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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.

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 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.

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 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.

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.

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.

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.

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.

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.

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 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

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...

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.

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.

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.

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 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.

A new spike sorting method based on the support vector machine (SVM) is proposed to resolve the superposition problem. The spike superposition is generally resolved by the template matching. Previous template matching methods separate the spikes through linear classifiers. The classification performance is severely influenced by the background noise included in spike trains. The nonlinear classifiers with high generation ability are required to deal with the task. A multi-class SVM classifier is therefore applied to separate the spikes, which contains several binary SVM classifiers. Every binary SVM classifier corresponding to one spike class is used to identify the single and superposition spikes. The superposition spikes are decomposed through template extraction. The experimental results on the simulated and real data demonstrate the utility of the proposed method.

Support vector machines (SVM) are applied to the prediction of key state variables in bioprocesses, such as product concentration and biomass concentration, which commonly play an important role in bioprocess monitoring and control. A so-called rolling learning-prediction procedure is used to deal with the time variant property of the process, and to establish the training database for the SVM predictor, which is characterized with the rolling update of the training database. As an example, product concentration in industrial penicillin production is predicted, and a comparison is also made with three different artificial neural network architectures (FBNN, RBFN, and RNN). The test results indicate that a prediction accuracy of 1-3 % can be obtained for 4-40h ahead prediction using the SVM, which is better than the best of the three artificial neural networks (ANNs). Moreover, for noise-added training highly noisy data or small-sample learning, the SVM also clearly outper-forms FBNN, RBFN, and RNN.

A new method for spike sorting is proposed which partly solves the overlapping problem. Principal component analysis and subtractive clustering techniques are used to estimate the number of neurons contributing to multi-unit recording. Spike templates (i.e. waveforms) are reconstructed according to the clustering results. A template-matching procedure is then performed. Firstly all temporally displaced templates are compared with the spike event to find the best-fitting template that yields the minimum residue variance. If the residue passes the χ 2 -test, the matching procedure stops and the spike event is classified as the best-fitting template. Otherwise the spike event may be an overlapping waveform. The procedure is then repeated with all possible combinations of two templates, three templates, etc. Once one combination is found, which yields the minimum residue variance among the combinations of the same number of component templates and makes the residue pass the χ 2 -test, the matching procedure stops. It is unnecessary to check the remaining combinations of more templates. Consequently, the computational effort is reduced and the over-fitting problem can be partly avoided. A simulated spike train was used to assess the performance of the proposed method, which was also applied to a real recording of chicken retina ganglion cells.

In mycelia cultivation, the moiphology of filamentous microorganisms changes greatly along with the cultivation time, which causes variations in mass transfer and metabolic behavior of the microorganisms. To give an insight into the morphology-associated time-variant process dynamics in a simple way, a cell age model is proposed for Penicillium chrysogenum fed-batch cultivation. With help of this model the average ages of the segregated cell populations may be estimated. The age model is further combined with a segregated penicillin model found in the literature. The combined model system seems to work well which is shown by the verification results with commercial penicillin fed-batch cultivations.

In antibiotics industry, the titre in bioreactors is the most important process variable both for process supervision and scheduling. It is therefore of great significance to develop a software sensor to predict the product formation. In this contribution, a pseudo dynamic product predictor based on artificial neural network is designed. The input process variables of the predictor include substrates, precursor, nutrients and oxygen consumption, carbon dioxide evolution and also the product formation. These process variables are usually available in practice. Only the accumulated values of these variables rather than the instant ones are used. A feedforward neural network is chosen for one-step-ahead prediction of total product titre at the next step. The input vector of the neural network is the time scries of process variables over a predetermined time period. The database for network training is composed of a series of such vectors taken from historical charges which are processed as well as updated by a moving-data-window’s technique. The software sensor is tested by data of three industrial charges and ten model “generated” batches. MATLAB is used for training and testing of the neural network. The predictor is expected to be applied in optimal fermentation time scheduling for antibiotics production. Improvements and further development of the software sensor for real time applications 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.

An on-line profit optimization approach for baker’s yeast continuous fermentation was developed. The profit optimization was done based on the process model system and profit function which were earlier established. An on-line rolling identification method with moving-data-windows technique was used to modify the model system so as to adapt the different transients caused by different initial conditions from run to run and the time-varied dynamics of cell metabolism. With the help of such an adaptive process model, optimal molasses feeding strategies were determined by maximizing the profit function. Another primary manipulating variable, the process water feeding rate, was found to have no significant influence on the profit optimization within its normal operation range.

In baker's yeast continuous fermentation process, profit optimization is essentially the final target of all process control and scheduling measures. In this paper, an earlier proposed structured kinetic model for baker's yeast Saccharomyces cerevisiae was tested and modified with industrial fermentation data. With the support of the process model system, the profit function of the whole process is estimated This makes a further profit optimization possible.

Modeling and simulation technology of biological reaction is the important method of theoretical studies and production control. However the greatest technical difficulties of biological reaction modeling is how to identify and discuss the undetermined parameters existing in the complex system by analyzing limited experimental data— to look for an intelligent optimization algorithm that can optimize multivariable and mult-extremum high dimensional nonlinear functions effectively. This thesis took the fed-batch fermentation model in the penicillin industrial production for example, based on the industrial control rolling simulation software, discussed the application characteristics and their improvement of the real model of four widely used optimization algorithms: Simplex Algorithm, Genetic Algorithm, Simulated Algorithm and Particle Swarm Optimization. Lastly, the thesis proposed a new combined optimization algorithm— Genetic-simplex Algorithm by analyzing the complementarity of SIM and GA. Proved by the results of industrial rolling simulation this algorithm has combined the advantages of Simplex Algorithm and Genetic Algorithm, possessing excellent optimization ability.

In fed-batch production of baker’s yeast the profile of the sugar flow must regard the Crabtree-Effect and the oxygen transfer capacity of the reactor to avoid loss of yield. In this paper, optimal fed-batch control strategies are under investigation which also consider the final product quality. The optimization is carried out based on a mathematical process model that consists of three parts: The structured growth model can predict the growth kinetics in dependence of the operating conditions of the reactor; the age-distribution model for the budding process provides the fraction of budding cells (FBC) of the yeast population which is inversly correlated to the quality parameter fermentative activity; the cost model gives information on the economics of the process operation. New parts of the model are presented and simulation results with the model are compared with experimental data. The optimum operation of the Baker’s yeast fed-batch production is then investigated. Different from usual control trajectories, consideration of the quality criterion leads to a pulsed control of substrate flow. The optimum control predicted by the model was verified in laboratory-scale experiments.

A cell cycling model for unequal budding yeast Saccharomyces cerevisiae is proposed and verified by steady state data from experiments available in the literature. This model can be used to determine the relative fraction of the cells in any cycling phase or with any genealogical age during fermentation. As the quality of yeast is strongly influenced by the cycling process, the model could therefore be used to control the quality of the harvested yeast cells. The input of the cell cycling model is the specific growth rate μ, which is obtained from a metabolic model for S. cerevisiae proposed earlier. With this extended model system not only the quality control, but also the whole economical profit optimization can be carried out. Simulations were done to optimize the profit of a commercial scale baker's yeast production process by manipulating substrate feeding rate and substrate concentration under different aeration rates, fermentation periods and other conditions applied in industry.

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In thermal cycling of polymerase chain reaction, the process recipe is characterized by high ramp rates and short temperature holds. In order to reduce the time taken to complete a standard PCR protocol, a model-based hybrid control configuration is designed to rapidly track the thermal cycling recipe. The hybrid control configuration consists of feedforward, feedback and Bang-Bang actions, of which, the feedforward control is a model predictive control action and improves the dynamic performance of temperature tracking significantly. The model of the thermocycler is identified with step response data at different operating regions. Experimental results demonstrate that the hybrid control strategy performs better than the conventional PID controller both in setpoint response and in steady-state performance of temperature control, so that the accuracy and efficiency of PCR are improved accordingly.

The characteristics of the antibiotic fermentation process with parallel lines are analyzed. The assignment of tasks, units and events is described by two sets of binary variables. Based on continuous time representation, a mixed integer linear programming (MILP) model of the antibiotic fermentation process is built. The computational example of cephalosporin fermentation process demonstrates the feasibility and validity of the scheduling model

A multiple modeling algorithm for antibiotic fermentation process based on fuzzy c-means (FCM) and support vector regression (SVR) is proposed. By analyzing the features of antibiotic fermentation, the mechanism of multiple modeling of the bioprocess is presented. Using FCM clustering method, the bioprocess is classified into several work states and sub-models. Then, taking advantage of the generalization properties of SVR, the multiple model of bioprocess is established and the proposed algorithm is described. Experimental data of industrial penicillin production is used to validate the model.

The work outlined in this paper is towards developing a portable, economy blood glucose meter for self-monitoring of blood glucose, which has high performance-price ratio with multifunction and is attractive to the average people. Its functions include blood glucose concentration measurement, LCD display, historical records restore, calendar/timer. It enables the patients with diabetes to detect blood glucose with a glucose oxidase electrode independently at home. This paper will outline hardware configuration in the implementation and software management. Especially, graphic trend curve on LCD allows individuals to easily see how actual blood glucose readings compare with historical records stored in the meter and show how individuals stay within their specific target range so that they can determine how well they are controlling their diabetes. Experimental results indicate that this device is easy-to-use and suitable for home blood glucose monitoring for patients.

Spike sorting is a prerequisite for studying the multi-channel extracellular recordings. A new method is developed for spike sorting in multi-channel extracellular recordings of retinas. Based on the assumption that noise follows Gaussian distribution, spike templates are constructed based on principal component analysis and subtractive clustering, then a template matching procedure is applied to choose the best-fit template or templates combination for overlapping spikes so that after subtracting the template or templates combination from the spike event, the sum of squared residue follows Chi-square distribution. Artificial spike trains are used to assess the performance of the proposed method.