Co-reporter:Xiang Zhang, Jingqi Yuan, Liang Xu, Zhen Tian, Jingcheng Wang
Applied Thermal Engineering 2017 Volume 126(Volume 126) pp:
Publication Date(Web):5 November 2017
DOI:10.1016/j.applthermaleng.2017.07.172
•Wetness fraction of the low pressure cylinders is identified.•Condenser thermodynamic characteristics under varying working conditions are investigated.•The objective function of the condenser pressure optimization is presented.•The set value of the mass flow rate of the circulating water is obtained with the manipulation strategy.The operation conditions of the cold-end system have significant impact on the unit thermal economy. However, due to the difficulty of online determination of some key parameters, the condenser pressure optimization has been a challenging task for a long time. This paper proposes an online applicable approach to optimize the condenser pressure with variable speed pumps, taking the mass flow rate of the circulating water as the manipulating variable, to achieve better thermal economy. After the exhaust steam wetness fraction is online identified, the condenser thermodynamic characteristics under varying working conditions are investigated based on the effectiveness and steady-state energy balance of the condenser. By maximizing the net power benefit, defined as the difference between the unit power increment and the pump power consumption increment, the optimal mass flow rate of the circulating water is derived. To validate the approach, pseudo-online simulations are conducted with the history data from an ultra-supercritical unit. The retention time during which the set value of the mass flow rate of the circulating water remains constant is studied in context with the implementation of the manipulation strategy under on-site scenario. Simulation results reveal the energy-saving potential of condenser pressure optimization with the proposed approach.
Co-reporter:Liang Xu, Jingqi Yuan, Jingcheng Wang
International Journal of Heat and Mass Transfer 2017 Volume 105(Volume 105) pp:
Publication Date(Web):1 February 2017
DOI:10.1016/j.ijheatmasstransfer.2016.09.084
•A moving segment model of the once-through evaporation system is established.•The model can switch between subcritical and supercritical working conditions.•A solution algorithm is proposed to deal with working condition auto-switching.•The proposed model and solution algorithm are validated using the real data.The evaporation system of the ultra-supercritical power plant is characterized with a once-through steam generator. Modeling of such evaporation system is of extreme importance for the purpose of efficiency assessment and operation optimization. There still exist some major problems in the modeling because of the complexity of the phase transitions in the evaporation system at different loads. This paper establishes a moving segment model (including subcooled water, saturated two-phase and superheated steam segments) based on the mass and energy conservation principles under the subcritical and supercritical working conditions, and proposes a solution algorithm to deal with working condition switching in an automatic mode. Pseudo-online simulations are performed to test the model and the algorithm by using the geometric parameters and the history operation data of a real ultra-supercritical unit in China. The calculated heat absorption rate of the working fluid from the entire evaporation system agrees well with the reference value. The maximum absolute relative error is found as small as 0.54% in the time resolution of 30 min, which indirectly demonstrates the feasibility and accuracy of the presented model and the solution algorithm.
Co-reporter:Zhi-xiong Zhang 张志雄;Jun-wei Sun 孙君伟
Journal of Shanghai Jiaotong University (Science) 2015 Volume 20( Issue 3) pp:281-285
Publication Date(Web):2015 June
DOI:10.1007/s12204-015-1622-y
In the two-step vitamin C fermentation process, its precursor 2-keto-L-gulonic acid was synthesized from L-sorbose by mixed culture of Gluconobacter oxydans and Bacillus megaterium. The interaction between Gluconobacter oxydans and Bacillus megaterium remains unclear and it is a challenge to mathematically model the mixed growth of these two strains. The Monod-type equations were previously proposed to describe the coupled growth of Gluconobacter oxydans and Bacillus megaterium. However, in this study, we modeled the interaction of these two strains in a macroscopic view by introducing the population theory. Taking account of the fact that the density or concentration of Gluconobacter oxydans or Bacillus megaterium was hardly to measure accurately in the mixed culture broth, the data of concentrations of the substrate and product were used to indirectly investigate the relation between these two strains. Three batch experiments were used to validate our model. And according to the values of identified parameters, the type of interaction between Gluconobacter oxydans and Bacillus megaterium was concluded to be predation, where Gluconobacter oxydans was predator, and Bacillus megaterium was prey.
Co-reporter:Tao Wang;Jibin Sun
Bioprocess and Biosystems Engineering 2015 Volume 38( Issue 4) pp:605-614
Publication Date(Web):2015/04/01
DOI:10.1007/s00449-014-1300-8
This article presents a modeling approach for industrial 2-keto-l-gulonic acid (2-KGA) fed-batch fermentation by the mixed culture of Ketogulonicigenium vulgare (K. vulgare) and Bacillus megaterium (B. megaterium). A macrokinetic model of K. vulgare is constructed based on the simplified metabolic pathways. The reaction rates obtained from the macrokinetic model are then coupled into a bioreactor model such that the relationship between substrate feeding rates and the main state variables, e.g., the concentrations of the biomass, substrate and product, is constructed. A differential evolution algorithm using the Lozi map as the random number generator is utilized to perform the model parameters identification, with the industrial data of 2-KGA fed-batch fermentation. Validation results demonstrate that the model simulations of substrate and product concentrations are well in coincidence with the measurements. Furthermore, the model simulations of biomass concentrations reflect principally the growth kinetics of the two microbes in the mixed culture.
Co-reporter:Lijia Luo, Ying Yan, Yuanyuan Xu, and Jingqi Yuan
Industrial & Engineering Chemistry Research 2012 Volume 51(Issue 20) pp:7104-7112
Publication Date(Web):March 14, 2012
DOI:10.1021/ie201774n
The flow regime transitions in an airlift reactor were investigated based on pressure fluctuation signals. Two time–frequency analysis methods, i.e., Wigner–Ville distribution and wavelet transform, were used to extract flow regime characteristics from pressure signals. The main frequency derived from the smoothed pseudo-Wigner–Ville distribution of the pressure signal was used to quantify flow regime transitions in the reactor. Two flow regime transition points were successfully detected from the evolution of main frequencies of pressure signals. In addition, the local dynamic characteristics of the pressure signal at different frequency bands were analyzed by use of the wavelet transform. A new flow regime identification method based on the wavelet entropy of the pressure signal was proposed. This method was confirmed to be reliable and efficient to detect flow regime transitions in the reactor.
Co-reporter:Zhixiong Zhang;Xinjie Zhu;Ping Xie
Biotechnology and Bioprocess Engineering 2012 Volume 17( Issue 5) pp:1008-1017
Publication Date(Web):2012 October
DOI:10.1007/s12257-011-0400-4
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-L-gulonic acid fermentation process with reasonable accuracy.
Co-reporter:L. Cui;Y. Xu;Q. Jia;H. Wu;J. Yuan
Chemical Engineering & Technology 2011 Volume 34( Issue 5) pp:751-759
Publication Date(Web):
DOI:10.1002/ceat.201000507
Abstract
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.
Co-reporter:Jun Geng
Bioprocess and Biosystems Engineering 2010 Volume 33( Issue 6) pp:665-674
Publication Date(Web):2010 August
DOI:10.1007/s00449-009-0340-y
A macrokinetic model employing cybernetic methodology is proposed to describe mycelium growth and penicillin production. Based on the primordial and complete metabolic network of Penicillium chrysogenum found in the literature, the modeling procedure is guided by metabolic flux analysis and cybernetic modeling framework. The abstracted cybernetic model describes the transients of the consumption rates of the substrates, the assimilation rates of intermediates, the biomass growth rate, as well as the penicillin formation rate. Combined with the bioreactor model, these reaction rates are linked with the most important state variables, i.e., mycelium, substrate and product concentrations. Simplex method is used to estimate the sensitive parameters of the model. Finally, validation of the model is carried out with 20 batches of industrial-scale penicillin cultivation.
Co-reporter:Y. F. Xue ;J. Q. Yuan
Chemical Engineering & Technology 2008 Volume 31( Issue 3) pp:433-439
Publication Date(Web):
DOI:10.1002/ceat.200700168
Abstract
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.
Co-reporter:Yuan-Hua Liu;Jing-Xiu Bi;An-Ping Zeng
Bioprocess and Biosystems Engineering 2008 Volume 31( Issue 6) pp:569-577
Publication Date(Web):2008 October
DOI:10.1007/s00449-008-0204-x
A simple kinetic model is developed to describe the dynamic behavior of myeloma cell growth and cell metabolism. Glucose, glutamine as well as lysine are considered as growth limiting substrates. The cell growth was restricted as soon as the extracellular lysine is exhausted and then intracellular lysine becomes a growth limiting substrate. In addition, a metabolic regulator model together with the Monod model is used to deal with the growth lag phase after inoculation or feeding. By using these models, concentrations of substrates and metabolites, as well as densities of viable and dead cells are quantitatively described. One batch cultivation and two fed-batch cultivations with pulse feeding of nutrients are used to validate the model.
Co-reporter:Weidong Ding
Medical & Biological Engineering & Computing 2008 Volume 46( Issue 2) pp:139-145
Publication Date(Web):2008 February
DOI:10.1007/s11517-007-0248-0
A new spike sorting method based on the support vector machine (SVM) is proposed to resolve the superposition problem. The spike superposition is generally resolved by the template matching. Previous template matching methods separate the spikes through linear classifiers. The classification performance is severely influenced by the background noise included in spike trains. The nonlinear classifiers with high generation ability are required to deal with the task. A multi-class SVM classifier is therefore applied to separate the spikes, which contains several binary SVM classifiers. Every binary SVM classifier corresponding to one spike class is used to identify the single and superposition spikes. The superposition spikes are decomposed through template extraction. The experimental results on the simulated and real data demonstrate the utility of the proposed method.
Co-reporter:Jieming Liang
Biotechnology Letters 2007 Volume 29( Issue 1) pp:27-35
Publication Date(Web):2007 January
DOI:10.1007/s10529-006-9203-7
An oxygen transfer model was established for Pichia pastoris growing on glycerol and methanol in a stirred tank bioreactor and expressing a recombinant human serum albumin (rHSA). This was based on pseudo-steady state mass balance, where the volumetric O2 transfer coefficient, kLa, was estimated as a function of power input per unit volume and aeration rate. Under pseudo-steady state, the O2 transfer rate model matched the O2 uptake rate obtained from a previous macrokinetic model. This procedure was also applied to estimate biomass concentration by using the on-line rolling identification approach.
Co-reporter:Y. Li;J. Yuan
Chemical Engineering & Technology 2006 Volume 29(Issue 3) pp:
Publication Date(Web):23 FEB 2006
DOI:10.1002/ceat.200500182
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 4–40 h 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 outperforms FBNN, RBFN, and RNN.
Co-reporter:Jingqi Yuan;Haitao Ren
Journal of Chemical Technology and Biotechnology 2005 Volume 80(Issue 11) pp:1268-1272
Publication Date(Web):26 MAY 2005
DOI:10.1002/jctb.1321
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
Co-reporter:Wang-Qiang Niu, Jing-Qi Yuan
Neurocomputing (December 2008) Volume 72(Issues 1–3) pp:302-312
Publication Date(Web):1 December 2008
DOI:10.1016/j.neucom.2008.01.012
The local edge detector cells play a fundamental role in the visual system. They may be involved in the control of eye movement and visual attention. However, few studies have been devoted to modeling their behaviors. In this study, a feedforward multi-subunit spatiotemporal model is proposed for local edge detector cells in the cat retina. The model is able to describe their responses to drifting sinusoidal gratings, alternating sinusoidal gratings, flashing spots and annuli, and these model responses are qualitatively consistent with the physiological observations. The organization of the model maps the anatomical structure of the cat retina well, and the model may be useful in explaining ON–OFF retinal cells in other vertebrates.
Co-reporter:Tong Yu, Jingqi Yuan
IFAC Proceedings Volumes (2013) Volume 46(Issue 13) pp:581-584
Publication Date(Web):1 January 2013
DOI:10.3182/20130708-3-CN-2036.00108
A lumped-parameter model for natural circulation drum-boilers is proposed to calculate the heat flux transferred into water wall tubes. The complicated dynamics of the entire evaporation system is described, including drum, downcomers and riser components. Based on the mass and energy balances, the model is characterized by only a few physical parameters. The heat flux into water wall tubes can be determined without any combustion conditions of the furnace. The model validation is carried out by using the industrial data of a coal fired power plant. The dynamic properties of the heat flux may be captured over a wide range of operating, which may potentially be applied for real time estimation of heat generation in the furnace and lower heating value (LHV) of the coal.
Co-reporter:Kunlun Hu, Jingqi Yuan
Chemometrics and Intelligent Laboratory Systems (15 February 2008) Volume 90(Issue 2) pp:
Publication Date(Web):15 February 2008
DOI:10.1016/j.chemolab.2007.10.002
A multivariate statistical process control (MSPC) method using dynamic multiway neighborhood preserving embedding (DMNPE) is proposed for fed-batch process monitoring. Different from principal component analysis (PCA) which aims at preserving the global Euclidean structure of the data set, neighborhood preserving embedding aims to preserve the local neighborhood structure of the data set. The neighborhood preserving property enables NPE to find more meaningful intrinsic information hidden in the high-dimensional observations compared with PCA. Moreover, the robustness of NPE is better than that of PCA. On the other hand, a dynamic monitoring approach based on moving window technique is employed to deal with the time-variant property of the dynamic processes. An industrial cephalosporin fed-batch fermentation process is used to demonstrate the performance of the DMNPE. The results show the advantages of DMNPE over those methods such as dynamic multiway PCA (DMPCA), static multiway NPE (SMNPE) and static multiway PCA (SMPCA) in fed-batch process monitoring. Finally, the robustness of the DMNPE monitoring is tested by adding noises to the original data sets.
Co-reporter:Lijia Luo, Yuanyuan Xu, Jingqi Yuan
Chemical Engineering Science (1 November 2011) Volume 66(Issue 21) pp:5224-5235
Publication Date(Web):1 November 2011
DOI:10.1016/j.ces.2011.07.018
Higher order statistics and Wigner higher order moment spectra were used to extract useful flow regime characteristics from wall pressure fluctuation signals in an annulus sparged internal loop airlift reactor. It is found that the pressure fluctuation in the airlift reactor is a typical nonlinear and non-stationary process, which exhibits different frequency characteristics depending on flow regimes. Analysis methods based on bispectrum and Wigner trispectrum are powerful tools to reveal frequency characteristics of pressure signals. To identify flow regime transitions in the reactor, two new characteristic quantities, namely average bispectrum and generalized average frequency, are defined from bispectrum and Wigner trispectrum of the pressure signal, respectively. Two flow regime transition points corresponding to three flow regimes in the reactor are successfully detected by using these two characteristic quantities.Highlights► Pressure fluctuation signals in an airlift reactor are investigated. ► Pressure signals are studied based on the higher order statistics. ► Wigner higher order moment spectra are used to analyze pressure signals. ► Two characteristic quantities are proposed for the flow regime identification.
