Dynamic neural networks as a tool for the online optimization of industrial fermentation

Becker, Thomas; Enders, T.; Delgado, Antonio

doi:10.1007/s004490100242

Cited by 37 publications

(1 citation statement)

References 16 publications

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“…The application of artificial neural networks (ANN) has already become commonplace in modelling as well as in the optimization of several processes in technical studies [1,2]. It is also increasingly becoming a subject of interest to scientists engaged in issues concerning animal breeding and use and optimal nutrition [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Utility Value of Hucul Horses by Means of Artificial Intelligence

Topczewska

Kwater

2020

Sustainability

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The paper suggests the application of artificial neural networks (ANN) for the analysis of variables that significantly impact on the results of Hucul horses that participate at the National Breeding and Utility Championships for Hucul horses. The study exploits the results obtained during 2009–2015. The research material collected enabled the creation of a set of input data (for the artificial neural network), out of which independent learning and testing sets were isolated. The neural classification system in form of a multi-layered artificial neural network suggested in this paper was implemented in the programming environment Matlab, the 8.1.0.604 version. Each horse was described using features in three models. Experimental simulations were carried out separately for each model, conducting the learning and testing simulation process 10 times. In accepting the division of the evaluated group of horses into 10 classes for the analysis of the issue both the expert and network designated the classes, not without reservations due to imprecision of demarcations. The increase in class numbers would result in increased accuracy of selection (allocation to varied classes) of individuals. The average for 10 network responses which was 77% suggest an identical or a very similar horse class when compared with the expert’s value. Preliminary results of the application of artificial neural networks in predicting the utility value of Hucul horses, relying on a specific set of features seem promising.

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Section: Introductionmentioning

confidence: 99%

Forecasting the Utility Value of Hucul Horses by Means of Artificial Intelligence

Topczewska

Kwater

2020

Sustainability

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Prediction of the Profit Function for Industrial 2‐Keto‐L‐Gulonic Acid Cultivation

Cui

Jia³

et al. 2011

Chem Eng & Technol

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The profit function is the generic criterion to describe the cost effect of a batch process. To focus on the prediction of the profit function for 2-keto-L-gulonic acid (2-KGA) cultivation, which is potentially applicable for process monitoring and optimal scheduling, rolling learning-prediction (RLP) based on a support vector machine (SVM) is applied. The RLP implies that the SVM training database is rolling updated as the batch of current interest proceeds, and the SVM learning is then repeated for the prediction. The database is further updated after termination of a batch. The updating procedures are investigated in detail. Pseudo-online prediction is carried out using the data from industrial-scale 2-KGA cultivation under actual and hypothetical inoculation sequences. The results indicate that the average relative prediction error is less than 5 % in the later phase of fermentation in all inoculation sequences. Introduction2-Keto-L-gulonic acid (2-KGA) is the precursor for the synthesis of L-ascorbic acid. It is typically produced by a mixed culture of Bacillus megaterium (B. megaterium) and Gluconobacter oxydans (G. oxydans) with L-sorbose as substrate [1][2][3]. In 2-KGA fermentation workshops, batch-to-batch variation in process performance is often observed, even though the same culture conditions are applied. This may result from many factors such as fluctuations in the seed quality and raw materials composition. For control engineers, batch-dependent variation between batches implies the potential to improve the process performance by dynamic optimization or optimal scheduling [4][5][6][7][8]. The profit function, which is defined as the gross profit of a batch over the production time, reflects the cost effect of the batch process and can be used as the index to evaluate the batch performance. Predicting the profit function will benefit process monitoring and optimal scheduling. Here, the focus is on the data-driven prediction of the profit function.In recent years, computational intelligence techniques such as artificial neural networks (ANNs) [9][10][11][12][13][14] and support vector machine (SVM) [14][15][16][17][18][19] received more and more attention because of their ability to model nonlinear systems. SVM, developed by Vapnik, is based on the statistical theory [20,21]. It implements the structural risk minimization (SRM) principle which is an approach to minimize the upper bound risk related to the generalization performance. The solid theoretical basis makes it advantageous in generalization and convergence [19][20][21]. In this work, rolling learning-prediction (RLP) based on an SVM is practiced to predict the profit function of industrial-scale 2-KGA cultivation. The RLP mechanism is adopted to deal with the time-variant property of fermentation, and it makes full use of the available data of the predicted batch by adding these real-time data into the training database of SVM to start a new round of SVM learning. When the prediction approach is online operated, new batches will arrive in ...

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Optimierung eines Fermentationsprozesses mit Data Mining‐Methoden

Rommel

Schuppert

2003

Chemie Ingenieur Technik

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Dynamic neural networks as a tool for the online optimization of industrial fermentation

Cited by 37 publications

References 16 publications

Forecasting the Utility Value of Hucul Horses by Means of Artificial Intelligence

Forecasting the Utility Value of Hucul Horses by Means of Artificial Intelligence

Prediction of the Profit Function for Industrial 2‐Keto‐L‐Gulonic Acid Cultivation

Optimierung eines Fermentationsprozesses mit Data Mining‐Methoden

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