On-line estimation of the specific growth rate based on viable biomass measurements: experimental validation

The application of model based control techniques to biotechnological processes is often hampered due to the lack of reliable on-line sensors. This problem can be tackled by the application of software sensors, in which the available hardware measurements are combined with the model equations. The resulting estimates serve as additional measurements useful for process monitoring and control. In this paper, an observer based estimator for the specific growth rate based on on-line viable biomass measurements is studied. Several fed-batch experiments with baker's yeast in a stirred tank bioreactor illustrate the design, tuning, and implementation from a practical point of view. The main contributions of this paper are to illustrate (i) the implementation and validation of the presented algorithm in real-time, (ii) the use of an advanced on-line biomass measurement, and (iii) the design and tuning of the algorithm from a practical point of view. Real-time knowledge of the specific growth rate is important because it yields information on the viability of the cells and it can be used in real-time feedback control algorithms.     

An adaptive state estimator for detecting contaminants in bioreactors

An algorithm is presented for detecting the appearance of contaminants during batch or fed-batch fermentations, using only presently available on-line measurements. Its adaptive nature enables it to rely on almost no prior knowledge of the real process. The necessary on-line measurements are total biomass and its production rate; it is also shown how a physical variable such as oxygen uptake can be used alone instead. The algorithm's properties are studied theoretically and through simulations. These were confirmed by on-line experimental results, obtained with a Yeast culture, both pure and contaminated by a Bacteria. The algorithm does not detect contaminants when none are there, and it also provides a convergent estimate of a pure culture's specific growth rate. Contaminated cultures are recognized by the algorithm, and this detection can be made more or less conservative. After detection, the various estimates may diverge, due to general observability difficulties, though this divergence can itself be monitored. Moreover, the algorithm is easy to tune and its qualitative behavior is quite insensitive to its adjustable parameters. A practical criterion and scheme for implementation are proposed. The generality of the approach, which far exceeds the experimental system used, is finally discussed.     

Estimation of multiple biomass growth rates and biomass concentration in a class of bioprocesses

In this paper, an approach to the estimation of multiple biomass growth rates and biomass concentration is proposed for a class of aerobic bioprocesses characterized by on-line measurements of dissolved oxygen and carbon dioxide concentrations, as well as off-line measurements of biomass concentration. The approach is based on adaptive observer theory and includes two steps. In the first step, an adaptive estimator of two out of three biomass growth rates is designed. In the second step, the third biomass growth rate and the biomass concentration are estimated, using two different adaptive estimators. One of them is based on on-line measurements of dissolved oxygen concentration and off-line measurement of biomass concentrations, while the other needs only on-line measurements of the carbon dioxide concentration. Simulations demonstrated good performance of the proposed estimators under continuous and batch-fed conditions.     

Adaptive steady-state optimization of biomass productivity in continuous fermentors

An adaptive steady-state optimization algorithm is presented and applied to the problem of optimizing the production of biomass in continuous fermentation processes. The algorithm requires no modeling information but is based on an on-line identified linear model, locates the optimum dilution rate, and maintains the chemostat at its optimum operating condition at all times. The behavior of the algorithm is tested against a dynamic model of a chemostat that incorporates metabolic time delay, and it is shown that large disturbances in the subtrate feed concentration and the specific growth rate, causing a shift in the optimum, are handled well. The developed algorithm is also used to drive a methylotroph single-cell production process to its optimum.     

Nonlinear predictive control for maximization of CO2 bio-fixation by microalgae in a photobioreactor

In the framework of environment preservation, microalgae biotechnology appears as a promising alternative for CO₂ mitigation. Advanced control strategies can be further developed to maximize biomass productivity, by maintaining these microorganisms in bioreactors at optimal operating conditions. This article proposes the implementation of Nonlinear Predictive Control combined with an on-line estimation of the biomass concentration, using dissolved carbon dioxide concentration measurements. First, optimal culture conditions are determined so that biomass productivity is maximized. To cope with the lack of on-line biomass concentration measurements, an interval observer for biomass concentration estimation is built and described. This estimator provides a stable accurate interval for the state trajectory and is further included in a nonlinear model predictive control framework that regulates the biomass concentration at its optimal value. The proposed methodology is applied to cultures of the microalgae Chlorella vulgaris in a laboratory-scale continuous photobioreactor. Performance and robustness of the proposed control strategy are assessed through experimental results.     

Two-dimensional fluorescence spectroscopy: a novel approach for controlling fed-batch cultivations

In industrial fed-batch cultivations it is often necessary to control substrate concentrations at a low level to prevent the production of overflow metabolites and thus optimize the biomass yield. A new method for on-line monitoring and fed-batch control based on fluorescence measurements has been developed. Via instantaneous in situ measurements and multivariate data analysis a chemometric model has been established, which enables the rapid detection of ethanol production at aerobic Saccharomyces cerevisiae fed-batch cultivations. The glucose feed rate is controlled by predicting the metabolic state directly from the fluorescence intensities. Thus, ethanol production could be avoided completely while increasing the biomass yield accordingly. The robust instrumentation is suitable for industrial applications.     

Real time monitoring biomass concentration in Streptomyces clavuligerus cultivations with industrial media using a capacitance probe

On-line monitoring biomass concentration in mycelial fed-batch cultivations of Streptomyces clavuligerus grown with soluble and partially insoluble complex media, was investigated with an in-situ capacitance probe fitted to an industrial pilot-plant tank. Standard off-line and on-line biomass determinations, including cell dry weight, packed mycelial volume, viscosity, DNA concentration and total CO(2) evolution in the exhaust gases, were performed throughout the experiments and compared to on-line capacitance measurements. Linear relations between capacitance and all other measurements were developed for both media that hold only in defined process phases, depending on the biomass state and the amount of insoluble matter present. For the industrial complex culture media good linear relations were obtained in the fast growth phase between capacitance and DNA concentration and total CO(2) evolution, while in the subsequent transition and stationary phases only with apparent viscosity was a reasonable correlation found. The capacitance probe was shown to be a valuable tool for real-time monitoring biomass concentration in industrial-like cultivation of mycelial streptomycetes.     

On-line estimation of the maximum specific growth rate of nitrifiers in activated sludge systems.

The on-line estimation of the maximum specific growth rate of autotrophic biomass is addressed in this article. A general nitrification process model, which is valid for any realistic flow pattern, is used to develop the estimation algorithm. Depending on the measurements available, two estimation equations are derived. While both require measuring the nitrification activity of the activated sludge, one requires the additional measurement of the nitrifiable nitrogen concentrations at the two ends of the bioreactor, and the other requires the nitrate nitrogen concentrations at the same locations. The algorithm also requires some stoichiometric and kinetic parameters. However, sensitivity analysis shows that the estimate is insensitive to the parameters other than the autotrophic decay rate. Compared to the existing algorithms, the algorithm developed in this article does not rely on the assumption of ideal flow pattern in the plant and does not require an error-prone estimate of the autotrophic biomass concentration. Experimental and simulation studies show that the algorithm performs well and is robust to influent variations and accidental sludge losses.     

Stable adaptive algorithm for simultaneous estimation of time-varying parameters and state variables in aerobic bioprocesses

The application of modern model based control algorithms in the bioprocesses is hampered by the lack of accurate and cheap on-line sensors, capable of providing on-line measurements of the main process variables and parameters. In this paper, a new approach for estimation of immeasurable time-varying parameters and state variable is presented for a class of aerobic bioprocesses using only on-line measurements of the oxygen uptake rate. The approach consists in the design of a new parameter estimator of biomass growth rate and yield coefficient for oxygen consumption on the basis of the theory of adaptive estimation. The dynamical equation of the measurable reaction rate, oxygen uptake rate, is presented as a linear one with respect to the biomass growth rate and the yield coefficient for oxygen consumption. In this way, the structure of the proposed estimator becomes linear time-varying one. After some mathematical transformations, that structure is presented in a form, allowing to be derived the stability conditions using some theoretical results concerning the stability of adaptive observers. The estimates of the yield coefficient for oxygen consumption, the biomass concentration and specific growth rate are obtained then on the basis of the generated estimates using well known kinetic models of bioprocesses. With respect to previous similar approaches, the new estimation algorithm gives stable estimates not only of immeasurable state variable and reaction rates but likewise of an yield coefficient. The behavior of the proposed estimator is studied under inexact initial conditions, step changes of dilution rate and in the presence of measurement noise by simulations using a process model, which belongs to the investigated class of bioprocesses.     

Feedback stabilization of fed-batch bioreactors: non-monotonic growth kinetics

This paper deals with the design of a feedback controller for fed-batch microbial conversion processes that forces the substrate concentration C(S) to a desired setpoint, starting from an arbitrary (initial) substrate concentration when non-monotonic growth kinetics apply. This problem is representative for a lot of industrial fermentation processes, with the baker's yeast fermentation as a well-known example. It is assumed that the specific growth rate mu is function of the substrate concentration only. A first approach exploits the availability of on-line measurements of both the substrate and biomass concentration. A second approach is merely based on on-line measurements of the biomass concentration, which provide an estimate for the specific growth rate. After a reformulation of the substrate concentration setpoint into a specific growth rate setpoint, it is demonstrated that the fed-batch process can still be stabilized around any desired operating point along the non-monotonic kinetics.     

A new method for the adaptive determination of optimum pH and temperature

An adaptive control algorithm for the on-line determination of optimal temperature or pH for biomass production in a continuous fermentor is presented. The algorithm requires no prior information and uses a dynamic Hammerstein model to identify parameters and to estimate an optimal steady-state control value. A check of the estimated performance measure second derivative is included to ensure that the target extremum is an optimum. The process is driven towards this optimum with a variable step size that depends on the quality of the on-line identified model. Numerical simulations are performed on a dynamic chemostat model that incorporates a metabolic time delay. The algorithm successfully finds the optimum temperature or pH values and maintains the reactor at the optimum steady state.     

On-line state estimation of biomass based on acid production in Zymomonas mobilis cultures

The concentrations of biomass, substrate and product are very important state variables of almost every bioprocess and generally unable to be measured directly in?situ due to the lack of reliable sensors. In this paper, an adaptive observer of the biomass concentration is proposed for an anaerobic fermentation process where only the measurement of the acid product is available on-line. The observer was tested to be effective by several experiments under various operating conditions. In this experimental system, an auto-sampling device was connected between the bioreactor for the fermentation of Zymomonas mobilis and a HPLC so that the concentrations of glucose and ethanol could be directly measured through such implementation.     

Adaptive on-line model for aerobic Saccharomyces cerevisiae fermentation

In order to study and control fermentation processes, indirect on-tine measurements and mathematical models can be used. In this article we present a mathematical on-line model for fermentation processes. The model is based on atom and partial mass balances as well as on equations describing the acid-base system. The model is brought into an adaptive form by including transport equations for mass transfer and unstructured expressions for the fermentation kinetics. The state of the process, i.e., the concentrations of biomass, substrate, and products, can be estimated on-line using the balance part of the model completed with measurement equations for the input and output flows of the process. Adaptivity is realized by means of on-line estimation of parameters in the transport and kinetic expressions using recursive regression analysis. These expressions can thus be used in the model as valid equations enabling prediction of the process. This makes model-based automation of the process and testing of the validity of the measurement variables possible. The model and the on-line principles are applied to a 3.5-L laboratory tormentor in which Saccharomyces cerevisiae is cultivated. The experimental results show that the model-based estimation of the state and the predictions of the process correlate closely with high-performance liquid chromatography (HPLC) analyses. (c) 1995 John Wiley & Sons, Inc.     

A Computer-aided noninterfering on-line technique for monitoring oxygen-transfer characteristics during fermentation processes

The role of computers in the monitoring and control of fermentation processes has increased steadfastly. The ultimate utility of the machines will not depend on the availability of online sensors but also on the availability of techniques that combine direct measurements, leading towards estimates of variable closely related to the microbial process or its control. In this article, a methodology for on-line and noninterfering evaluation of the volumetric mass-transfer coefficient K(l)a is developed. A detailed presentation of the procedure, called "the static method," is given. Its feasibility is proved through implementation of the method on an antibiotic fermentation process. These experiments indicate that operator actions meant to modify the oxygen-transfer conditions can be checked on-line. The quantitative value of the static method is ascertained by comparing the experimental results with K(l)a estimates obtained with the "gassing-out" method. A sensitivity analysis was carried out, revealing the need for temperature and pressure corrections and showing that the precision of the oxygen analyzer determines the precision of the static method.     

Structured modeling and state estimation in a fermentation process: Lipase production by Candida rugosa

A simple structured mathematical model coupled with a methodology of state and parameter estimation is developed for lipase production by Candida rugosa in batch fermentation. The model describes the system according to the following qualitative observations and hypothesis: Lipase production is induced by extracellular oleic acid present in the medium. The acid is transported into the cell where it is consumed, transformed, and stored. Lipase is excreted to the medium where it is distributed between the available oil-water interphase and aqueous phase. Cell growth is modulated by the intracellular substrate concentration. Model parameters have been determined and the whole model validated against experiments not used in their determination. The estimation problem consists in the estimation of three state variables (biomass, intra- and extracellular substrate) and two kinetic parameters by using only the on-line measurement provided by exhaust gas analysis. The presented estimation strategy divides the complex problem into three subproblems that can be solved by stable algorithms. The estimation of biomass (X) and the specific growth rate (mu), is achieved by a recursive prediction error algorithm using the on-line measurement of the carbon dioxide evolution rate. mu is then used to perform an estimation of intracellular substrate and the other kinetic parameter related to substrate transport (A) by an adaptive observer. Extracellular substrate is then evaluated by means of the estimated values of intracellular substrate and biomass through the material balance of the reactor. Simulation and experimental tests showed good performance of the developed estimator, which appears suitable to be used for process control and monitoring. (c) 1995 John Wiley & Sons, Inc.     

Model identification and physiological control of xylitol production using Debaryomyces hansenii

The control of xylitol production from xylose-grown Debaryomyces hansenii yeast, which is a very complex biological process, usually requires accurate and demanding analytical HPLC measurements. For this reason, estimating relationships of the main variables of the fermentation process-concentration of xylitol, biomass and xylose-are suggested and studied in this article. The volume of added base for maintaining pH at the prescribed level has been shown to be important for approximate assessment of the biomass concentration and, therefore, for all estimation relationships. Furthermore, replacement of expensive off-line HPLC analyses of xylose by an on-line determined respiratory quotient RQ for control purposes is discussed. On the basis of this, physiological control of xylitol production which takes advantage of on-line classification of the different metabolic states of the culture from easily and cheaply measured variables, has been developed. Data and knowledge obtained from several experiments were evaluated for this reason and results are presented.     

The tuning of a model-based estimator for the specific growth rate of Candida utilis

Control of microbial conversion processes is frequently inhibited by the infeasibility of measuring important process variables. In order to circumvent this lack of measurements, an accurate or valuable and conveniently measurable on-line hardware measurement can be combined with the balance equations describing the process to obtain estimates of less easily measurable variables. In this article the on-line estimation of the specific growth rate of Candida utilis is evaluated. The observer-based estimator requires a hardware measurement of the biomass during fermentations in conjuction with a model of the process; therefore the Biomass Monitor, giving an on-line measurement of viable biomass, is used in the bioreactor experiments described. The optimal tuning of the estimation for the experimental conditions is described and several alternative adaptations of the design of the estimator are presented. The influence of implemented time intervals for discretization of the estimator on the reliability of the estimated growth rate values is discussed. Additionally, the necessary choice of an initial value of the estimated specific growth rate has proven to be of great importance in practice.     

An adaptive control algorithm for fed-batch culture

The implementation of adaptive control for a fed-batch culture in order to maximize the output of product based on a self-adjusting model is discussed in the present work. Optimization methods were applied to the generalized mathematical model of a fed-batch fermentation process to determine control algorithms that could be used for on-line process control. The efficiency of the proposed adaptive algorithms was investigated by simulating a model system. The model of amylotytic enzyme fermentation that was proposed by the authors was taken from a real process. Dynamic modelling has shown that the main problem of realization is connected with the on-line identification of the adaptive model's parameters. To avoid this problem, we have introduced special limitations on the parameters' time variations that increased the convergence of the identification algorithm. The results of the investigation have shown the efficiency of the proposed adaptive algorithms, and the results of this work should be investigated for real process control.     

生物量浓度实时在线检测方法的研究

微生物的存在会改变发酵液的电特性,发酵液在无线电频率范围内的电容率增量是测量频率和生物量浓度的函数.基于对发酵液电容率分布的研究,提出了测量生物量浓度的新方法.用此方法不用取样就能对发酵液中的生物量进行实时在线测量,而且测得的是活的生物量浓度.制作的电极直接插入发酵器中并满足高温蒸气灭菌条件.此方法在生化制药、食品发酵、啤酒酿造、污水检测等工业领域里有很好的推广应用前景.     

Modeling and parameters identification of 2-keto-<Emphasis Type="SmallCaps">l</Emphasis>-gulonic acid fed-batch fermentation

This article presents a modeling approach for industrial 2-keto-l-gulonic acid (2-KGA) fed-batch fermentation by the mixed culture of Ketogulonicigenium vulgare (K. vulgare) and Bacillus megaterium (B. megaterium). A macrokinetic model of K. vulgare is constructed based on the simplified metabolic pathways. The reaction rates obtained from the macrokinetic model are then coupled into a bioreactor model such that the relationship between substrate feeding rates and the main state variables, e.g., the concentrations of the biomass, substrate and product, is constructed. A differential evolution algorithm using the Lozi map as the random number generator is utilized to perform the model parameters identification, with the industrial data of 2-KGA fed-batch fermentation. Validation results demonstrate that the model simulations of substrate and product concentrations are well in coincidence with the measurements. Furthermore, the model simulations of biomass concentrations reflect principally the growth kinetics of the two microbes in the mixed culture.