Improved monitoring of batch processes by incorporating external information

Ramaker, Henk‐Jan; Sprang, E.N.M. van; Gurden, Stephen P.; Westerhuis, Johan A.; Smilde, Age K.

doi:10.1016/s0959-1524(01)00022-1

Cited by 39 publications

(22 citation statements)

References 32 publications

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“…Specifically, batch process monitoring can be divided into three phases: initial, training and application phases, as described by Ramaker et al [40].…”

Section: Monitoring Stepmentioning

confidence: 99%

Fault diagnosis of a benchmark fermentation process: a comparative study of feature extraction and classification techniques

Monroy

Villez

Graells

et al. 2011

Bioprocess Biosyst Eng

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This paper investigates fault diagnosis in batch processes and presents a comparative study of feature extraction and classification techniques applied to a specific biotechnological case study: the fermentation process model by Birol et al. (Comput Chem Eng 26:1553-1565, 2002), which is a benchmark for advanced batch processes monitoring, diagnosis and control. Fault diagnosis is achieved using four approaches on four different process scenarios based on the different levels of noise so as to evaluate their effects on the performance. Each approach combines a feature extraction method, either multi-way principal component analysis (MPCA) or multi-way independent component analysis (MICA), with a classification method, either artificial neural network (ANN) or support vector machines (SVM). The performance obtained by the different approaches is assessed and discussed for a set of simulated faults under different scenarios. One of the faults (a loss in mixing power) could not be detected due to the minimal effect of mixing on the simulated data. The remaining faults could be easily diagnosed and the subsequent discussion provides practical insight into the selection and use of the available techniques to specific applications. Irrespective of the classification algorithm, MPCA renders better results than MICA, hence the diagnosis performance proves to be more sensitive to the selection of the feature extraction technique.

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“…Specifically, batch process monitoring can be divided into three phases: initial, training and application phases, as described by Ramaker et al [40].…”

Section: Monitoring Stepmentioning

confidence: 99%

Fault diagnosis of a benchmark fermentation process: a comparative study of feature extraction and classification techniques

Monroy

Villez

Graells

et al. 2011

Bioprocess Biosyst Eng

View full text Add to dashboard Cite

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“…The suggested method, however, is restricted to (pseudo) first-order kinetic models, since concentration profiles have to be written as a sum of exponentially decaying terms and author's goal was only to estimate accurately kinetic parameters. There are also some reports on the use of hard constraint in restricted Tucker3 models [26][27][28][29]. However, in our knowledge, there are no reports on applying hard modeling on some concentration profiles to reduce rotational ambiguity in the PARAFAC solution.…”

Section: Research Articlementioning

confidence: 99%

Hard–soft modeling parallel factor analysis to solve equilibrium processes

Sajjadi

Abdollahi

2011

Journal of Chemometrics

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PARAFAC model is the most famous model for analyzing three-way data. However, this method does not converge to chemically meaningful solutions when applied to three-way problems involving rank overlap profiles at least in one mode. Rank overlap can be simply found where components have similar spectral profiles or analytes appearing in identical proportions throughout an experiment. However, an appropriate selection of the initial parameters and constraints such as non-negativity and unimodality can still make PARAFAC model useful in this regard. Although such constraints reduce rotational freedom in PARAFAC solution, they are generally insufficient to wholly eliminate the rotational problem. The goal of the present paper is to incorporate hard modeling constraint in the soft-modeled PARAFAC algorithm to overcome non-uniqueness problem in the equilibrium processes involving linearly dependent factors at least in one mode. The hard constraint is introduced to force some or all of the concentration profiles to fulfill an equilibrium model that is refined at each iteration cycle of the optimization process of PARAFAC. The proposed approach is called hard-soft PARAFAC (HSPARAFAC). When the rank overlap species obeys equilibrium model in HSPARAFAC, the unique results are obtained even in the presence of non-modeled interferences. The new modification in the treatment of equilibrium data sets yields more satisfactory results than the exclusive PARAFAC algorithm. Simulated and real examples with rank overlap problem are used to confirm this statement.

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“…In the context of grey modelling, there are different approaches to decompose the data into the three types of variation (known causes, unknown causes and residuals) [17]. In the previous work [13], a model based on known constraints was imposed.…”

Section: Introductionmentioning

confidence: 99%

MCR-ALS on metabolic networks: Obtaining more meaningful pathways

Folch-Fortuny

Tortajada

Prats-Montalbán

et al. 2015

Chemometrics and Intelligent Laboratory Systems

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With the aim of understanding the flux distributions across a metabolic network, i.e. within living cells, Principal Component Analysis (PCA) has been proposed to obtain a set of orthogonal components (pathways) capturing most of the variance in the flux data. The problems with this method are (i) that no additional information can be included in the model, and (ii) that orthogonality imposes a hard constraint, not always reasonably. To overcome these drawbacks, here we propose to use a more flexible approach such as Multivariate Curve Resolution -Alternating Least Squares (MCR-ALS) to obtain this set of biological pathways through the network. By using this method, different constraints can be included in the model, and the same source of variability can be present in different pathways, which is reasonable from a biological standpoint. This work follows a methodology developed for Pichia pastoris cultures grown on different carbon sources, lately presented in [González Martínez et al. 2014].In this paper a different grey modelling approach, which aims to incorporate a priori knowledge through constraints on the modelling algorithms, is applied to the same case of study. The results of both models are compared to show their strengths and weaknesses.

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Improved monitoring of batch processes by incorporating external information

Cited by 39 publications

References 32 publications

Fault diagnosis of a benchmark fermentation process: a comparative study of feature extraction and classification techniques

Fault diagnosis of a benchmark fermentation process: a comparative study of feature extraction and classification techniques

Hard–soft modeling parallel factor analysis to solve equilibrium processes

MCR-ALS on metabolic networks: Obtaining more meaningful pathways

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