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Efficient and stable operation of marine diesel engines is crucial both for navigation maneuverability and safety of waterjet propulsion vessels. In this paper, a piecewise linear model and an affine nonlinear model of marine diesel engines are established by applying mechanism analysis techniques. The models are validated with the experimental data collected from a 290 kW (maximum power) diesel engine equipped on a waterjet propulsion vessel. The relative error of engine speed between model simulation and the measurements is found less than 3%. To improve the performance of the speed regulation system of diesel engines, a sliding mode control strategy is developed on the basis of the affine nonlinear model utilizing the equivalent control method. Simulation results are presented to illustrate the effectiveness of the proposed control strategy regarding model mismatch, response speed, disturbance rejection, and engine speed tracking ability.

The lower heating value (LHV, also called net calorific value, NCV) of the coal entering the furnace is one of the most important variables for process optimization. However, in most cases, it is not online measurable in coal-fired power plants. In this paper, an online identification approach of the LHV is proposed based on the boiler-side whole process models together with the real-time data acquired by the distributed control system (DCS). The boiler-side whole process models cover typical facilities of a power plant, and are established based on dynamic mass and energy balances. The accurate estimation of the mass flow rate of the coal entering the furnace and the total output energy of the boiler is the highlight of the approach. Rolling identification of the LHV of the coal is presented to take the intrinsic time variant characteristics of the coal combustion and heat transfer processes into account. To validate the proposed approach, pseudo-online simulations are carried out with the history data of a real 300 MW coal-fired power plant. The mean absolute relative error between the calculated LHVs and their original assays is found less than 5.0%, and the mean absolute relative error becomes less than 1.1%, if the rectified assays are used.

Thermodynamic properties of the working fluid and the flue gas play an important role in the thermodynamic calculation for the boiler design and the operational optimization in power plants. In this study, a generic approach to online calculate the thermodynamic properties of the flue gas is proposed based on its composition estimation. It covers the full operation scope of the flue gas, including the two-phase state when the temperature becomes lower than the dew point. The composition of the flue gas is online estimated based on the routinely offline assays of the coal samples and the online measured oxygen mole fraction in the flue gas. The relative error of the proposed approach is found less than 1% when the standard data set of the dry and humid air and the typical flue gas is used for validation. Also, the sensitivity analysis of the individual component and the influence of the measurement error of the oxygen mole fraction on the thermodynamic properties of the flue gas are presented.

Load following is one of the basic control task for nuclear power plants. This paper proposes a RBF-based adaptive sliding mode control strategy with extended state observer (ESO) for load following of nuclear power plants in the presence of disturbances. The nonlinear mathematical model of the nuclear reactor system is firstly described. In order to recover unmeasured states including average fuel temperature and relative delayed neutron precursor density, an extended state observer is designed to reconstruct both unmeasured states and disturbances at the same time, and its asymptotically stability condition is analyzed. Based on the nuclear reactor model and estimation information from the ESO, a RBF-based adaptive sliding mode control strategy is proposed, which consists of a dual closed-loop integral sliding mode control scheme while taking into account disturbances simultaneously. In addition, the chattering phenomenon of sliding mode control is alleviated by replacing the sign function with a saturation function. Simulation results are provided to verify the effectiveness of the overall control scheme compared with conventional PID control and sliding mode control approaches in accordance to load following performance, states reconstruction accuracy, and disturbances rejection ability.

Grid voltage parameters, including the amplitude, frequency and phase angle, are crucial information for real-time control and protection in power systems. Currently, the phase-locked loop methods are widely used for voltage parameters estimation. However, they could hardly achieve accurate estimation in the presence of unbalanced grid voltage and harmonic disturbance. In this paper, a novel three-phase sinusoid-locked loop (SLL) approach is proposed to address the estimation issue of the unbalanced grid voltage parameters. Firstly, the unbalanced three-phase grid voltage is decomposed as the positive and negative sequences based on the instantaneous symmetrical components theory. Then, two SLLs based on the principles of the virtual synchronous machine (VSM) are developed for the positive and negative sequence grid voltages, respectively. Following the idea of SLL, the output voltage of the VSM is locked with the input grid voltage when the real power and reactive power are both driven to zero. Eventually, the unbalanced three-phase grid voltage is reconstructed with the estimated parameters of the positive and negative sequence voltages. Simulation results demonstrate that the proposed SLL approach achieves excellent estimation performance with respect to the unbalanced grid voltage.

T0620. The gap metric is applied to investigate the nonlinearity of the boiler-turbine system to determine the linearization points for nonlinear model such that a Takagi-Sugeno fuzzy model of appropriate accuracy and complexity is obtained. The FAMPC is then designed based on the fuzzy model that is online updated to well approximate the nonlinear dynamics. The fuzzy model is validated with the plant data of a 300 MW unit and the result shows good agreement. The simulation cases demonstrate the proposed controller has good tracking performance and strong robustness.

Grid voltage parameters, including the amplitude, frequency and phase angle of the grid voltage, are essential for real-time control and protection in power systems. Current approaches mainly focus on adopting a phase-locked loop or its derivatives for parameters estimation. However, they could hardly achieve fast response and are sensitive to voltage variations and disturbances. In this paper, the estimation of grid voltage parameters is recast as an observer design problem. An exosystem model of the sinusoidal voltage is established at first. Then, a framework for the observer-based methods is proposed for the robust estimation of single-phase grid voltage parameters. Moreover, a second-order sliding mode observer (SMO), which actually has the lowest order among all possible observers, is designed to estimate the parameters. Based on the Lyapunov theory, the asymptotic stability of the closed-loop SMO is proved. Compared with two other typical methods, simulations are carried out to illustrate the effectiveness and robustness of the proposed approach in the presence of voltage variations and external disturbances. In addition, the proposed estimation approach may also be valuable for online fault detection of voltage sag and low-frequency oscillation.

This study considers the observer-based consensus tracking problem of linear multi-agent systems with input saturation. Existing observer-based consensus protocols are designed based on an undirected graph, and need global information, such as the network size or eigenvalue information of the Laplacian matrix. In this study, based on only the agent dynamics and the relative outputs of neighbouring agents, an adaptive consensus protocol is proposed by assigning a time-varying coupling weight to each node, and this protocol is independent of any global information and hence is fully distributed. Under the assumptions that each agent is asymptotically null controllable with bounded controls and detectable, and the topological graph has a directed spanning tree with the leader as the root node, semi-global observer-based consensus tracking of multi-agent systems can be reached despite actuator saturation occurring. The results are applied to consensus tracking control of two-mass-spring systems, which are well-known models for vibration in many mechanical systems.

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.

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.

Online performance monitoring of the steam turbine system is very important for the economic and safe operation of a power plant. For a subcritical steam turbine system, the wet saturated steam in the last few stages of the low pressure cylinder (LPC) increases the heat loss and threatens the unit safety. Unfortunately, online measurement of the exhaust steam wetness fraction of the LPC has been a difficult task for a long time. This paper proposes an online applicable approach based on two equivalent overall efficiency models to monitor the exhaust steam wetness fraction. Firstly, to estimate the total energy entering the steam turbine system, the lumped models of the evaporation system and the heat exchangers are established. Then two equivalent overall efficiency models of the steam turbine system are presented. Based on the same overall efficiency, the exhaust steam wetness fraction is therefore identified. The effectiveness of the proposed approach is validated through the comparison between the calculated and the designed operation data of a subcritical coal-fired unit. As the extension of the applications, the calculated wetness fraction is further used to estimate the thermal economic indices for the purpose of assessing the operation performance of the steam turbine system.

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.

Selective catalytic reduction as one of the secondary NOx control technologies is widely used in industrial sources including coal-fired power plants and large boilers. The performance of an SCR-DeNOx system is sensitive to the installment of its components such as turning vanes and hybrid grids. In this work, three-dimensional CFD simulations are carried out to analyze the breakage failure of an SCR-DeNOx system for a certain 350 MW coal-fired power plant. Research results are consistent with the phenomena that occur in the industrial application. It reveals that the breakage failure in the industrial application is likely to be caused by the inappropriate installation of the turning vane 3 locating closest to the catalyst, especially the angle of the turning vane 3. The analysis further shows that the lifetime or the breakage of the catalyst layers depends highly on the gas velocity, the fly ash distribution and its particle velocity.

In this paper, the GTucker2 model is proposed for monitoring both even-length and uneven-length batch processes. The GTucker2 model has two prominent advantages. The first one is that it performs tensor decomposition on the three-way data array and thus avoids potential problems of information loss and “curse of dimensionality” induced by data unfolding. The second one is that it solves the uneven-length problem in a “natural” way without using batch trajectory synchronization, which prevents distorting data and fault patterns and guarantees higher modeling and monitoring precisions. An online batch process monitoring method is then developed by integrating GTucker2 with the moving data window technique. Three monitoring statistics named Q, R2, and T2 statistics are constructed for fault detection and diagnosis. The effectiveness and advantages of the GTucker2-based monitoring method are illustrated by two case studies in a benchmark fed-batch penicillin fermentation process.

This paper presents an approach to modeling the water pressure transfer among nodes in an urban water supply network for the purpose of pressure control. The network is divided into different sub-networks based on the Pearson correlation analysis of the nodal pressure measurements. The Pearson correlation analysis is performed to find out the set of nodes, whose water pressures are highly correlated, and thus a corresponding sub-network is formulated. As a case study, 47 sub-networks are recognized for a region with an area of 250 km2 and 77 nodes in total. For each sub-network, a linear model is constructed to quantify the pressure transfer. The output of the model is the pressure estimate for the node of our interest which is called the center node. The rest of the nodes in the sub-network are called the correlated nodes of the center node, and the pressure measurements at the correlated nodes constitute the input to the model. The average relative error of the model is less than 3%. A pressure regulating method based on the model is proposed and tested numerically.

A novel data projection algorithm named tensor global-local preserving projections (TGLPP) is proposed for three-way data arrays. TGLPP is able to preserve global and local structures of data simultaneously. TGLPP builds a unified framework for global structure preserving and local structure preserving. Tensor principal component analysis (TPCA) and tensor locality preserving projections (TLPP) are unified in the TGLPP framework. They are proved to be two limiting cases of TGLPP. A batch process monitoring method is then developed on the basis of TGLPP. The SPD statistic and R2 statistic are used for fault detection. The contribution plot is applied for fault identification. Two case studies are carried out to validate the efficiency of TGLPP-based monitoring method.

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.

A novel method named tensor global–local structure analysis (TGLSA) is proposed for batch process monitoring. Different from principal component analysis (PCA) and locality preserving projections (LPP), TGLSA aims at preserving both global and local structures of data. Consequently, TGLSA has the ability to extract more meaningful information from data than PCA and LPP. Moreover, the tensor-based projection strategy makes TGLSA more applicable for the three-dimensional data than multiway-based methods, such as MPCA and MLPP. A TGLSA-based online monitoring approach is developed by combining TGLSA with a moving window technique. Two new statistics, i.e., SPD and R2 statistics, are constructed for fault detection and diagnosis. In particular, the R2 statistic is a novel monitoring statistic, which is proposed based on a support tensor domain description method. The effectiveness and advantages of the TGLSA-based monitoring approach are illustrated by a benchmark fed-batch penicillin fermentation process.

Effects of the sieve plate on hydrodynamics and mass transfer in an annulus sparged airlift reactor (0.08 m 3 , 1.3 m tall, and 0.284 m 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.

Prediction of protein subcellular location is a meaningful task which attracted much attention in recent years. A lot of protein subcellular location predictors which can only deal with the single-location proteins were developed. However, some proteins may belong to two or even more subcellular locations. It is important to develop predictors which will be able to deal with multiplex proteins, because these proteins have extremely useful implication in both basic biological research and drug discovery. Considering the circumstance that the number of methods dealing with multiplex proteins is limited, it is meaningful to explore some new methods which can predict subcellular location of proteins with both single and multiple sites. Different methods of feature extraction and different models of predict algorithms using on different benchmark datasets may receive some general results. In this paper, two different feature extraction methods and two different models of neural networks were performed on three benchmark datasets of different kinds of proteins, i.e. datasets constructed specially for Gram-positive bacterial proteins, plant proteins and virus proteins. These benchmark datasets have different number of location sites. The application result shows that RBF neural network has apparently superiorities against BP neural network on these datasets no matter which type of feature extraction is chosen.

A dynamic model called hybrid cybernetic model (HCM) based on structured metabolic network is established for simulating mammalian cell metabolism featured with partially substitutable and partially complementary consumption patterns of two substrates, glucose and glutamine. Benefiting from the application of elementary mode analysis (EMA), the complicated metabolic network is decomposed into elementary modes (EMs) facilitating the employment of the hybrid cybernetic framework to investigate the external and internal flux distribution and the regulation mechanism among them. According to different substrate combination, two groups of EMs are obtained, i.e., EMs associated with glucose uptake and simultaneous uptake of glucose and glutamine. Uptake fluxes through various EMs are coupled together via cybernetic variables to maximize substrate uptake. External fluxes and internal fluxes could be calculated and estimated respectively, by the combination of the stoichiometrics of metabolic networks and fluxes through regulated EMs. The model performance is well validated via three sets of experimental data. Through parameter identification of limited number of experimental data, other external metabolites are precisely predicted. The obtained kinetic parameters of three experimental cultures have similar values, which indicates the robustness of the model. Furthermore, the prediction performance of the model is successfully validated based on identified parameters.

Selective catalytic reduction (SCR) with ammonia or urea is regarded as one of the most important technologies to reduce the NO x emissions from coal-fired power plants. However, the design and development of SCR–DeNO x systems are a complicated process involving the optimization of several parameters such as the ammonia/urea injection strategy, the installment of the gate leaf and the hybrid grid, as well as the thickness of straightener. These parameters determine the velocity and concentration distributions at the entrance of catalyst layers, which are key factors to affect the efficiency of flue gas denitrification and ammonia slip. In this work, CFD simulations are carried out to portray the performance of the SCR–DeNO x facility in a 300 MW coal-fired power plant. The influences of the gate leaf, hybrid grid and straightener on the distributions of the velocity and concentration are investigated. And then the corresponding experiments are performed to qualitatively confirm the simulation results.

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.

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.

In this paper, a method to estimate the target value of terminal temperature difference of horizontal three-segment feed water heater under various working conditions is proposed. The temperature of the water at the outlet of the condensation segment is obtained through mass and energy balance on the basis of actual extracted steam flow rate. Then by means of energy balance equation and heat transfer efficiency equation, the temperature of the outlet water at maximum heat transfer efficiency is calculated, thus obtaining the terminal temperature difference target value. Results based on offline operation data of a real 1000 MW supercritical power plant are given and the difference between target value and actual operation value could be used to estimate the loss differential of the terminal temperature difference.

As more stringent requirements are proposed for the load and frequency regulation in modern power grid, the performance of load tracking and main steam pressure stability becomes more important for the safe and economical operation of thermal power plants. In this paper, a new nonlinear control-oriented model of drum boiler-turbine system is developed with a balance between fidelity and simplicity, and validated with history operating data from a real 300 MW subcritical coal-fired unit over wide range. Then, feedback linearization is applied to the nonlinear model, resulting in two independent linear subsystems. Moreover, a sliding mode tracking controller is constructed for coordinated control based on the linearized model. Simulation results are presented to demonstrate the good tracking performance and strong robustness of the proposed approach.

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.

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.

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

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.

The factors affecting the separation efficiencies of structured packed columns are multi-scale, including the macroscopic variables (eg feed rate, pressure and temperature) and the microscopic liquid flow behavior and distribution. Especially in the situation in which the second liquid phase is inevitable within distillation columns, the microscopic liquid flow behavior could lead to the uncertainty of the separation efficiencies. This work mainly focuses on the investigation of the local flow behavior of a liquid phase flowing on an inclined steel plate at different situations. On one hand, a three-dimensional CFD model based on the VOF model is developed with the commercial CFD package, FLUENT 6.3. On the other hand, the results of the experimental study are adopted to validate partially the model. Within the developed model, the influences of the liquid and gas flow rates are studied with respect to the specific wetted area, the thickness profile of film and rivulet flows as well as the average...

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

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.

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.

A macrokinetic model describing mycelium growth and product formation in fed-batch fermentation of Penicillium chrysogenum is proposed on the basis of intracellular metabolic pathway networks found in the literature. The model is composed of the stoichiometric balance equations for carbon source, ATP, NADH and pyruvate. A regulator model is used as an auxiliary part to simulate the lag phase at the beginning of product synthesis. Combining the macrokinetic model with the bioreactor model, one yields the relationship between manipulating variables, i.e., the substrate feeding rate, and the other state variables such as biomass, substrate and product concentrations. The rolling parameter identification is then applied to online identify the most sensitive model parameters dealing with the model mismatch caused by time-variant kinetic changes. Model validation was performed with the data of 22 batches from industrial-scale penicillin cultivation. Finally, the predictions of the product concentration, up to 24 h ahead, are carried out with an average prediction error of less than 3.5%.

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.

This study deals with the problem of H∞ output-feedback control for switched linear discrete-time systems with time-varying delay. A delay-dependent stability criterion for a given H∞ performance is derived by employing finite sum equality based on quadratic terms, a new method of estimating the upper bound on the finite sum. Both static and dynamic H∞ output-feedback controllers are designed based on a performance analysis. Moreover, a modified cone complementary linearisation algorithm is exploited to solve the non-convex feasibility problem. Numerical examples are given to illustrate the theoretical results.

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.

An on-line application oriented scheduling approach for cultivation periods of penicillin fed-batch fermentation is proposed to maximize the economic profit of the whole workshop. The approach consists of profit function calculation, profit potential prediction, on-line batch classification and scheduling decision-making. The objective function is to maximize the total economic profit of those batches in parallel operation by repeated updating of the termination sequence while without significant disturbance on upstream and downstream work sections. Pseudo-on-line simulations of re-scheduling on industrial penicillin cultivations demonstrate the potential benefits of the approach. The original data is from a Chinese pharmaceutical company which consists of 58 industrial batches of penicillin fed-batch cultivation under empirical operation. Re-scheduling is carried out under three different inoculation sequences and the simulation results indicate that 2–3% of total profit increase could be generally achieved, compared with the empirical one.

A batch process monitoring method using tensor factorization, tensor locality preserving projections (TLPP), is proposed. In many existing vector-based methods on batch process monitoring such as MPCA and MLPP, a batch data is represented as a vector in high-dimensional space. But vectorizing batch data will lead to information loss. Essentially, a batch data is presented as a second order tensor, or a matrix. In this case, tensor factorization may be used to deal with the two-way batch data matrix directly instead of performing vectorizing procedure. Furthermore, tensor representation has some advantages such as low memory and storage requirements and less estimated parameters for normal operating condition (NOC) model. On the other hand, different from principal component analysis (PCA) which aims at preserving the global Euclidean structure of the data, the TLPP aims to preserve the local neighborhood information and to detect the intrinsic manifold structure of the data. Consequently, TLPP may be used to find more meaningful intrinsic information hidden in the observations. The effectiveness and advantages of the TLPP monitoring approach are tested with the data from a benchmark fed-batch penicillin fermentation and two industrial fermentation processes, penicillin and cephalosporin, respectively.

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.

A cybernetic model is developed to model the dynamics of mammalian cell growth and metabolism. Especially, the formation of the byproducts such as ammonia and alanine are taken into account in the model. The production of these byproducts is mainly regulated by the competition between transamination and deamination. In addition, amino acids utilization for protein formation and energy supply is also involved due to lysine limitation found in our experiments. The model is able to simulate the transients of the substrate and byproduct concentrations, the viable and dead biomass as well as the intracellular concentrations of the intermediates and enzymes. Myeloma cell cultivations are applied to validate the model.

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.

An approach for multivariate statistical process control based on multiway locality preserving projections (LPP) is presented. The recently developed LPP is a linear dimensionality reduction technique for preserving the neighborhood structure of the data set. It is characterized by capturing the intrinsic structure of the observed data and finding more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. In this study, LPP is used to extract the intrinsic geometrical structure of the process data. Hotelling’s T 2 ( D ) and the squared prediction error (SPE or Q ) statistic charts for on-line monitoring are then presented, and the contribution plots of these two statistical indices are used for fault diagnosis. Moreover, a moving window technique is used for the implementation of on-line monitoring. Case study was carried out with the data of industrial penicillin fed-batch cultivations. As a comparison, the results obtained with the MPCA are also presented. It is concluded that the Multiway LPP (MLPP) outperforms the conventional MPCA. Finally, the robustness of the MLPP monitoring is analyzed by adding noises to the original data.

The dynamics of a nuclear steam generator (SG) is very different according to the power levels and changes as time goes on. Therefore, it is an intractable as well as challenging task to improve the water level control system of the SG. In this paper, a robust model predictive control (RMPC) method is developed for the level control problem. Based on a multi-model framework, a combination of a local nominal model with a polytopic uncertain linear parameter varying (LPV) model is built to approximate the system’s non-linear behavior. The optimization problem solved here is based on a receding horizon scheme involving the linear matrix inequality (LMI) technique. Closed loop stability and constraints satisfaction in the entire operating range are guaranteed by the feasibility of the optimization problem. Finally, simulation results show the effectiveness and the good performance of the proposed method.

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.