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This paper presents a disturbance observer-based robust backstepping load-following control (DO-RBLFC) scheme for modular high-temperature gas-cooled reactors (MHTGRs) in the presence of actuator saturation and disturbances. Based on reactor kinetics and temperature reactivity feedback, the mathematical model of the MHTGR is first established. After that, a DO is constructed to estimate the unknown compound disturbances including model uncertainties, external disturbances, and unmeasured states. Besides, the actuator saturation is compensated by employing an auxiliary function in this paper. With the help of the DO, a robust load-following controller is developed via the backstepping technique to improve the load-following performance of the MHTGR subject to disturbances. At last, simulation and comparison results verify that the proposed DO-RBLFC scheme offers higher load-following accuracy, better disturbances rejection capability, and lower control rod speed than a PID controller, a conventional backstepping controller, and a disturbance observer-based adaptive sliding mode controller.

It is well known that the model of nuclear reactors features natural nonlinearity, and variable parameters during power tracking operation. In this paper, a disturbance observer-based adaptive sliding mode control (DOB-ASMC) strategy is proposed for power tracking of the pressurized-water reactor (PWR) in the presence of lumped disturbances. The nuclear reactor model is firstly established based on point-reactor kinetics equations with six delayed neutron groups. Then, a new sliding mode disturbance observer is designed to estimate the lumped disturbance, and its stability is discussed. On the basis of the developed DOB, an adaptive sliding mode control scheme is proposed, which is a combination of backstepping technique and integral sliding mode control approach. In addition, an adaptive law is introduced to enhance the robustness of a PWR with disturbances. The asymptotic stability of the overall control system is verified by Lyapunov stability theory. Simulation results are provided to demonstrate that the proposed DOB-ASMC strategy has better power tracking performance than conventional sliding mode controller and PID control method as well as conventional backstepping controller.

The boiler-turbine system (BTS) is usually subject to the tight input constraint, the strong nonlinearity and the complex disturbance, which makes the control a challenging task To this end, a disturbance observer based fuzzy model predictive control (DOBFMPC) scheme is proposed for the BTS in this paper. The generalized discrete-time nonlinear disturbance observer (GDNDO) is first developed to estimate the higher-order disturbance by systematically extending the conventional nonlinear disturbance observer. The GDNDO exhibits a series structure of the internal states, and can precisely estimate the disturbance if its order is equal to or greater than that of the disturbance In addition, a baseline fuzzy model predictive control (FMPC) law is synthesized on the fuzzy model. With FMPC, the asymptotic stability is guaranteed, and meanwhile the input constraints are satisfied by both the free control variables and the future control inputs in the form of the state feedback law. At last, the disturbance estimate and the FMPC are applied to constitute the DOBFMPC law. With the proper design of the disturbance compensation gain, the disturbance influence is removed from the output channels by the composite DOBFMPC law at the steady state. Simulations for a 300 MW subcritical BTS well demonstrate the effectiveness of the proposed control scheme.

This paper proposes a disturbance-observer-based fuzzy model predictive control (DOBFMPC) scheme for the nonlinear process subject to disturbances and input constraints. The proposed control scheme is composed of the baseline fuzzy model predictive control (FMPC) law designed on the Takagi–Sugeno fuzzy model and the disturbance compensation law. To build a fuzzy model of appropriate complexity and accuracy for the nonlinear process model, a systematic approach is developed via the gap metric to determine the linearization points. With FMPC, the asymptotic stability is theoretically proved, and the input constraints are satisfied by both the free control variables and the future control inputs in the form of the state feedback law. The disturbance compensation gain is designed such that the influence of the disturbance is removed from the output channels by the composite DOBFMPC law at the steady state. The application to a subcritical boiler–turbine system demonstrate the effectiveness of the proposed control scheme.

In thermal power plants, air preheaters are used to preheat the combustion air with residual thermal energy of the flue gas. The air leakage from air to flue gas has negative impact on the heat transfer efficiency of air preheaters and the unit thermal economy. This paper proposes an online applicable approach to estimate the direct leakage of the air preheater based on temperature distribution modeling. To describe the heat transfer process in the air preheater, energy balance equations are established after discretizing the air preheater into a number of heat transfer units, where the unstable heat transfer caused by the rotor rotation is considered. The temperature distribution of the air preheater is obtained through iterative calculation of these energy balance equations. By solving the optimization problems with the objective to meet the conservation laws of mass and energy, the direct leakage of the secondary air and the primary air is estimated. Pseudo-online simulations are carried out with the history data of a 300 MW subcritical unit to verify the approach. The updating period of the calculation results is set to 30 s so that the proposed approach may be applied for online monitoring purposes.

The uncertainty and disturbance estimator (UDE)-based robust control approach is widely investigated and applied due to its excellent performance and simple implementation. However, its stabilisability of unstable systems has not been documented. In this study, the sufficient and necessary stabilisability conditions of the UDE-based robust control are investigated. According to the stabilisability conditions, a systematical design method is presented for the reference model based on the controllable canonical transformation and pole placement. This is then applied to a magnetic levitation system (MagLev) subject to model uncertainties and external disturbance as an example. Simulation results are presented to illustrate the effectiveness of the proposed control approach with respect to matched uncertainties.

Grid voltage parameters are essential for the real-time control and protection in power systems. Current approaches mainly focus on the phase-locked loop methods, but which are slow and sensitive to voltage variations and disturbances. In this paper, the estimation of grid voltage parameters is recast as an observer design problem. A second-order sliding mode observer (SMO) is designed to estimate the grid voltage parameters based on the exosystem model. Moreover, the asymptotic stability of the closed-loop SMO is proved. Simulation and experimental results are presented to illustrate the effectiveness of the proposed approach, which achieves fast response and accurate estimation

The DC–DC power converter plays an important role in solar systems to provide the stable DC bus voltage, but which is easily subject to various uncertainties and disturbances in practical operation. In this paper, an uncertainty and disturbance estimator (UDE) based sliding mode control approach is applied to improve the performance of power converters. The UDE is designed for the estimation of both the matched and mismatched uncertainties. To address the mismatched uncertainties, an adaptive sliding mode function is constructed with the compensation of the estimated uncertainties, which renders a chattering-free robust control law. Simulation and experimental results illustrate that the proposed control scheme achieves good dynamic performance, strong robustness and chattering reduction in the presence of uncertainties and disturbances

As higher requirements are proposed for the load regulation and efficiency enhancement, the control performance of boiler-turbine systems has become much more important. In this paper, a novel robust control approach is proposed to improve the coordinated control performance for subcritical boiler-turbine units. To capture the key features of the boiler-turbine system, a nonlinear control-oriented model is established and validated with the history operation data of a 300 MW unit. To achieve system linearization and decoupling, an adaptive feedback linearization strategy is proposed, which could asymptotically eliminate the linearization error caused by the model uncertainties. Based on the linearized boiler-turbine system, a second-order sliding mode controller is designed with the super-twisting algorithm. Moreover, the closed-loop system is proved robustly stable with respect to uncertainties and disturbances. Simulation results are presented to illustrate the effectiveness of the proposed control scheme, which achieves excellent tracking performance, strong robustness and chattering reduction.

[Objective] To understand the relationship between species diversity and tree growth in natural secondary forests in Northeast China, to determine the reasonable size of species diversity, and to carry out appropriate nurturing harvesting and artificial replanting, so as to provide a scientific and theoretical basis for secondary forest management and management. [Methods] A total of 123 sample plots were set up in the Xiaoxinganling (XXAL), Zhangguangcailing (ZGCL), Laojialing (LYL), Changbai Mountain (CBS), Hadaling (HDL) and Longgang Mountain (LGS) areas in Northeast China, they were used to investigate the species composition, importance value, diversity and tree growth in each area. [Results] A total of 48 species belonging to 17 families and 31 genera were investigated in all the sample plots, among which the sample plots in Longgang Mountain contained the largest number of families, genera and species, followed by Hada Ling, Changbai Mountain, Laoyaling, Zhangguangcai Mountain and Xiaoxinganling. The α-diversity index of species in the sample sites was the largest in Changbai Mountain and the smallest in Xiaoxinganling, and the difference between them was significant (P < 0.05), while the richness index was the largest in Longgang Mountain and the smallest in Xiaoxinganling. The difference between them was significant (P < 0.05), while the greater the difference in latitude between the regions, the more obvious the difference in β-diversity index of species in the sample sites, and the fewer species shared between the two regions. The higher the rate of community succession, the higher the average diameter at breast height and the average tree height in each region were CBS > LYL > LGS > ZGCL > HDL > XXAL. The largest breast tree species in each region was Mongolian oak in Changbai Mountain with a diameter at breast height of 64.8 cm, and the smallest breast tree species in each region was Tyrannus sylvestris in Longgang Mountain with a diameter at breast height of 4.0 cm. The highest tree species in each region was Liriodendron sylvestris in Longgang Mountain with a height of 28.9 m, and the smallest species is yellow pineapple with a height of 1.3 m in Longgang Mountain. [Conclusion] Within a certain range, species diversity has a facilitating effect on the average diameter at breast height and average tree height of species within a stand. Therefore, during the management of secondary forests, appropriate nurturing harvesting and artificial replanting should be adopted to ensure reasonable species diversity in the stands and provide optimal space for the growth of natural secondary forests.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

A profit function is established to evaluate the economic performance of baker's yeast continuous production. A structured kinetic model for baker's yeast Saccharomyces cerevisiae is applied to estimate the key state variables of the process, such as biomass and ethanol concentration. Taking possible time variant dynamics into account, a rolling identification approach is used to identify several key model parameters online so as to adapt the changing dynamics of the process. The well predicted biomass concentration makes the profit function online predictable very well. This investigation may benefit the study on profit optimization of the process.

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, k L a, 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.

To track the rapidly changing temperature profiles of thermal cycling of polymerase chain reaction (PCR) accurately, an innovative feedforward variable structural proportional-integral-derivative (FVSPID) controller was developed. Based on the step response test data of the heat block, a reduced first order model was established at different operating points. Based on the reduced model, the FVSPID controller combined a feedforward path with the variable structural proportional-integral-derivative (PID) control. The modified feedforward action provided directly the optimal predictive power for the desired setpoint to speed up the dynamic response. To cooperate with the feedforward action, a variable structural PID was applied, where the P mode was used in the case of the largest errors to speed up response, whereas the PD mode was used in the case of larger errors to suppress overshoot, and finally the PID mode was applied for small error conditions to eliminate the steady state offset. Experimental results illustrated that compared to the conventional PID controller, the FVSPID controller can not only reduce the time taken to complete a standard PCR protocol, but also improve the accuracy of gene amplification.

We report a convenient and highly efficient method for the synthesis of beta-organotellurobutenolides by the aryltellurenyl halides-induced electrophilic tellurolactonization of alpha-allenoic acids under mild conditions. The resulting beta-organotellurobutenolides can be utilized as precursors for versatile butenolide derivatives through a substitution reaction with organocuprate reagent or Pd/Cu(I)-catalyzed cross-coupling with terminal alkyne.

A rolling learning-prediction approach based on neural networks is proposed with the aim of on-line prediction of the product formation. Commercial-scale penicillin cultivations were taken as an example to test the product predictor. Raw data are pretreated in such a way that each input vector of the neural network consists of a series of time-discretised values on a specified transient of process variables. The output vector is composed of the amount of product at the next one and two prediction steps. The process variables involved in the predictor include carbon dioxide and product formation as well as oxygen, precursor and substrate consumption. Accumulated rather than instant values of these variables were used. A simple three-layer feedforward backpropagation neural network with a tangent sigmoidal transfer function in the hidden nodes and a linear one in the output nodes was used as the main frame of the product predictor. The proposed prediction procedure is called rolling learning-prediction because the training database is updated after each sampling interval and the learning-prediction is repeated thereafter. The robustness of the predictor was illustrated by its adaptive ability to widely scattered data sets and extra added noises. The testing results indicated that a prediction accuracy of 2–5% could be generally expected in the later phase of cultivation and reliable prediction time spans may take more than 10% of the cultivation period for penicillin production. An intrinsic problem of using neural networks—occasional trap of the network in bad local minima—is automatically detected and remedied. In addition, it was illustrated by example that the prediction error signal may be potentially used to detect extraordinary charges caused, for example, by contamination. Problems associated with the industrial application of the predictor are discussed.

The goal of bioprocess-control and-scheduling is to maximize the profit of the whole process. Maximizing of the profit implies the optimal coordination among the subindices such as productivity, yield and quality of the product. These three subindices often interact with each other since the manipulating variables may influence them simultaneously. Therefore, the best way to control the process is to optimize the profit index directly. In this paper, it is illustrated by an example of industrial penicillin fed-batch production how the profit index, named as profit function, is established. The profit function is an objective description of a process under the aspect of economy. In reality, it may be found that the profit function is often different from batch to batch. Such a fluctuation is demonstrated by gibberellic acid, penicillin and cephalosporin fed-batch processes. This suggests that optimal scheduling of process time may be an effective way to enhance the total profit. Based on a database which consists of the characteristic description of the profit function of earlier batches, the procedures of classification and optimal scheduling decision are described in details. On-line profit optimization may be done in particular if a process model is available. This is demonstrated with cephalosporin C fed-batch cultivation as an example.

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.

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A macrokinetic model for Pichia pastoris expressing recombinant human serum albumin is proposed. The model describes the balances of some key metabolites, ATP and NADH, during glycerol and methanol metabolism. In the glycerol growth phase, the metabolic pathways mainly include phosphorylation, glycolysis, tricarboxylic acid cycle, and respiratory chain. In the methanol growth phase, methanol is oxidized to formaldehyde at first. Then, while a part of formaldehyde is oxidized to formate, the rest is condensed with xylulose-5-monophosphate to form glyceraldehyde-3-phosphate, and further assimilated to form cell constituents. The metabolic pathways following glyceraldehyde-3-phosphate were assumed to be similar to those in the glycerol growth phase. Based on the 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 obtained. The specific substrate consumption rate and the specific growth rate are then coupled into a bioreactor model such that the relationship between substrate feeding rates and the main state variables, i.e., the medium volume, the concentrations of the biomass, the substrate, and the product, is set up. Experimental results demonstrate that the model can describe the cell growth and the protein production with reasonable accuracy.