Shijiazhuang, China
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Feng Q.,Shanghai JiaoTong University | Pan Y.,Shanghai JiaoTong University | Cheng B.,Shanghai JiaoTong University | Sun J.,Hebei Welcome Pharmaceutical Co. | Yuan J.,Shanghai JiaoTong University
Huagong Xuebao/CIESC Journal | Year: 2013

2-Keto-L-gulonic acid (2-KGA), the precursor for vitamin C synthesis, is produced by the mixed culture of Ketogulonicigenium vulgare and Bacillus megaterium. In this paper, the previously established kinetic model for 2-KGA mixed culture was firstly tested with the data of 80 industrial batches. Based on sensitivity analysis, it was found that some insensitive parameters might be assigned fixed values to minimize computing time. Then, the model was used to predict the most important state variables, i. e., substrate and product concentrations. Moving data window technique and rolling parameter identification approach were used in the prediction process. 4 h and 8 h ahead prediction errors for 2-KGA concentration were less than 5%. © All Rights Reserved.


Feng Q.,Shanghai JiaoTong University | Pan Y.,Shanghai JiaoTong University | Cheng B.,Shanghai JiaoTong University | Sun J.,Hebei Welcome Pharmaceutical Co. | Yuan J.,Shanghai JiaoTong University
Huagong Xuebao/CIESC Journal | Year: 2013

The interaction between Bacillus megaterium (also called big bacterium) and Ketogulonicigenium vulgare (also called small bacterium) was studied for 2-keto-L-gulonic acid (2-KGA) mixed culture fermentation. The following mechanisms were found. (1) Big bacterium might adjust its growth behavior by quorum sensing. (2) Metabolites and autolysis substances of big bacterium were beneficial to overcoming the metabolic defects of small bacterium and therefore accelerating the latter's growth. (3) Small bacterium released lysozyme to promote big bacterium's autolysis. (4) Big bacterium autolysis released specific protease substances to enhance sorbitol dehydrogenase (SDH) which might increase the synthesis rate of 2-KGA. Based on such mechanisms, a kinetic model of 2-KGA mixed fermentation was established. Model validation was carried out with four sets of experimental data under different cultivation conditions. The results demonstrated that the proposed model was able to well describe the growth of two bacteria and 2-KGA production. © All Rights Reserved.


Cui L.,Shanghai JiaoTong University | Xie P.,HeBei Welcome Pharmaceutical Co. | Sun J.,HeBei Welcome Pharmaceutical Co. | Guo W.,HeBei Welcome Pharmaceutical Co. | Yuan J.,Shanghai JiaoTong University
2011 International Symposium on Advanced Control of Industrial Processes, ADCONIP 2011 | Year: 2011

2-keto-L-gulonic acid (2-KGA), a key precursor in the synthesis of L-ascorbic acid, is produced by mixed fermentation of Bacillus megaterium and Gluconobacter oxydans with L-sorbose as substrate. For such mixed cultivation, the mechanistic modelling is difficult because the interactions between the two strains are not well known yet. Therefore, data-driven modelling is studied in this paper. The rolling learning-prediction (RLP) based on support vector machine (SVM) is practiced to predict the product formation. To satisfy the online application demand, pseudo-on-line prediction is carried out using the data from commercial scale 2-KGA cultivation. The prediction approach receives data in sequence and the historical database of the SVM is updated with statistical analysis of the product formation after the termination of a batch. The robustness of the prediction approach is further tested by adding extra noises to the process variables. © 2011 Zhejiang University.


Zhang Z.,Shanghai JiaoTong University | Zhu X.,North China Pharmaceutic Group Corporation | Xie P.,Hebei Welcome Pharmaceutical Co. | Sun J.,Hebei Welcome Pharmaceutical Co. | Yuan J.,Shanghai JiaoTong University
Biotechnology and Bioprocess Engineering | Year: 2012

A set of kinetic models have been developed for the production of 2-keto-L-gulonic acid from L-sorbose by a mixed culture of Gluconobacter oxydans and Bacillus megaterium. A metabolic pathway is proposed for Gluconobacter oxydans, and a macrokinetic model has been developed for Gluconobacter oxydans, where the balances of some key metabolites, ATP and NADH are taken into account. An unstructured model is proposed for concomitant bacterium Bacillus megaterium. In the macrokinetic model and unstructured model, the mechanism of interaction between Gluconobacter oxydans and Bacillus megaterium is investigated and modeled. The specific substrate uptake rate and the specific growth rate obtained from the macrokinetic model are then coupled into a bioreactor model such that the relationship between the substrate feeding rate and the main state variables, such as the medium volume, the biomass concentrations, the substrate, and the is set up. A closed loop regulator model is introduced to approximate the induction of enzyme pool during lag phase after inoculation. Experimental results demonstrate that the model is able to describe 2-keto-Lgulonic acid fermentation process with reasonable accuracy. © The Korean Society for Biotechnology and Bioengineering and Springer 2012.


Luo L.,Zhejiang University of Technology | Yuan J.,Shanghai JiaoTong University | Xie P.,Hebei Welcome Pharmaceutical Co. | Sun J.,Hebei Welcome Pharmaceutical Co. | Guo W.,Hebei Welcome Pharmaceutical Co.
Chemical Engineering Research and Design | Year: 2013

Effects of the sieve plate on hydrodynamics and mass transfer in an annulus sparged airlift reactor (0.08m3, 1.3m tall, and 0.284m in diameter) were investigated. It is found that the sieve plate can significantly enhance gas holdup and volumetric mass transfer coefficient. The sieve pore plays an important role in breaking up bubbles. With a given free area ratio, the sieve plate with a larger sieve pore diameter is more efficient in increasing the volumetric mass transfer coefficient. Four different free area ratios between 37% and 73% are tested, and then an optimal free area ratio is determined. The effect of the sieve plate is found to be related to sparger types. The sieve plate leads to a larger increase of volumetric mass transfer coefficient with the O-ring distributor as compared to the 4-orifice nozzle. Empirical correlations and a hydrodynamic model are proposed to predict gas holdup, volumetric mass transfer coefficient and liquid velocity in airlift reactors with sieve plates. © 2013 The Institution of Chemical Engineers.


Luo L.,Shanghai JiaoTong University | Yan Y.,Shanghai JiaoTong University | Xu Y.,Shanghai JiaoTong University | Xie P.,Hebei Welcome Pharmaceutical Co. | And 3 more authors.
Canadian Journal of Chemical Engineering | Year: 2013

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 30Hz. 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.


Wang T.,Shanghai JiaoTong University | Sun J.,HeBei Welcome Pharmaceutical Co. | Zhang W.,Shanghai JiaoTong University | Yuan J.,Shanghai JiaoTong University
Process Biochemistry | Year: 2014

As the key precursor for L-ascorbic acid synthesis, 2-keto-l-gulonic acid (2-KGA) is widely produced by the mixed culture of Bacillus megaterium and Ketogulonicigenium vulgare. In this study, a Bayesian combination of multiple neural networks is developed to obtain accurate prediction of the product formation. The historical batches are classified into three categories with a batch classification algorithm based on the statistical analysis of the product formation profiles. For each category, an artificial neural network is constructed. The input vector of the neural network consists of a series of time-discretized process variables. The output of the neural network is the predicted product formation. The training database for each neural network is composed of both the input-output data pairs from the historical bathes in the corresponding category, and all the available data pairs collected from the batch of present interest. The prediction of the product formation is practiced through a Bayesian combination of three trained neural networks. Validation was carried out in a Chinese pharmaceutical factory for 140 industrial batches in total, and the average root mean square error (RMSE) is 2.2% and 2.6% for 4 h and 8 h ahead prediction of product formation, respectively. © 2013 Elsevier Ltd. All rights reserved.


Cui L.,Shanghai JiaoTong University | Xie P.,HeBei Welcome Pharmaceutical Co. | Sun J.,HeBei Welcome Pharmaceutical Co. | Yu T.,Shanghai JiaoTong University | Yuan J.,Shanghai JiaoTong University
Computers and Chemical Engineering | Year: 2012

Mixed culture fermentation of Bacillus megaterium and Gluconobacter oxydans is widely used to produce 2-keto-l-gulonic acid (2-KGA), a key precursor for l-ascorbic acid synthesis. For such mixed cultivation, kinetic modelling is difficult because the interactions between the two strains are not well known yet. In this paper, data-driven prediction of the product formation is presented for the purpose of better process monitoring. A rolling learning-prediction approach based on neural networks is practiced to predict 2-KGA formation. Techniques associated with the approach, such as the data pretreatment and the rolling learning-prediction mechanism, are given in more detail. The validation results by using the data from commercial scale 2-KGA cultivation indicate that the prediction error is less than 5% in the later phase of fermentation and the reliable prediction time span is 8. h. The robustness of the prediction approach is further tested by adding extra noises to the process variables. © 2011 Elsevier Ltd.


Luo L.,Shanghai JiaoTong University | Yan Y.,Shanghai JiaoTong University | Xie P.,HeBei Welcome Pharmaceutical Co. | Sun J.,HeBei Welcome Pharmaceutical Co. | And 2 more authors.
Chemical Engineering Journal | Year: 2012

The flow regimes and their transitions in an internal loop airlift reactor were investigated. The Hilbert-Huang transform (HHT) was applied to analyze the energy-frequency-time distribution of the pressure signal. It was found that the Hilbert spectrum of the pressure signal was closely related to the superficial gas velocity. The stochastic behaviors of intrinsic mode functions (IMFs) extracted from the pressure signal were studied using the Hurst analysis. Two different Hurst exponents were obtained for each pressure signal: one was smaller than 0.5, while the other was larger than 0.5, representing the anti-persistent and persistent hydrodynamic behaviors, respectively. The evolution of the larger Hurst exponent clearly indicated flow regime transitions in the downcomer. The wavelet transform combined with the autocorrelation analysis were applied to extract chaotic components of the pressure signal. Two flow regime transition points were successfully detected from the evolution of chaotic parameters, i.e. the largest Lyapunov exponent, correlation dimension and Kolmogorov entropy. © 2011 Elsevier B.V..

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