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.     

Enzyme sensor-FIA-system for on-line monitoring of glucose, lactate and glutamine in animal cell cultures.

Enzyme sensors for glucose, lactate and glutamine were connected via flow-injection analysis (FIA) devices to two different bioprocesses. They were used for on-line process control of perfused bioreactor systems containing mammalian cell lines producing a monoclonal antibody and recombinant interleukin-2. The biosensor system gives direct access to important process data which can be used as control parameters for long term cell cultivation systems.     

On-line soft sensing in upstream bioprocessing

This review provides an overview and a critical discussion of novel possibilities of applying soft sensors for on-line monitoring and control of industrial bioprocesses. Focus is on bio-product formation in the upstream process but also the integration with other parts of the process is addressed. The term soft sensor is used for the combination of analytical hardware data (from sensors, analytical devices, instruments and actuators) with mathematical models that create new real-time information about the process. In particular, the review assesses these possibilities from an industrial perspective, including sensor performance, information value and production economy. The capabilities of existing analytical on-line techniques are scrutinized in view of their usefulness in soft sensor setups and in relation to typical needs in bioprocessing in general. The review concludes with specific recommendations for further development of soft sensors for the monitoring and control of upstream bioprocessing.     

Enhanced production of L-(+)-lactic acid in chemostat by Lactobacillus casei DSM 20011 using ion-exchange resins and cross-flow filtration in a fully automated pilot plant controlled via NIR

Due to the lack of suitable in-process sensors, on-line monitoring of fermentation processes is restricted almost exclusively to the measurement of physical parameters only indirectly related to key process variables, i.e., substrate, product, and biomass concentration. This obstacle can be overcome by near infrared (NIR) spectroscopy, which allows not only real-time process monitoring, but also automated process control, provided that NIR-generated information is fed to a suitable computerized bioreactor control system. Once the relevant calibrations have been obtained, substrate, biomass and product concentration can be evaluated on-line and used by the bioreactor control system to manage the fermentation. In this work, an NIR-based control system allowed the full automation of a small-scale pilot plant for lactic acid production and provided an excellent tool for process optimization. The growth-inhibiting effect of lactic acid present in the culture broth is enhanced when the growth-limiting substrate, glucose, is also present at relatively high concentrations. Both combined factors can result in a severe reduction of the performance of the lactate production process. A dedicated software enabling on-line NIR data acquisition and reduction, and automated process management through feed addition, culture removal and/or product recovery by microfiltration was developed in order to allow the implementation of continuous fermentation processes with recycling of culture medium and cell recycling. Both operation modes were tested at different dilution rates and the respective cultivation parameters observed were compared with those obtained in a conventional continuous fermentation. Steady states were obtained in both modes with high performance on lactate production. The highest lactate volumetric productivity, 138 g L(-1) h(-1), was obtained in continuous fermentation with cell recycling.     

An on-line approach to monitor ethanol fermentation using FTIR spectroscopy

Fermentation process control is currently limited by its inability to measure parameters such as substrate, product, and biomass concentrations rapidly for consistent on-line feedback. Physical and chemical parameters, such as temperature and pH, currently can be obtained on-line using appropriate sensors. However, to obtain information on the concentration of the substrate, product, and biomass, samples must be taken off-line for measurement. With the use of spectroscopic techniques, real-time monitoring of process constituents such as product and substrate is possible. Spectroscopic techniques are rapid and nondestructive, require minimal or no sample preparation, and can be used to simultaneously assess several constituents in complex matrices. The production of ethanol is the largest fermentation process in terms of production volume and economic value as a result of its prominence in the food, agricultural, and fuel industries. This study attempts to develop an on-line ethanol fermentation monitoring technique using Fourier transform infrared (FTIR) spectroscopy with a flow-through ATR capability. Models developed using multivariate statistics, employed to obtain on-line FTIR measurements, were successfully validated by off-line HPLC analysis and spectrophotometry data. Standard errors of prediction (SEP) values of 0.985 g/L (R2 = 0.996), 1.386 g/L (R2 = 0.998), and 0.546 (R2 = 0.972) were obtained for ethanol, glucose, and OD, respectively. This work demonstrates that FTIR spectroscopy could be used for rapid on-line monitoring of fermentation.     

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.     

On-line measurement of gas production rates

Gas evolution rates represent an important variable to track in biological and certain electrochemical processes. Accurate gas flow rate sensors exist for gas streams possessing a pressure head, such as when pressurized air or oxygen is delivered to a fermentation process. However, these devices impose pressure heads that can inhibit gas production and, therefore, yield false measurements. Examples of effected processes would include electrochemical production of a gas at the electrode (e.g., electrolysis) or anaerobic fermentation (e.g., anaerobic production of methane). In this work, we present an on-line gas measurement technique that measures on-line gas production from an anaerobic microbial process that is continuously fed simulated food waste over a 6-month period. Commentary is given on the sensor's accuracy and ease of use within the context of long-term operation, ability to measure both low and high gas production rates, as well as its potential for process control and system-health monitoring.     

Biosensoren für die Lebensmittelindustrie

Biosensors are obtaining an increasing significance for analytical purposes as well as for on-line monitoring and control in biotechnological processes [1]. In the special field of food industry enzyme and microbial biosensors are suited. With enzyme sensors one can analyse in mostly high specific reactions many substances – microbial sensors normally are unspecific. But they have the advantage of high stability and there is no necessity of enzyme isolation and purification. On the basis of the manifold of microbial metabolic pathways one can expect a wider range of application for microbial sensors, much more as existent for enzyme sensors. This paper is dealing with a microbial aspartam sensor.     

Software sensors for biomass concentration in a SSC process using artificial neural networks and support vector machine

The lack of sensors for some relevant state variables in fermentation processes can be coped by developing appropriate software sensors. In this work, NARX-ANN, NARMAX-ANN, NARX-SVM and NARMAX-SVM models are compared when acting as software sensors of biomass concentration for a solid substrate cultivation (SSC) process. Results show that NARMAX-SVM outperforms the other models with an SMAPE index under 9 for a 20 % amplitude noise. In addition, NARMAX models perform better than NARX models under the same noise conditions because of their better predictive capabilities as they include prediction errors as inputs. In the case of perturbation of initial conditions of the autoregressive variable, NARX models exhibited better convergence capabilities. This work also confirms that a difficult to measure variable, like biomass concentration, can be estimated on-line from easy to measure variables like CO₂ and O₂ using an adequate software sensor based on computational intelligence techniques.     

Studies on on-line bioreactor identification. I. Theory

An integrated approach is presented for the on-line estimation of the state of a biochemical reactor from presently attainable real-time measurements. Elemental and macroscopic balances are used for the determination of the total rate of growth and state-of-the-art estimation techniques are subsequently employed for the elimination of process and measurement noises and the estimation of state variables and unknown culture parameters. The proposed approach is very flexible in that as new sensors become available they can be easily incorporated within the present framework to estimate new variables or improve the accuray of the old ones. The method does not require any model for the growth kinetics and is very successful in accurately estimating the above variables in the presence of intense noise and under both steady-state and transient conditions. State estimates obtained by the presented method can be used for the development of adaptive optimal control schemes as well as for basic studies of the characteristic properties of microbial cultures.     

Sensor systems,electronic tongues and electronic noses,for the monitoring of biotechnological processes

Production of biofuel is based on the conversion by microorganisms of complex organic substrates into the methane or ethanol, which are consequently used as energy sources. Real time monitoring of the fermented media composition is of paramount for the effectiveness of the whole process. However, despite the fact that products worth billions of dollars are produced through fermentation processes annually, analytical instruments used for these processes’ monitoring remain relatively primitive. Established laboratory techniques produce exhaustive information about media composition but analysis is often quite time-consuming, expensive, requires skilled personnel and hardly can be automated. Lack of on-line sensors for the fermentation monitoring is commonly stressed in the literature. One of the techniques particularly suitable for this purpose is chemical sensors. Such features as low prices, relatively simple instrumentation, minimal sample preparation and easy automation of measurements make chemical sensors an attractive tool for industrial process control. However, practical use of chemical sensors in complex media is often hindered by their insufficient selectivity. For example, only pH and oxygen probes are routinely used in bio-reactors. One of the emerging approaches permitting to overcome the selectivity problems is the use of systems instead of discrete sensors. Such systems for liquid and gas analysis were named electronic tongues and electronic noses correspondingly. They are capable to perform both quantitative analysis (components’ concentrations) and classification or recognition of multicomponent media. This review presents recent achievements in the R&D and applications of electronic tongues and noses to the monitoring of biotechnological processes. JIMB-2008: BioEnergy—Special issue.     

Fermentation fault management and on-line state estimation using methods of cluster analysis

The results of the cluster analysis of fermentation data are used for the supervision and on-line state estimation. The results of the classification are presented as the average over all fermentation runs belonging to the class as well as the standard deviation. With the help of the class information the on-line fermentation is associated with the best suiting class. Faults in the data such as spikes or total failure of the sensors are detected as the class information automatically supplies tolerance regions for the measurements. In case of a fault a reliable extrapolation for the time of the fault can be calculated. The approach is implemented in the real-time expert system tool G2 and is applied to data of the carbon dioxide evolution rate (CER) of an industrial antibiotic fermentation process.     

Monitoring pH and dissolved oxygen in mammalian cell culture using optical sensors

Here, we have studied two parameters critical to process control in mammalian cell culture; dissolved oxygen (dO₂) and pH, measured with fluorescent sensors thus allowing the study of the metabolic state of cells in culture without removing or damaging cells during cultivation. Two cell lines, namely, NS0 and CHO were batch-grown in 24-well plates at different serum concentrations with the sensors implemented in the bottom of each well. The data showed a good relationship between the dO₂ and pH data obtained from fluorescent probes and the growth and death characteristics of cells. The method has provided a high throughput on-line multi-parametric analysis of mammalian cell cellular activity.     

Evaluation of the GFP signal and its aptitude for novel on-line monitoring strategies of recombinant fermentation processes

A high number of economically important recombinant proteins are produced in Escherichia coli based host/vector systems. The major obstacle for improving current processes is a lack of appropriate on-line in situ methods for the monitoring of metabolic burden and critical state variables. Here, a pre-evaluation of the reporter green fluorescent protein (GFP) was undertaken to assess its use as a reporter of stress associated promoter regulation. The investigation of GFP and its blue fluorescent variant BFP was done in model fermentations using E. coli HMS 174(DE3)/pET11 aGFPmut3.1 and E. coli HMS174(DE3)/pET1aBFP host/vector systems cultured in fed-batch and chemostat regime. Our results prove the suitability of the fluorescent reporter proteins for the design of new strategies of on-line bioprocess monitoring. GFPmut3.1 variant can be detected after a short lag-phase of only 10 min, it shows a high fluorescence yield in relation to the amount of reporter protein, a good signal to noise ratio and a low detection limit. The fluorescence-signal and the amount of fluorescent protein, determined by ELISA, showed a close correlation in all fermentations performed. A combination of reporter technology with state of the art sensors helps to develop new strategies for efficient on-line monitoring needed for industrial process optimisation. The development of efficient monitoring will contribute to advanced control of recombinant protein production and accelerate the development of optimised production processes.     

Simplified off-gas analyses in animal cell cultures for process monitoring and control purposes

Batch-to-batch reproducibility of animal cell cultures can significantly be enhanced using process control procedures. Most informative signals for advanced process control can be derived from the volume fractions of oxygen and carbon dioxide in the vent line of the reactors. Here we employed simple low-cost sensors, previously not considered for off-gas analysis at a laboratory-scale cell cultures, and compared them with a simultaneously used quadrupole mass spectrometer, i.e., the standard equipment. A decisive advantage is that the sensors did not need any calibration and are easy to use. We show that monitoring and advanced control of cell cultures can significantly be simplified using the devices tested here and that the same batch-to-batch reproducibility can be obtained with much less effort than before.     

Adaptive on-line simulation of bioreactors: Fermentation monitoring and modeling system

Summary In order to study and control fermentation processes, indirect on-line measurements and mathematical models can be used. Here an on-line model for fermentation processes is presented. The model is based on atom and partial mass balances as well as on stability equations for the protolytes. The model is given an adaptive form by including transport equations for mass transfer and expressions for the fermentation kinetics. The state of the process can be estimated on-line using the balance component of the model completed with measurement equations for the input and the output flows of the process. Adaptivity is realized by means of on-line estimation of the parameters in the transport and kinetic expressions using recursive regression analysis. On-line estimation of the kinetic and mass transfer parameters makes model-based predictions possible and enables intelligent process control while facilitating testing of the validity of the measurement variables. A practical MS-Windows 3.1 model implementation called FMMS—Fermentation Monitoring and Modeling System is shown. The system makes it easy to configure the operating conditions for a run. It uses Windows dialogs for all set-ups, model configuration parameters, elemental compositions, on-line measurement devices and signal conditioning. Advanced on-line data analysis makes it possible to plot variables against each other for easy comparison. FMMS keeps track of over 100 variables per run. These variables are either measured or estimated by the model. Assay results can also be entered and plotted during fermentation. Thus the model can be verified almost instantly. Historical fermentation runs can be re-analyzed in simulation mode. This makes it possible to examine different signal conditining filters as well as the sensitivity of the model. Combined, the data analysis and the simulation mode make it easy to test and develop model theories and new ideas.     

On-line monitoring of hybridoma cell growth using a laser turbidity sensor

A high-sensitivity turbidity probe was used for on-line monitoring of the cell concentration in batch hybridoma cultivation. Good correlation between off-line cell counts and the linearized sensor signal was found. The quality of the signal was sufficiently high to provide for on-line estimation of the specific growth rate using an efficient filtering procedure. These positive results suggest that such laser turbidity sensors will facilitate development of systems for on-line monitoring and control of animal cell cultivations. (c) 1992 John Wiley & Sons, Inc.     

Algae and rotifer turbidostats: studies on stability of live feed cultures

A two stage turbidostat was developed according toBoraas & Bennet (1988), but with highly improvedturbidity sensors. The first stage was an algalturbidostat where algal density was regulated byturbidity measurements. Algal density was also heldconstant in the second stage (rotifer production)according to turbidity measurements. Additionally,the growth rates were monitored. The regulation system allowed an effective on-line process control.Initially, the production of rotifers in long-timestudies was variable. However, after furtherimprovements of the turbidity measurement, fluctuations in the rotifer turbidostat decreasedsignificantly.     

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.