Process monitoring of an industrial fed‐batch fermentation

Lennox, Barry; Montague, G.A.; Hiden, Hugo; Kornfeld, G.; Goulding, Peter R.

doi:10.1002/bit.1102

Cited by 149 publications

(70 citation statements)

References 32 publications

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“…Since then other researchers have adopted the approach and applied it to a variety of processes. For example, Gallagher et al (1996) applied the technique to monitor nuclear waste storage vessels and Lennox et al (2001) and Lakshminarayanan et al (1996) investigated the detection of faults in fed-batch fermentation processes.…”

Section: Multiway Partial Least Squaresmentioning

confidence: 99%

“…The prediction of future process values is referred to as filling up the array. Of the three methods that were suggested by Nomikos and MacGregor (1994) for filling up the array, Lennox et al (2001) found that the most appropriate method for an industrial fed-batch process was to assume that the values of all process measurements remain at their current offset from the mean trajectory through to the end of the batch. Whilst the most suitable filling up method is likely to be process dependent, this method was also found to be the most appropriate in this work.…”

Section: Figure 1 Unfoldingmentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Study of Soft-Sensing Methods for Fed-Batch Fermentation Processes

Zhang¹,

Zouaoui²,

Lennox

2005

IFAC Proceedings Volumes

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A comparative study of software sensors using Multiway Partial Least Squares and Extended Kalman Filters in an application to a fed-batch yeast fermentation process is presented. The MPLS theory is introduced firstly and then applied to a yeast fed-batch fermentation process to provide soft-sensing facilities. The soft-sensing capabilities of the MPLS approach are found to compare favourably with the results using EKF.

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Section: Multiway Partial Least Squaresmentioning

confidence: 99%

Section: Figure 1 Unfoldingmentioning

confidence: 99%

A Comparative Study of Soft-Sensing Methods for Fed-Batch Fermentation Processes

Zhang¹,

Zouaoui²,

Lennox

2005

IFAC Proceedings Volumes

View full text Add to dashboard Cite

show abstract

“…Batch data consist of batch runs, variables, and time, contrary to the continuous processes where only time and variables are involved. Multiway PCA has also been subject to some application studies [17]. The fact that multiway PCA was a fixed model monitoring technique and thus might lead to false alarms when it was used to monitor real processes whose normal operation involved slow changes, led Lee et al [9] to a consecutively updated multiway PCA.…”

Section: Introductionmentioning

confidence: 98%

On‐line Statistical Process Monitoring and Fault Diagnosis in Batch Baker's Yeast Fermentation

et al. 2009

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This study involves real-time monitoring and fault diagnosis in batch baker's yeast fermentation. A specific Real Time Statistical Process Analysis and Control (RT-SPAC) program was developed to monitor instantaneous reaction conditions. The air flow rate fed to the reactor, temperature, pH, and dissolved oxygen concentration in a laboratory-size fermenter were monitored and recorded by means of on-line sensors. Under control of the RT-SPAC program, 22 batch baker's yeast fermentation operations were carried out. In the first 20 operations, an ordinary process was followed under previously defined nominal operating conditions. Historical data collected from these batches were then used for online Dynamic Principal Component Analysis (DPCA) in the course of the following two batches. The last two batches were implemented such that some deliberate faults (in temperature and pH) were introduced during the operation. The results indicated that the software was capable of capturing the process faults, and furthermore the possible causes of these faults were identified by contribution plots.

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“…These methods integrate the time-lagged windows of the process dynamic behavior with PCA and PLS respectively for on-line batch monitoring. Of the methods mentioned above, the application of MPCA to industrial batch monitoring has received the greatest attention (Gallagher and Wise, 1996;Kosanovich et al, 1996;Nomikos, 1996;Gregersen and Jorgensen, 1999;Tates et al, 1999;Westerhuis et al, 1999;Wise et al, 1999;Lennox et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

On-line Batch Process Monitoring Using Different Unfolding Method and Independent Component Analysis.

Lee

2003

J. Chem. Eng. Japan

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In many industries, the effective monitoring and control of batch processes is crucial to the production of high-quality materials. Several techniques using multivariate statistical analysis have been developed for monitoring and fault detection of batch processes. Multiway principal component analysis (MPCA) has shown a powerful monitoring performance in many industrial batch processes. However, it has shortcomings that all batch lengths should be equalized and future values of batches should be estimated for on-line monitoring. In order to overcome these drawbacks and obtain better monitoring performance, we propose a new statistical method for on-line batch process monitoring that uses different unfolding method and independent component analysis (ICA). If the measured data set contains non-Gaussian latent variables, the ICA solution can extract the original source signal to a much greater extent than the PCA solution since ICA involves higher-order statistics and is not based on the assumption that the latent variables follow a multivariate Gaussian distribution. The proposed monitoring method was applied to fault detection and identification in the simulation benchmark of the fed-batch penicillin production, which is characterized by some fault sources with non-Gaussian characteristics. The simulation results clearly show the power and advantages of the proposed method in comparison to MPCA.

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Process monitoring of an industrial fed‐batch fermentation

Cited by 149 publications

References 32 publications

A Comparative Study of Soft-Sensing Methods for Fed-Batch Fermentation Processes

A Comparative Study of Soft-Sensing Methods for Fed-Batch Fermentation Processes

On‐line Statistical Process Monitoring and Fault Diagnosis in Batch Baker's Yeast Fermentation

On-line Batch Process Monitoring Using Different Unfolding Method and Independent Component Analysis.

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