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Shijiazhuang, China

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. Source


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. Source


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. Source


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. Source


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. Source

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