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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.

A complete mass transfer model was established for an internal loop airlift reactor. All sections of the reactor, including bottom, riser, top, and downcomer, were taken into account. A numerical method was developed to solve the mass transfer model. The effectiveness of the model was validated by experimental measurements. Based on numerical results of the model, the dynamical mass transfer process in the reactor was analyzed in detail. Some mass transfer characteristics of the airlift reactor were revealed. The results indicated that the proposed model well predicted the mass transfer in the internal loop airlift reactor.

The present work is initiated to investigatewhether a defined culture comprising a mixture of threeyeast species, Kluyveromyces marxianus, Saccharomyces cer-evisiae, and Pichia stipitis can ferment a mixture of sugars toproduce bioethanol at rates higher than those achieved bypure cultures of the same. For this purpose, we developmodels of single species based on the hybrid cyberneticmodel framework, and simulate fermentations in the mixedculture by combining individual models. An underlyingassumptionisthatthebehaviorofeachspeciesisdeterminedonly by the common environment independently of thepresence and metabolism of other species. Model perfor-mance is thoroughly assessed using the experimental dataavailable in the literature. The dynamic behavior of mixedcultures in mixed culture experiments are accurately pre-dicted by the model reflecting faithfully the simultaneous/sequentialuptakepatternsofmixedsubstrates.Thismodelisthen used to investigate performance of various possiblereactor configurations. With the foregoing species of organ-isms, mixed culture itself does not lead to a significantincrease of bioethanol productivity. Rather, the modelshows that substantial improvement is acquired by sequen-tial use of different, properly chosen organisms duringfermentation. Thus, the successive use of K. marxianusand P. stipitis is shown to increase bioethanol productivityup to about 58% in comparison to fermentation by singlespecies alone. Biotechnol. Bioeng.Biotechnol. Bioeng. 2012;109: 1508–1517. 2012 Wiley Periodicals, Inc.KEYWORDS: hybrid cybernetic model (HCM); elementarymodes (EMs); mixed culture; lignocellulose; bioethanolproductivity

The origin and coupling of pressure fluctuations in an internal loop airlift bioreactor are investigated. The pressure fluctuations in the reactor are divided into two categories: global pressure fluctuations and local pressure fluctuations. It is found that the coupling between global pressure fluctuations and local pressure fluctuations mainly focuses in the frequency region between 10 and 30 Hz. Local pressure fluctuations in the reactor are strongly affected by pressure waves originating from the air-supply system, while pressure fluctuations caused by the bubble eruption at the liquid surface have less influence on local pressure fluctuations. Based on the coherence analysis, the pressure signal at a certain position in the reactor is decomposed into three different parts: coherent part, joint incoherent part and exclusive incoherent part. The energy ratios of these different parts are helpful to study the interaction among pressure fluctuations from different sources. Three flow regimes were identified from the evolution of the energy ratio of the joint incoherent part. © 2011 Canadian Society for Chemical Engineering

Air-lift bioreactor (ABR) are applied in the pharmaceutical industry for those processes of low-oxygen demand. The characteristics of the air bubbles in an ABR are important since they influence the overall performance of the bioreactor. In this article, two-dimensional CFD simulations are carried out to portray the flow characteristics in an annulus-sparged ABR with a low-aeration rate, which is a scaled geometry of a 200 m3 annulus-sparged ABR for the L-ascorbic acid manufacturing. VOF model is applied firstly to capture the flow behaviour of the bubbles in the bioreactor, and the distributions of the bubbles and local mean bubble sizes are approximately obtained. With the local mean bubble size distribution, Eulerian model is adopted to investigate the performance of the bioreactor. The liquid velocity and the gas holdup are further discussed. The results show the existence of the local back mixing as well as the mal-distributions of the velocity and the gas holdup along the radial direction in the annulus-sparged ABR. Des bioreacteurs a agitation pneumatique sont mis en application dans l'industrie pharmaceutique pour les procedes a demande en oxygene faible. Les caracteristiques des bulles d'air dans un bioreacteur a agitation pneumatique sont importantes, puisqu'elles influencent le rendement general du bioreacteur. Dans cet article, des simulations de dynamique des fluides numerique bidimensionnelles sont realisees pour representer les caracteristiques de debit d'un bioreacteur a agitation a barbotage annulaire avec un faible taux d'aeration, qui est une geometrie a l'echelle d'un bioreacteur a agitation a barbotage annulaire de 200 m3 pour la fabrication d'acide ascorbique. Le modele de volume de fluide est applique en premier pour capter le comportement de debit des bulles dans le bioreacteur et les distributions des bulles et on obtient une approximation des tailles de bulles moyennes locales. Avec la distribution des tailles de bulles moyennes locales, on adopte un modele eulerien pour analyser le rendement du bioreacteur. On discute davantage de la vitesse du liquide et du volume mort. Les resultats demontrent l'existence du melange a contre-courant local ainsi que des mauvaises repartitions de la vitesse et du volume mort dans la direction radiale dans le bioreacteur a agitation a barbotage annulaire.

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.

This paper deals with the problem of H∞ control of linear discrete-time systems with time-varying delay. In order to highlight the relations between all combinations of delayed states, a general Lyapunov–Krasovskii functional is introduced, from which the relation between the switched system transformation and the classical Lyapunov–Krasovskii method is established. The basic idea is to formulate the original time-delay systems as switched systems with arbitrary switching. Therefore, the stability and the H∞ performance problems can be reduced to the corresponding problems of such switched systems. This fact bridges two distinct research fields. Finally, numerical examples are given to illustrate the effectiveness of the method and its advantage over existing results. Copyright © 2009 John Wiley & Sons, Ltd.

Structured packed columns are widely used in the chemical industry for distillation and absorption. However, the understanding of the transfer mechanism behind the counter-current gas-liquid flow in structured packed columns is still limited. In this work, a three-dimensional CFD model that considers the local absorption and the local momentum transfer mechanism is developed for a film flow on a small plate with a counter-current gas flow. The model, based on the Volume of Fluid (VOF) method, is built up on the basis of a pressure drop model and the penetration theory to quantitatively investigate the instantaneous hydrodynamics and mass transfer characteristics of the liquid phase. Simulations and experiments are carried out for a system consisting of propane and toluene. A comparison of the simulation results with the experimental data for the outlet concentrations shows good agreement.

The design of packed columns requires the detailed description of the hydrodynamics on the surface of the packings. To analyze the local flow behavior of the liquid phase, a three-dimensional Computational Fluid Dynamics (CFD) model was developed that applies to the two-phase countercurrent flow on an inclined and flat plate. This model, based on the volume-of-fluid (VOF) method, considers the gravity, the surface tension and the drag force between the two phases. The development of such a model allows investigation of the influences of the liquid and gas flow rates on the flow behavior such as the film flow and the rivulet flow. A validation of the model was performed using data from the literature and from experiments conducted in this work. Simulation and experimental results demonstrate that the specific wetted area on the plate decreases with decreasing liquid load. Moreover, CFD simulations reveal that the presence of the countercurrent gas phase tends to increase the fluctuation and the thickness of the film flow, which is in accordance with experimental data. It also affects the flow behavior of the rivulet flow and changes the velocity profiles for both film and rivulet flow behavior. On the other hand, the simulation results indicate that CFD is a potent tool for analyzing and investigating the flow phenomena in chemical engineering.

A scheduling model for a multi-product, multistage batch plant with parallel units is presented. The objective is to maximize the weighted completion times of orders in every processing stage while imposing a penalty on the slower orders. The proposed model uses the continuous-time representation mode and describes the allocations of tasks, units and stages by a set of binary variables. In order to reduce the model size and provide a more effective solution to the model, a pre-ordering approach that sorts the processing sequence of orders is developed. The pre-ordering approach identifies the infeasible assignments through which the number of binary variables is significantly reduced. Illustrative examples are provided to show that the size of the proposed model is small, and therefore, needs much less computational effort in comparison with the existing models in the literature.

Support vector machines (SVM) are applied to the prediction of key state variables in bioprocesses, such as product concentration and biomass concentration, which commonly play an important role in bioprocess monitoring and control. A so-called rolling learning-prediction procedure is used to deal with the time variant property of the process, and to establish the training database for the SVM predictor, which is characterized with the rolling update of the training database. As an example, product concentration in industrial penicillin production is predicted, and a comparison is also made with three different artificial neural network architectures (FBNN, RBFN, and RNN). The test results indicate that a prediction accuracy of 1-3 % can be obtained for 4-40h ahead prediction using the SVM, which is better than the best of the three artificial neural networks (ANNs). Moreover, for noise-added training highly noisy data or small-sample learning, the SVM also clearly outper-forms FBNN, RBFN, and RNN.

Constant specific growth rate control in the methanol growth phase was investigated for the fed-batch cultivation of Pichia pastoris expressing recombinant human serum albumin (rHSA). The methanol feeding strategy was determined based on an earlier proposed macrokinetic model to maintain the specific growth rate at preset levels. The experimental results demonstrate that the control strategy of constant specific growth rate is more effective than that of constant feeding rate to maximize production. Furthermore, the most productive setpoint of the specific growth rate is found between 0.005 and 0.006 h−1, which yields protein concentrations higher than 5 g l−1 at 160 h. In addition, a setpoint of 0.008 h−1 is suggested as the upper limit for specific growth rate control for the given expression system. Copyright © 2005 Society of Chemical Industry

A multi-staged population balance model is proposed to describe the cell cycle dynamics of myeloma cell cultivation. In this model, the cell cycle is divided into three stages, i.e., G1, S, and G2M phases. Both DNA content and cell volume are used to differentiate each cell from other cells of the population. The probabilities of transition from G1 to S and division of G2M are assumed to be dependent on cell volume, and transition probability from S to G2M is determined by DNA content. The model can be used to simulate the dynamics of DNA content and cell volume distributions, phase fractions, and substrate and byproduct concentrations, as well as cell densities. Measurements from myeloma cell cultivations, especially the FACS data with respect to DNA distribution and cell fractions in different stages, are employed for model validation.

A macrokinetic model for a myeloma cell line is proposed. The model describes the dynamic balances of lactate, alanine, ATP and NADH during the metabolsim of glucose, glutamine and other amino acids. The metabolic pathways mainly include glycolysis, glutaminolysis, the trcicarboxylic acid cycle, the formation and utilization of amino acids, the respiratory chain, cell growth and cell death. The metabolic shift of glucose is especially considered because of a change in the rate of glycolysis. Thus the model functions in three modes to describe the behaviour of the myeloma cell line. On the basis of this model the macrokinetic bioreaction rates such as the specific substrate consumption rate, the specific growth rate, the specific acetyl-CoA formation rate, as well as the specific oxygen uptake rate, are estimated. The specific substrate consumption rate and the specific growth rate are then coupled into a bioreactor model such that the key variables, i.e., the cell density, the substrate and metabolite concentrations, are obtained. Experiments with batch and fed-batch cultures of a myeloma cell line (X63-Ag8.653) were used to validate the model. The prediction of the model was simulated by the rolling prediction approach.