### Abstract

Operating distillation columns under control requires inferring the compositions of the distillate and bottom streams (which are challenging to measure) from other more easily measured variables, such as temperatures at different trays of the column. Models that can be used in this regard are called inferential models. Commonly used inferential models include latent variable regression (LVR) techniques, such as principal component regression (PCR), partial least square (PLS), and regularized canonical correlation analysis (RCCA). Unfortunately, measured practical data are usually contaminated with errors, which degrade the prediction accuracy of inferential models. Therefore, noisy measurements need to be filtered to enhance the prediction ability of these models. Wavelet-based multiscale filtering has been shown to be a powerful denoising tool. In this work, the advantages of multiscale filtering are utilized to enhance the prediction accuracy of LVR models by developing an integrated multiscale LVR (IMSLVR) modeling algorithm that integrates modeling and filtering. The idea behind the IMSLVR modeling algorithm is to filter the process data at different decomposition levels, model the filtered data from each level, and then select the LVR model that optimizes a model selection criterion. The performance of the developed IMSLVR algorithm is illustrated using two examples, one using synthetic data and the other using simulated distillation column data. Both examples clearly demonstrate the effectiveness of the IMSLVR algorithm.

Original language | English |
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Title of host publication | Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Control and Automation, CICA 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 |

Pages | 73-80 |

Number of pages | 8 |

DOIs | |

Publication status | Published - 2013 |

Event | 2013 3rd IEEE Symposium on Computational Intelligence in Control and Automation, CICA 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 - Singapore, Singapore Duration: 16-04-2013 → 19-04-2013 |

### Conference

Conference | 2013 3rd IEEE Symposium on Computational Intelligence in Control and Automation, CICA 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 |
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Country | Singapore |

City | Singapore |

Period | 16-04-13 → 19-04-13 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Artificial Intelligence
- Computer Science Applications

### Cite this

*Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Control and Automation, CICA 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013*(pp. 73-80). [6611666] https://doi.org/10.1109/CICA.2013.6611666

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*Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Control and Automation, CICA 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013.*, 6611666, pp. 73-80, 2013 3rd IEEE Symposium on Computational Intelligence in Control and Automation, CICA 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, Singapore, Singapore, 16-04-13. https://doi.org/10.1109/CICA.2013.6611666

**Enhanced modeling of distillation columns using integrated multiscale latent variable regression.** / Madakyaru, Muddu; Nounou, Mohamed N.; Nounou, Hazem N.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Enhanced modeling of distillation columns using integrated multiscale latent variable regression

AU - Madakyaru, Muddu

AU - Nounou, Mohamed N.

AU - Nounou, Hazem N.

PY - 2013

Y1 - 2013

N2 - Operating distillation columns under control requires inferring the compositions of the distillate and bottom streams (which are challenging to measure) from other more easily measured variables, such as temperatures at different trays of the column. Models that can be used in this regard are called inferential models. Commonly used inferential models include latent variable regression (LVR) techniques, such as principal component regression (PCR), partial least square (PLS), and regularized canonical correlation analysis (RCCA). Unfortunately, measured practical data are usually contaminated with errors, which degrade the prediction accuracy of inferential models. Therefore, noisy measurements need to be filtered to enhance the prediction ability of these models. Wavelet-based multiscale filtering has been shown to be a powerful denoising tool. In this work, the advantages of multiscale filtering are utilized to enhance the prediction accuracy of LVR models by developing an integrated multiscale LVR (IMSLVR) modeling algorithm that integrates modeling and filtering. The idea behind the IMSLVR modeling algorithm is to filter the process data at different decomposition levels, model the filtered data from each level, and then select the LVR model that optimizes a model selection criterion. The performance of the developed IMSLVR algorithm is illustrated using two examples, one using synthetic data and the other using simulated distillation column data. Both examples clearly demonstrate the effectiveness of the IMSLVR algorithm.

AB - Operating distillation columns under control requires inferring the compositions of the distillate and bottom streams (which are challenging to measure) from other more easily measured variables, such as temperatures at different trays of the column. Models that can be used in this regard are called inferential models. Commonly used inferential models include latent variable regression (LVR) techniques, such as principal component regression (PCR), partial least square (PLS), and regularized canonical correlation analysis (RCCA). Unfortunately, measured practical data are usually contaminated with errors, which degrade the prediction accuracy of inferential models. Therefore, noisy measurements need to be filtered to enhance the prediction ability of these models. Wavelet-based multiscale filtering has been shown to be a powerful denoising tool. In this work, the advantages of multiscale filtering are utilized to enhance the prediction accuracy of LVR models by developing an integrated multiscale LVR (IMSLVR) modeling algorithm that integrates modeling and filtering. The idea behind the IMSLVR modeling algorithm is to filter the process data at different decomposition levels, model the filtered data from each level, and then select the LVR model that optimizes a model selection criterion. The performance of the developed IMSLVR algorithm is illustrated using two examples, one using synthetic data and the other using simulated distillation column data. Both examples clearly demonstrate the effectiveness of the IMSLVR algorithm.

UR - http://www.scopus.com/inward/record.url?scp=84886044183&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84886044183&partnerID=8YFLogxK

U2 - 10.1109/CICA.2013.6611666

DO - 10.1109/CICA.2013.6611666

M3 - Conference contribution

AN - SCOPUS:84886044183

SN - 9781467358934

SP - 73

EP - 80

BT - Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Control and Automation, CICA 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013

ER -