstract
In this paper, an inferential sensor for the final viscosity of an industrial batch polymerization reaction is developed using multivariate statistical methods. This inferential sensor tackles one of the main problems of chemical batch processes: the lack of reliable online quality estimates.
In a data preprocessing step, all batches are brought to equal lengths and significant batch events are aligned via dynamic time warping. Next, the optimal input measurements and optimal model order of the inferential multiway partial least squares (MPLS) model are selected. Finally, a full batch model is trained and successfully validated. Additionally, intermediate models capable of predicting the final product quality after only 50% or 75% batch progress are developed. All models provide accurate estimates of the final polymer viscosity.
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References
Aach, J., Church, G.: Aligning gene expression time series with time warping algorithms. Bioinformatics 17, 495–508 (2001)
Arteaga, F., Ferrer, A.: Dealing with missing data in MSPC: several methods, different interpretations, some examples. J. Chemometr. 16, 408–418 (2002)
Caiani, E.G., Porta, A., Baselli, G., Turiel, M., Muzzupappa, S., Pieruzzi, F., Crema, C., Malliani, A., Cerutti, S.: Warped-average template technique to track on a cycle-by-cycle basis the cardiac filling phases on left ventricular volume. IEEE Computers in Cardiology 25, 73–76 (1998)
Choi, S.W., Martin, E.B., Morris, A.J., Lee, I.-B.: Dynamic model-based batch process monitoring. Chem. Eng. Sci (2007), doi:10.1016/j.ces.2007.09.046
Dorsey, A.W., Lee, J.H.: Building inferential prediction models of batch processes using subspace identification. J. Proc. Contr. 13, 397–406 (2003)
García-Munoz, S., Kourti, T., MacGregor, J.F., Mateos, A.G., Murphy, G.: Troubleshooting of an industrial batch process using multivariate methods. Ind. Eng. Chem. Res. 42, 3592–3601 (2003)
García-Munoz, S., Kourti, T., MacGregor, J.F.: Model predictive monitoring for batch processes. Ind. Eng. Chem. Res 43, 5929–5941 (2004)
Geladi, P., Kowalski, B.R.: Partial least-squares regression: a tutorial. Anal. Chim. Acta 185, 1–17 (1986)
Gins, G.: Modelling of (bio)chemical processes using data-driven techniques, PhD Thesis, Faculteit Ingenieurswetenschappen, Katholieke Universiteit Leuven, Belgium (2007)
Itakura, F.: Minimum prediction residual principle applied to speech recognition. IEEE Trans. on Acoustics, Speech and Signal Proc. ASSP-23, 52–57 (1975)
Kassidas, A., MacGregor, J.F., Taylor, P.A.: Synchronization of batch trajectories using dynamic time warping. AIChE J. 44(4), 864–875 (1998)
Keogh, E.J., Pazzani, M.J.: Derivative dynamic time warping. In: First SIAM International Conference on Data Mining, Chicago, IL, 2001 (2001)
Kourti, T., Nomikos, P., MacGregor, J.F.: Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS. J. Proc. Contr. 5, 277–284 (1995)
Kourti, T.: Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions. J. Chemometr. 17, 93–109 (2003)
Lee, J.H., Dorsey, A.W.: Monitoring of batch processes through state-space models. AIChE J. 50(6), 1198–1210 (2004)
Lu, N., Yao, Y., Gao, F.: Two-dimensional dynamic pca for batch process monitoring. AIChE J. 51(12), 3300–3304 (2005)
McCready, C.: Model predictive multivariate control. In: Proc. 2nd European Conference on Process Analytics and Control Technology, p. 82 (2011)
Nomikos, P., MacGregor, J.F.: Monitoring of batch processes using multi-way principal component analysis. AIChE J. 40(8), 1361–1375 (1994)
Nomikos, P., MacGregor, J.F.: Multivariate SPC charts for monitoring batch processes. Technometr. 37(1), 41–59 (1995)
Nomikos, P., MacGregor, J.F.: Multiway partial least squares in monitoring batch processes. Chemometr. Intell. Lab. Syst. 30, 97–108 (1995)
Ramaker, H.-J., Van Sprang, E.N.M., Westerhuis, J.A., Smilde, A.K.: Dynamic time warping of spectroscopic BATCH data. Anal. Chim. Acta 498, 133–153 (2003)
Ratanamahatana, C.A., Keogh, E.J.: Three myths about dynamic time warping. In: Proceedings of SIAM International Conference on Data Mining (SDM 2005), Newport Beach, California, USA, pp. 506–510 (2005)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. on Acoustics, Speech, and Signal Proc. ASSP-26(1), 43–49 (1978)
Wold, S., Geladi, P., Ebensen, K., Öhman, J.: Multi-way principal components- and PLS-analysis. J. Chemometr. 1(1), 41–56 (1987)
Tracy, N., Young, J., Mason, R.: Multivariate control charts for individual observations. J. Qual. Technol. 24(2), 88–95 (1992)
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Gins, G., Pluymers, B., Smets, I.Y., Espinosa, J., Van Impe, J.F.M. (2011). Prediction of Batch-End Quality for an Industrial Polymerization Process. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2011. Lecture Notes in Computer Science(), vol 6870. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23184-1_24
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DOI: https://doi.org/10.1007/978-3-642-23184-1_24
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