Statistical analysis of material balance of a chemical reactor

Madron, František; Veverka, Vladimír; Vaněček, V.

doi:10.1002/aic.690230412

Cited by 55 publications

(20 citation statements)

References 7 publications

(4 reference statements)

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“…Although conversion rates ought to satisfy all elemental balances, measured rates never do because of gross and statistical measurement errors. Methods have been described in the literature, which use the elemental balances as further constraints to 1) determine whether gross measurement errors are present or whether unidentified metabolites were produced (or consumed), which have not been included in the stoichiometric black box description; 2) calculate those conversion rates that were not measured; and 3) to reconcile the rate data in order to eliminate the elemental balance errors (De Kok and Roels, 1980;Madron, 1977;van der Heijden et al, 1994b).…”

Section: Data Consistencymentioning

confidence: 99%

On‐line stoichiometry and identification of metabolic state under dynamic process conditions

Herwig

Marison

Stockar

2001

Biotech & Bioengineering

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A method for the on-line calculation of conversion rates and yield coefficients under dynamic process conditions was developed. The method is based on cumulated mass balances using a moving average method. Elemental balances were used to test the measured cumulated quantities for gross errors and inappropriate stoichiometry definition followed by data reconciliation and estimation of non-measured conversion rates, using a bioprocess set-up including multiple on-line analysis techniques. The quantitative potential of the proposed method is demonstrated by executing transient experiments in aerobic cultures of Saccharomyces cerevisiae on glucose. Rates and yield coefficients could be consistently quantified in shift-up, shift-down, and accelerostat experiments. The method shows the capability to describe quantitatively transient changes in metabolism including uncoupling of catabolism and anabolism, also for the case when multiple components of metabolism are not measured. The validity of the experiment can be evaluated on-line. Additionally, the method detects with high sensitivity inappropriate stoichiometry definition, such as a change in state of metabolism. It was shown that concentration values can be misleading for the identification of the metabolic state. In contrast, the proposed method provides a clear picture of the metabolic state and new physiological regulations could be revealed. Hence, the novelty of the proposed method is the on-line availability of consistent stoichiometric coefficients allowing a significant speed up in strain characterization and bioprocess development using minimal knowledge of the metabolism. Additionally, it opens up the use of transient experiments for physiological studies.

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Section: Data Consistencymentioning

confidence: 99%

On‐line stoichiometry and identification of metabolic state under dynamic process conditions

Herwig

Marison

Stockar

2001

Biotech & Bioengineering

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“…1A), a simple calibration model, containing only a basic set of a few standards, is used to estimate the concentration profiles (C S t ) of S metabolites measured by the optical sensor system. Methods have been described in the literature, which used elemental balances as further constraints to determine measurement errors (Herwig et al, 2001;Madron et al, 1977;van der Heijden et al, 1994;Wang and Stephanopoulos, 1983). These methods imply the transformation of measured concentration into number of C-moles exchanged in the system.…”

Section: Description Of the Algorithm For The Plsr Calibration Model mentioning

confidence: 99%

Real‐time update of calibration model for better monitoring of batch processes using spectroscopy

Kornmann¹,

Valentinotti²,

Marison³

et al. 2004

Biotech & Bioengineering

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In order to reduce the large calibration matrix usually required for calibrating multiwavelength optical sensors, a simple algorithm based on the addition in process of new standards is proposed. A small calibration model, based on 14 standards, is periodically updated by spectra collected on-line during fermentation operation. Concentrations related to these spectra are reconciled into best-estimated values, by considering carbon and oxygen balances. Using this method, fructose, acetate, and gluconacetan were monitored during batch fermentations of Gluconacetobacter xylinus 12281 using mid-infrared spectroscopy. It is shown that this algorithm compensates for noncalibrated events such as production or consumption of by-products. The standard error of prediction (SEP) values were 0.99, 0.10, and 0.90 g/L for fructose, acetate, and gluconacetan, respectively. By contrast, without an updating of the calibration model, the SEP values were 2.46, 0.92, and 1.04 g/L for fructose, acetate, and gluconacetan, respectively. Using only 14 standards, it was therefore possible to approach the performance of an 88-standard-based calibration model having SEP values of 1.11, 0.37, and 0.79 g/L for fructose, acetate, and gluconacetan, respectively. Therefore, the proposed algorithm is a valuable approach to reduce the calibration time of multiwavelength optical sensors.

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“…It is a straightforward argument (Madron et al, 1977) to show that when the data contain small random errors only, the differences in reconciled values obtained using the two sets of regression variables should be of little practical significance. Such may not be the case, however, when the data also contain gross errors.…”

Section: Introductionmentioning

confidence: 99%

“…These two sets of variables will be referred to here as primary and secondary variables, respectively. Likewise, it is possible to formulate the data reconciliation problem either in terms of primary variables (e.g., Serth et al, 1987;MacDonald and Howat, 1988) or secondary variables (e.g., Madron et al, 1977;Crowe, 1986). However, there is a cogent reason for using the latter variables.…”

Section: Introductionmentioning

confidence: 99%

On the Choice of Regression Variables in Nonlinear Data Reconciliation

Serth

Weart

Heenan

1989

Chemical Engineering Communications

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Reconciliation of process data constrained by multicomponent material balance equations can be accomplished by a regression calculation in which either total flow rates and compositions (primary variables) or total and component flow rates (secondary variables) are used as the regression variables. It is shown by means of computer simulation experiments that the choice of secondary variables for regression can adversely affect the performance of an associated gross error detection algorithm. The principal effect is an increased tendency of the algorithm to make Type I errors.

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Statistical analysis of material balance of a chemical reactor

Cited by 55 publications

References 7 publications

On‐line stoichiometry and identification of metabolic state under dynamic process conditions

On‐line stoichiometry and identification of metabolic state under dynamic process conditions

Real‐time update of calibration model for better monitoring of batch processes using spectroscopy

On the Choice of Regression Variables in Nonlinear Data Reconciliation

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