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This paper focuses on the power-level control of nuclear power plants (NPPs) in the presence of lumped disturbances. An adaptive second-order nonsingular terminal sliding mode control (ASONTSMC) scheme is proposed by resorting to the second-order nonsingular terminal sliding mode. The pre-existing mathematical model of the nuclear reactor system is firstly described based on point-reactor kinetics equations with six delayed neutron groups. Then, a second-order sliding mode control approach is proposed by integrating a proportional-derivative sliding mode (PDSM) manifold with a nonsingular terminal sliding mode (NTSM) manifold. An adaptive mechanism is designed to estimate the unknown upper bound of a lumped uncertain term that is composed of lumped disturbances and system states real-timely. The estimated values are then added to the controller, resulting in the control system capable of compensating the adverse effects of the lumped disturbances efficiently. Since the sign function is contained in the first time derivative of the real control law, the continuous input signal is obtained after integration so that the chattering effects of the conventional sliding mode control are suppressed. The robust stability of the overall control system is demonstrated through Lyapunov stability theory. Finally, the proposed control scheme is validated through simulations and comparisons with a proportional-integral-derivative (PID) controller, a super twisting sliding mode controller (STSMC), and a disturbance observer-based adaptive sliding mode controller (DO-ASMC).

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.

Grid voltage parameters, including the amplitude, frequency and phase angle, are the essential information for the control and protection of power systems. The sinusoid-locked loop (SLL) is a novel approach to estimate the grid voltage parameters, which achieves accurate estimation and strong robustness in the presence of harmonic disturbances. However, the conventional SLL is sensitive to large voltage variations or computation errors, which may fail to achieve accurate estimation when the controller is trapped in the undesired equilibrium. In this paper, a modified sinusoid-locked loop (MSLL) is designed to improve the estimation performance with respect to large voltage variations. A virtual stator impedance is introduced to eliminate the undesired equilibrium, which mimics the armature reaction of a virtual synchronous machine (VSM). In addition, the closed-loop stability is investigated based on the small-signal model of the proposed MSLL. Following the idea of the VSM, 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. Simulation and experimental results are presented to illustrate the effectiveness of the proposed MSLL, which achieves excellent estimation performance in the presence of large voltage variations and harmonic disturbances.

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.

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.

The flue gas temperature is an important parameter for safe and economic operation of thermal power plants. However, in high temperature areas, it is difficult to measure the temperature directly because the sensors are subject to flying ash erosion. This paper establishes a dynamic heat transfer model for the furnace and the semi-radiative heat exchangers, and proposes an estimation approach of the flue gas temperature in the boiler’s horizontal flue. The boiler-side heat absorption models, which cover the heat exchangers, the metal wall and the heat losses are established to calculate the heat absorption rate of the working fluid in the boiler. By solving the optimization problems which are developed based on the dynamic heat transfer models, the flue gas temperature at the outlet of the furnace and the semi-radiative heat exchangers are obtained. Pseudo-online simulations are performed with the history data of an ultra-supercritical unit to verify the approach. The relative errors between the calculated and the designed flue gas temperature with both designed and calibrated feed coal are found less than 3.5%. Additionally, the proportion of the convective heat in the total heat absorption for each semi-radiative heat exchanger is obtainable with the same approach.

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.

The load following of nuclear power plants (NPPs) has been a contentious issue in the control field. In this paper, we propose a chattering-free higher order sliding mode control scheme with a high-gain observer for the load following of a pressurized water reactor (PWR) in the presence of lumped disturbances owing to model uncertainties and external disturbances. The mathematical model of the PWR system is first set up in the form of an affine nonlinear equation. Subsequently, a high-gain observer, which achieves accurate estimations of the unmeasured state and lumped disturbances, is designed. Based on the outputs of the high-gain observer, we develop a chattering-free higher order sliding mode controller to improve the load-following performance while dealing with lumped disturbances and estimation errors of the high-gain observer. In contrast to some previous sliding mode controllers for the load following of NPPs, the proposed controller is completely free from chattering effects because the control input is obtained after integration. The asymptotic stability of the overall control scheme is demonstrated by combining the Lyapunov stability theory with backstepping technology. Finally, the simulation results reveal that the maximum absolute power error is less than 1 × 10 4 W with the proposed control scheme, 5 × 10 5 W with a PID controller, and 4 × 10 5 W with a conventional sliding mode controller. In addition, in contrast to the conventional sliding mode controller, the proposed control scheme produces smooth control input without the chattering phenomenon. Thus, the proposed chattering-free higher order sliding mode control scheme with a high-gain observer provides smoother control input, higher load-following accuracy, and stronger robustness against lumped disturbances than the PID controller and the conventional sliding mode controller.

Online performance monitoring is crucial for the safe and economical operation of thermal power plants. In this study, dynamic models of the whole process are established for the subcritical coal-fired power plant in terms of thermodynamic balances, which covers both boiler-side and turbine-side processes. Based on the whole process models, a monitoring platform is constructed with the functions of online estimation of key state variables and performance evaluation of the unit. The evaluation results of the unit efficiency with the time period of every 5 s may be provided. Model validations are carried out with the history operation data derived from the distributed control system (DCS) of a real 300 MW power plant. Comparisons between the simulation results and designed values of the unit demonstrate the high accuracy of the whole process models. The monitoring platform may also play the role as an offline simulator for the purpose of double check of the unit’s operation during specific time period and accidental cases.

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.

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.

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.

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.

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.

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.

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.

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.

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%.

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.

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 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.

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.

Recurrent network models are widely used to characterize the behaviors of neurons in the visual cortex. However, they are seldom used to simulate neurons in the retina. In this study, two slightly different recurrent network models are introduced to describe two types of non-concentric ganglion cells, i.e., the impressed-by-contrast cell and the suppressed-by-contrast cell in the cat retina. By simulations, it is found that the additive recurrent network is able to describe qualitatively the behavior of the impressed-by-contrast cell, while the other additive recurrent network with saturation rectification is able to describe qualitatively the behavior of the suppressed-by-contrast cell.

A cell cycle model is proposed for methylotrophic yeast Pichia pastoris grown on glycerol during fed-batch cultivation. Morphological differentiation of cells, such as unbudded daughter cell, unbudded parent cell and budding cell, is depicted by the model. During the cyclic growth, cells in different cycling period are assumed to undergo sequential shifting dominantly. The input of the cell cycle model is the specific growth rate, which is calculated from the macrokinetic model proposed previously. The cell cycle related variables, such as the fraction of budding cells and the cell density are then simulated. Model validation is carried out with the experimental data of off-line assays.