The snowball effect in fed-batch bioreactions

The bioreactor will play an important role in future biological manufacturing. For economic profit, important profiles of the feed rate in fed-batch cultures have been discussed. Unfortunately, the optimal feed rate is less robust. In these studies there exists the snowball effect in a substrate-inhibited bioprocess, in which substrate is accumulated due to uncertain parameters in the model or feed-rate error. The snowball effect also exists in multi-substrate-limited processes. In further studies, the interaction between the substrates has been higher in essential substrates than in growth-enhancing substrates. In a typical fed-batch bioreactor, the amount of the product can be reduced to 1% or less when the snowball effect arises. A new control structure, i.e., an off-line optimized feedforward controller added to a gain-scheduling PI(2)D feedback controller, is proposed to eliminate the troublesome snowball effect. The proposed control strategy recovers the yield up to 95%. Moreover, the robustness of the proposed control structure is demonstrated by simulation.     

Fuzzy logic control of a fed-batch fermentor with significant measurement delay

A rule based fuzzy logic controller is developed for control of product concentration in a fed-batch fermentor with a significant measurement delay. The performance of the delay compensated fuzzy logic controller is compared by simulation with that of a delay uncompensated fuzzy controller and with that of a conventional proportional and derivative (PD) controller.     

Fuzzy control of ethanol concentration its application to maximum glutathione production in yeast fed-batch culture

A fuzzy logic controller (FLC) for the control of ethanol concentration was developed and utilized to realize the maximum production of glutathione (GSH) in yeast fedbatch culture. A conventional fuzzy controller, which uses the control error and its rate of change in the premise part of the linguistic rules, worked well when the initial error of ethanol concentration was small. However, when the initial error was large, controller overreaction resulted in an overshoot.An improved fuzzy controller was obtained to avoid controller overreaction by diagnostic determination of "glucose emergency states" (i.e., glucose accumulation or deficiency), and then appropriate emergency control action was obtained by the use of weight coefficients and modification of linguistic rules to decrease the overreaction of the controller when the fermentation was in the emergency state. The improved fuzzy controller was able to control a constant ethanol concentration under conditions of large initial error.The improved fuzzy control system was used in the GSH production phase of the optimal operation to indirectly control the specific growth rate mu to its critical value mu(c). In the GSH production phase of the fed-batch culture, the optimal solution was to control mu to mu(c) in order to maintain a maximum specific GSH production rate. The value of mu(c) also coincided with the critical specific growth rate at which no ethanol formation occurs. Therefore, the control of mu to mu(c) could be done indirectly by maintaining a constant ethanol concentration, that is, zero net ethanol formation, through proper manipulation of the glucose feed rate. Maximum production of GSH was realized using the developed FLC; maximum production was a consequence of the substrate feeding strategy and cysteine addition, and the FLC was a simple way to realize the strategy. (c) 1993 John Wiley & Sons, Inc.     

Control of unstable bioreactor using fuzzy tuned PI controller

A fuzzy tuning scheme for conventional PI controller is developed for controlling an unstable continuous bioreactor. The performance is compared with that of a fixed setting conventional PI controller. The performance of the tuning scheme is studied by simulating the non-linear model equations of the bioreactor. The robustness of the controller is also studied for uncertainties in the process parameters such as yield factor and measurement delay. Simulation results show that the fuzzy tuning improves the overall performance and particularly it is more robust to parameter uncertainties.     

Fuzzy logic control of an unstable bioreactor

A rule based fuzzy controller (FLC) is developed for stabilization of an unstable continuous stirred tank bioreactor (CSTBR) from various start-up conditions. The output variable is the reactor substrate concentration and the manipulated variable is the dilution rate. The performance of the FLC is evaluated by simulating a mathematical model of an unstable CSTBR. FLC is robust to perturbations in the specific growth rate, specific consumption rate and also to a disturbance in the feed substrate concentration. The performance of the FLC is superior to that of a conventional proportional controller.     

Control and fault diagnosis in bottom fermentation systems using parity space approach

The main objective of this note is to describe a real-time fault diagnosis and control for a cylindroconical fermenter to laboratory scale as an alternative to the classic systems. Development of a good controller for a fed-batch reactor is not enough without a fault diagnosis system. We are working to expand this idea. The failure detection system is based on the parity space approach joined with a fuzzy controller with more robustness and of superior performance in MIMO systems compared to conventional strategies. A 2-week period is reported.     

Fuzzy self-tuning PI controller for bioreactors

A fuzzy self-tuned PI controller for regulation of a nonlinear bioreactor is presented. The basic idea is to parameterize Ziegler-Nichols like tuning formula by two parameters and and then to use an on-line fuzzy inference mechanism to tune the PI controller parameters k _c and _I . The fuzzy self-tuning method takes the process output error as input and the tuning parameters and as outputs. Simulation studies on the nonlinear bioreactor model equations show that the present method is superior to that of fixed parameters conventional PI controller (based on transfer function) for both servo and regulatory problems. The present fuzzy logic controller is robust to process parameters uncertainties and to changes in magnitude and direction of the disturbances.     

A fuzzy logic controller to control nutrient dosage in a fed-batch baker's yeast process

A fuzzy logic controller designed to control glucose feeding in a fed-batch baker's yeast process is presented. Feeding is carried out in portions and the controller determines the time at which glucose should be added and computes the size of the portion to provide the maximum glucose uptake rate. Moreover, the controller detects and prevents the occurrence of overdosage. The experimental results indicate that yield and specific growth rate obtained with the controller approached 55% and 0.13 h^–1, respectively.     

Design and simulation of a fuzzy controller for fed-batch yeast fermentation

In baker's yeast fermentation, the process is non-linear and the response of the system to changes in glucose feeding has a very long delay time. Therefore, a conventional system can not give satisfactory results. In this paper, a fuzzy controller designed to control a fed-batch fermenter is presented. The fuzzy controller uses Respiratory Quotient (RQ) as a controller input and produces glucose feeding rate as control variable. The controller has been tested on a simulated fed-batch fermenter. The results show that the maximum yeast production is possible by keeping the specific growth rate (μ) and the glucose concentration (C _s) at preset values (μ _Cand C _s,c) and minimizing the ethanol production.     

Fed-batch culture of yeast Saccharomyces cerevisiae with a DO-stat method by a fuzzy controller

In this research a fuzzy controller was built to perform fed-batch cultures of Saccharomyces cerevisiae with a DO-stat method. The basic principle of fed-batch culture employing the DO-stat method is that a rapid increase of dissolved oxygen concentration due to a lack of substrate (the DO signal) is used as an indicator for substrate feeding. The proposed fuzzy controller can diagnose the state of fermentation and determine a proper feed rate of substrate for the culture of high density and high yield. The results indicate that cell concentration reached to 110?g/l and residual sugar kept below the level of 0.05?g/l.     

Simulation of L/A control in fed-batch culture of baker's yeast

L/A controllers have extended their use from continuous to fed-batch fermentation where the control is applied from the start of an initial batch phase. As opposed to proportional integral derivative (PID) controllers where even a startup procedure is recommended prior to fed-batch, the L/A controller is not upset by an early connection. It is easily retuned continuously by means of ethanol measurements and can cope with a large range of output conditions. The performance of an L/A algorithm, which uses biomass concentration as the controlled variable, is assessed through simulation. The self-contained algorithm is relatively simple with no greater intrinsic complexity than modern PID stand alone controllers.     

A fuzzy logic controller.

This paper describes a fuzzy sets method which is very useful for handling uncertainties and essential for knowledge acquisition of a human expert. Kinetics of a reactor is often complex and not trivial to describe by mathematical equations. Reactor control by traditional control technology is therefore difficult. A novel technology is presented. In the following a fuzzy inference (approximate reasoning) is used for decision making in analogy to human thinking, facilitating a more sophisticated control. Readers of this paper do not need any advanced mathematics beyond the four basic operations in arithmetic (+, -, x, divided by) and using the maximum and minimum values. This fuzzy inference is introduced to construct a fuzzy logic controller which is suitable for a nonlinear, multivariable and time variant system applied to a bioreactor.     

On-line multi-analyzer monitoring of biomass, glucose and acetate for growth rate control of a Vibrio cholerae fed-batch cultivation

In situ near-infrared (NIR) spectroscopy and in-line electronic nose (EN) mapping were used to monitor and control a cholera-toxin producing Vibrio cholerae fed-batch cultivation carried out with a laboratory method as well as with a production method. Prediction models for biomass, glucose and acetate using NIR spectroscopy were developed based on spectral identification and partial-least squares (PLS) regression resulting in high correlation to reference data (standard errors of prediction for biomass, glucose and acetate were 0.20 gl(-1), 0.26 gl(-1) and 0.28 gl(-1)). A compensation algorithm for aerated bioreactor disturbances was integrated in the model computation, which in particular improved the prediction by the biomass model. First, the NIR data were applied together with EN in-line data selected by principal component analysis (PCA) for generating a trajectory representation of the fed-batch cultivation. A correlation between the culture progression and EN signals was demonstrated, which proved to be beneficial in monitoring the culture quality. It was shown that a deviation from a normal cultivation behavior could easily be recognized and that the trajectory was able to alarm a bacterial contamination. Second, the NIR data indicated the potential of predicting the concentration of formed cholera toxin with a model prediction error of 0.020 gl(-1). Third, the on-line biomass prediction based on the NIR model was used to control the overflow metabolism acetate formation of the V. cholerae culture. The controller compared actual specific growth rate as estimated from the prediction with the critical acetate formation growth rate, and from that difference adjusted the glucose feed rate.     

Online automatic tuning and control for fed-batch cultivation

Performance of controllers applied in biotechnological production is often below expectation. Online automatic tuning has the capability to improve control performance by adjusting control parameters. This work presents automatic tuning approaches for model reference specific growth rate control during fed-batch cultivation. The approaches are direct methods that use the error between observed specific growth rate and its set point; systematic perturbations of the cultivation are not necessary. Two automatic tuning methods proved to be efficient, in which the adaptation rate is based on a combination of the error, squared error and integral error. These methods are relatively simple and robust against disturbances, parameter uncertainties, and initialization errors. Application of the specific growth rate controller yields a stable system. The controller and automatic tuning methods are qualified by simulations and laboratory experiments with Bordetella pertussis.     

Dynamic fuzzy model based predictive controller for a biochemical reactor

The kinetics of bioreactions often involve some uncertainties and the dynamics of the process vary during the course of fermentation. For such processes, conventional control schemes may not provide satisfactory control performance and demands extra effort to design advanced control schemes. In this study, a dynamic fuzzy model based predictive controller (DFMBPC) is presented for the control of a biochemical reactor. The DFMBPC incorporates an adaptive fuzzy modeling framework into a model based predictive control scheme to derive analytical controller output. The DFMBPC has the flexibility to opt with various types of fuzzy models whose choice also lead to improve the control performance. The performance of DFMBPC is evaluated by comparing with a fuzzy model based predictive controller (FMBPC) with no model adaptation and a conventional PI controller. The results show that DFMBPC provides better performance for tracking setpoint changes and rejecting unmeasured disturbances in the biochemical reactor.     

A cultivation technique for <Emphasis Type="Italic">E. coli</Emphasis> fed-batch cultivations operating close to the maximum oxygen transfer capacity of the reactor

A cultivation strategy combining the advantages of temperature-limited fed-batch and probing feeding control is presented. The technique was evaluated in fed-batch cultivations with E. coli BL21(DE3) producing xylanase in a 3 liter bioreactor. A 20% increase in cell mass was achieved and the usual decrease in specific enzyme activity normally observed during the late production phase was diminished with the new technique. The method was further tested by growing E. coli W3110 in a larger bioreactor (50 l). It is a suitable cultivation technique when the O₂ transfer capacity of the reactor is reached and it is desired to continue to produce the recombinant protein.Revisions requested 13 April 2005; Revisions received 6 May 2005     

Uniform step-by-step observer for aerobic bioreactor based on super-twisting algorithm

This paper describes a fixed-time convergent step-by-step high order sliding mode observer for a certain type of aerobic bioreactor system. The observer was developed using a hierarchical structure based on a modified super-twisting algorithm. The modification included nonlinear gains of the output error that were used to prove uniform convergence of the estimation error. An energetic function similar to a Lyapunov one was used for proving the convergence between the observer and the bioreactor variables. A nonsmooth analysis was proposed to prove the fixed-time convergence of the observer states to the bioreactor variables. The observer was tested to solve the state estimation problem of an aerobic bioreactor described by the time evolution of biomass, substrate and dissolved oxygen. This last variable was used as the output information because it is feasible to measure it online by regular sensors. Numerical simulations showed the superior behavior of this observer compared to the one having linear output error injection terms (high-gain type) and one having an output injection obtaining first-order sliding mode structure. A set of numerical simulations was developed to demonstrate how the proposed observer served to estimate real information obtained from a real aerobic process with substrate inhibition.     

Hybrid control strategies for a five-finger robotic hand

This paper presents a hybrid controller of soft control techniques, adaptive neuro-fuzzy inference system (ANFIS) and fuzzy logic (FL), and hard control technique, proportional-derivative (PD), for a five-finger robotic hand with 14-degrees-of-freedom (DoF). The ANFIS is used for inverse kinematics of three-link fingers and FL is used for tuning the PD parameters with 2 input layers (error and error rate) using 7 triangular membership functions and 49 fuzzy logic rules. Simulation results with the hybrid of FL-tuned PD controller exhibit superior performance compared to PD, PID and FL controllers alone.     

Fuzzy supervisory control of fed-batch baker's yeast process

Summary A fuzzy supervisory system for bioprocess control was developed, and applied to baker's yeast fermentation. The system was based on hierarchical bioprocess control with fuzzy phase recognition and separate fuzzy control of each process phase. A two-level knowledge base included rules both for the phase recognition and control. The system was tested by using experimental data of fed-batch baker's yeast cultivations and by process simulations.     

Development of a large-scale biocalorimeter to monitor and control bioprocesses

Calorimetry has shown real potential at bench-scale for chemical and biochemical processes. The aim of this work was therefore to scale-up the system by adaptation of a standard commercially available 300-L pilot-scale bioreactor. To achieve this, all heat flows entering or leaving the bioreactor were identified and the necessary instrumentation implemented to enable on-line monitoring and dynamic heat balance estimation. Providing that the signals are sufficiently precise, such a heat balance would enable calculation of the heat released or taken up during an operational (bio)process. Two electrical Wattmeters were developed, the first for determination of the power consumption by the stirrer motor and the second for determination of the power released by an internal calibration heater. Experiments were designed to optimize the temperature controller of the bioreactor such that it was sufficiently rapid so as to enable the heat accumulation terms to be neglected. Further calibration experiments were designed to correlate the measured stirring power to frictional heat losses of the stirrer into the reaction mass. This allows the quantitative measurement of all background heat flows and the on-line quantitative calculation of the (bio)process power. Three test fermentations were then performed with B. sphaericus 1593M, a spore-forming bacterium pathogenic to mosquitoes. A first batch culture was performed on a complex medium, to enable optimization of the calorimeter system. A second batch culture, on defined medium containing three carbon sources, was used to show the fast, accurate response of the heat signal and the ability to perfectly monitor the different growth phases associated with growth on mixed substrates, in particular when carbon sources became depleted. A maximum heat output of 1100 W was measured at the end of the log-phase. A fed-batch culture on the same defined medium was then carried out with the feed rate controlled as a function of the calorimeter signal. A maximum heat output of 2250 W was measured at the end of the first log-phase. This work demonstrates that real-time quantitative calorimetry is not only possible at pilot-scale, but could be readily applied at even larger scales. The technique requires simple, readily available devices for determination of the few necessary heat flows, making it a robust, cost-effective technique for process development and routine monitoring and control of production processes.