On-line application oriented optimal scheduling for penicillin fed-batch fermentation

Yuan, Jun; Xue, Yaofeng; Hu, Kejia; Wu, Hongtao; Jia, Q.

doi:10.1016/j.cep.2008.07.010

Cited by 7 publications

(7 citation statements)

References 18 publications

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“…Optimization of production scheduling in such bio-processes has been studied e.g. in [32] for penicillin production and in [33] for intra-cellular enzyme production.…”

Section: Problem Descriptionmentioning

confidence: 99%

A discrete time exact solution approach for a complex hybrid flow-shop scheduling problem with limited-wait constraints

Gicquel

Hege

Minoux

et al. 2012

Computers & Operations Research

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a b s t r a c tWe study a real-world complex hybrid flow-shop scheduling problem arising from a bio-process industry. There are a variety of constraints to be taken into account, in particular zero intermediate capacity and limited waiting time between processing stages. We propose an exact solution approach for this optimization problem, based on a discrete time representation and a mixed-integer linear programming formulation. The proposed solution algorithm makes use of a new family of valid inequalities exploiting the fact that a limited waiting time is imposed on jobs between two successive production stages. The results of our computational experiments confirm that the proposed method produces good feasible schedules for industrial instances.

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“…Optimization of production scheduling in such bio-processes has been studied e.g. in [32] for penicillin production and in [33] for intra-cellular enzyme production.…”

Section: Problem Descriptionmentioning

confidence: 99%

A discrete time exact solution approach for a complex hybrid flow-shop scheduling problem with limited-wait constraints

Gicquel

Hege

Minoux

et al. 2012

Computers & Operations Research

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“…For the sake of optimal scheduling, the prediction error in the later phase of cultivations is of special interest [5]. Therefore, in the following discussion, the prediction error in the later phase of each batch is considered.…”

Section: X+1)mentioning

confidence: 99%

“…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.…”

mentioning

confidence: 99%

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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“…coli fermentation processes. Yuan et al formulated the optimization of cultivation periods in fed‐batch fermentation of penicillin.…”

Section: Introductionmentioning

confidence: 99%

“…The objectives of a scheduling approach may vary, such as maximizing product formation, minimizing the makespan, minimizing production costs, or maximizing profit . For a given production facility and all necessary resources, the goal a of 2‐keto‐L‐gulonic acid (2‐KGA) fermentation workshop is to obtain the maximum yield at a minimum production cost.…”

Section: Introductionmentioning

confidence: 99%

Online Application‐Oriented Optimal Scheduling for 2‐keto‐l‐gulonic Acid Production

Cui

et al. 2015

Can J Chem Eng

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An optimal scheduling approach for the 2‐keto‐L‐gulonic acid (2‐KGA) fermentation process is proposed to improve allocation of L‐sorbose resources with the aim of profit maximization in a multi‐bioreactor workshop. The empirical operation in 2‐KGA cultivation under study is to assign the same quantity of L‐sorbose to each batch without taking batch‐to‐batch variations into account, while the optimal scheduling approach presented in this paper will determine L‐sorbose feeding according to the evaluation of the profit‐making ability of the individual batch. Each 2‐KGA batch is classified online according to the prediction of the profit function, so that each batch is assigned into different profit‐making categories. Pseudo‐online scheduling is implemented with the data of industrial 2‐KGA cultivations. A total profit increase of 6–7 % is found to be achievable for the workshop in comparison with the empirical operation.

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On-line application oriented optimal scheduling for penicillin fed-batch fermentation

Cited by 7 publications

References 18 publications

A discrete time exact solution approach for a complex hybrid flow-shop scheduling problem with limited-wait constraints

A discrete time exact solution approach for a complex hybrid flow-shop scheduling problem with limited-wait constraints

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

Online Application‐Oriented Optimal Scheduling for 2‐keto‐l‐gulonic Acid Production

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