Bioprocess control from a multivariate process trajectory

A multivariate bioprocess control approach, capable of tracking a pre-set process trajectory correlated to the biomass or product concentration in the bioprocess is described. The trajectory was either a latent variable derived from multivariate statistical process monitoring (MSPC) based on partial least squares (PLS) modeling, or the absolute value of the process variable. In the control algorithm the substrate feed pump rate was calculated from on-line analyzer data. The only parameters needed were the substrate feed concentration and the substrate yield of the growth-limiting substrate. On-line near-infrared spectroscopy data were used to demonstrate the performance of the control algorithm on an Escherichia coli fed-batch cultivation for tryptophan production. The controller showed good ability to track a defined biomass trajectory during varying process dynamics. The robustness of the control was high, despite significant external disturbances on the cultivation and control parameters.     

On-line monitoring and controlling system for fermentation processes

A personal computer-based on-line monitoring and controlling system was developed for the fermentation of microorganism. The on-line HPLC system for the analysis of glucose and ethanol in the fermentation broth was connected to the fermenter via an auto-sampling equipment, which could perform the pipetting, filtration and dilution of the sample and final injection onto the HPLC through automation based on a programmed procedure. The A/D and D/A interfaces were equipped in order to process the signals from electrodes and from the detector of HPLC, and to direct the feed pumps, the motor of stirrer and gas flow-rate controller. The software that supervised the control of the stirring speed, gas flow-rate, pH value, feed flow-rate of medium, and the on-line measurement of glucose and ethanol concentration was programmed by using Microsoft Visual Basic under Microsoft Windows. The signal for chromatographic peaks from on-line HPLC was well captured and processed using an RC filter and a smoothing algorithm. This monitoring and control system was demonstrated to be effective in the ethanol fermentation of Zymomonas mobilis operated in both batch and fed-batch modes. In addition to substrate and product concentrations determined by on-line HPLC, the biomass concentration in Z. mobilis fermentation could also be on-line estimated by using the pH control and an implemented software sensor. The substrate concentration profile in the fed-back fermentation followed well the set point profile due to the fed-back action of feed flow-rate control.     

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.     

Model-based optimization of viral capsid protein production in fed-batch culture of recombinant <Emphasis Type="Italic">Escherichia coli</Emphasis>

An optimized fed-batch cultivation process for the production of the polyoma virus capsid protein VP1 in recombinant Escherichia coli BL21 bacteria is presented. The optimization procedure maximizing the amount of desired protein is based on a mathematical model. The model distinguishes an initial cell growth phase from a protein production phase initiated by inducer injection. A new approach to model the target protein formation rate was elaborated, where product formation is primarily dependent on the specific biomass growth rate. Lower growth rates led to higher specific protein concentrations. The model was identified from a series of fed-batch experiments designed for parameter identification purposes and possesses good prediction quality. Then the model was used to determine optimal open-loop control profiles by manipulating the substrate feed rates in both phases as well as the induction time. Feed-rate optimization has been solved using Pontryagin's maximum principle. The solution was validated experimentally. A significant improvement of the process performance index was achieved.     

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.     

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.     

Model-based monitoring of industrial reversed phase chromatography to predict insulin variants

Downstream processing in the manufacturing biopharmaceutical industry is a multistep process separating the desired product from process- and product-related impurities. However, removing product-related impurities, such as product variants, without compromising the product yield or prolonging the process time due to extensive quality control analytics, remains a major challenge. Here, we show how mechanistic model-based monitoring, based on analytical quality control data, can predict product variants by modeling their chromatographic separation during product polishing with reversed phase chromatography. The system was described by a kinetic dispersive model with a modified Langmuir isotherm. Solely quality control analytical data on product and product variant concentrations were used to calibrate the model. This model-based monitoring approach was developed for an insulin purification process. Industrial materials were used in the separation of insulin and two insulin variants, one eluting at the product peak front and one eluting at the product peak tail. The model, fitted to analytical data, used one component to simulate each protein, or two components when a peak displayed a shoulder. This monitoring approach allowed the prediction of the elution patterns of insulin and both insulin variants. The results indicate the potential of using model-based monitoring in downstream polishing at industrial scale to take pooling decisions.     

Influence of type and concentration of flavinogenic factors on production of riboflavin by Eremothecium ashbyii NRRL 1363

A study was conducted to determine the effect of various low cost organic wastes as flavinogenic factors and the various concentrations at which they induced flavinogenecity resulting in higher yields of riboflavin. A high-yielding riboflavin strain; Eremothecium ashbyii NRRL 1363 was chosen to determine the flavinogenicity. Carbon source at 50 g l(-1) (dextrose equivalents) of molasses and nitrogen source at 50 g l(-1) (weight/volume) of peanut seed cake were found to be optimal levels to yield higher riboflavin. Among the organic wastes, (beef extract, hog casings, blood meal, fish meal) hog casings in association with fish meal supported the highest yield of riboflavin. Among the different recovery processes studied, a vacuum drying process was the most efficient allowing maximum yield, followed by drying at 90 degrees C and freeze-drying. It is apparent from this study that inexpensive or waste organic materials could induce E. ashbyii to synthesize and secrete riboflavin at higher levels in the medium and this could be purified using a vacuum drying process. This bioconversion process allows us to recycle the biomaterials and produce a value-added product of economic importance.     

The kinetics of riboflavin secretion by Eremothecium ashbyii nrrl 1363

For riboflavin production a highly flavinogenic strain Eremothecium ashbyii is used. Vitamins are classified as chemical substances that control and effect the physiological processes; Riboflavin is one among them. The deficiency of riboflavin in human beings results in the cracking of lips and corners of mouth (cheilosis); nerve tissues are affected. For riboflavin production a highly flavinogenic strain Eremothecium ashbyii NRRL 1363 was used. Investigations were conducted in shake flask using inexpensive and abundantly available raw materials. Among the stimulants, a combination of hog casings and beef extract stimulated the highest and promoted the maximum riboflavin yield followed by the combination of fish meal and beef extract. The fermented broth (an enriched, riboflavin concentrate) can be directly used as a feed grade riboflavin. To upgrade it to pharmaceutical grade further investigations are required.     

Dynamic optimization of hybridoma growth in a fed-batch bioreactor

This study addressed the problem of maximizing cell mass and monoclonal antibody production from a fed-batch hybridoma cell culture. We hypothesized that inaccuracies in the process model limited the mathematical optimization. On the basis of shaker flask data, we established a simple phenomenological model with cell mass and lactate production as the controlled variables. We then formulated an optimal control algorithm, which calculated the process-model mismatch at each sampling time, updated the model parameters, and re-optimized the substrate concentrations dynamically throughout the time course of the batch. Manipulated variables were feed rates of glucose and glutamine. Dynamic parameter adjustment was done using a fuzzy logic technique, while a heuristic random optimizer (HRO) optimized the feed rates. The parameters selected for updating were specific growth rate and the yield coefficient of lactate from glucose. These were chosen by a sensitivity analysis. The cell mass produced using dynamic optimization was compared to the cell mass produced for an unoptimized case, and for a one-time optimization at the beginning of the batch. Substantial improvements in reactor productivity resulted from dynamic re-optimization and parameter adjustment. We demonstrated first that a single offline optimization of substrate concentration at the start of the batch significantly increased the yield of cell mass by 27% over an unoptimized fermentation. Periodic optimization online increased yield of cell mass per batch by 44% over the single offline optimization. Concomitantly, the yield of monoclonal antibody increased by 31% over the off-line optimization case. For batch and fed-batch processes, this appears to be a suitable arrangement to account for inaccuracies in process models. This suggests that implementation of advanced yet inexpensive techniques can improve performance of fed-batch reactors employed in hybridoma cell culture.     

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.     

An experimental study of acid production rate controlled operations of a continuous fermentor

The efficacy of acid production rate (APR) controlled operations of a continuous fermentor supporting the growth of a methylotroph, L3, was experimentally examined. Direct digital control of pH at a constant value allowed for on-line estimation of APR during the fermentation. Two types of APR controlled operations were studied. In the first type of operation, the APR was controlled at a constant value according to a predetermined program by manipulating the feed flow rate to the fermentor. Such an operation effectively stabilized the cell mass productivity of a continuous fermentor subjected to disturbances in the feed nutrient concentration. It resulted in a near complete conversion of methanol to yield a cell mass product with very low amounts of unutilized methanol at both steady state and transient fermentation situations. In the second type of operation, the feed flow rate was manipulated to optimize the steady state value of APR during the fermentation. This method shows promise for on-line steady state optimization of cell mass productivity in a continuous fermentor.     

Effect of High Product Concentration in a Dual Fed-batch Asymmetric 3-oxo Ester Reduction by Baker's Yeast

Baker's-yeast-mediated asymmetric ethyl 3-oxobutanoate reduction using a fed-batch feeding strategy for both the 3-oxo ester and the electron donor, was explored as potential production system for enantiopure ethyl ( S )-3-hydroxybutanoate. The dual feed strategy was based on kinetic and stoichiometric data. One major aspect is the effect of high product concentrations on the progress of the reduction. According to initial rate experiments, product inhibition occurs at concentrations above 600 mM product causing a 10-fold decrease of the initial biomass-specific reduction rate. By using optimized feed rates and a biomass concentration of 43 g dw l -1 , a product concentration of 350 mM was reached within 80 h with a degree of conversion of 95%. The volumetric productivity was 0.58 g l -1 h -1 , using 2.1 kg pressed yeast kg product -1 and 0.52 kg glucose kg product -1 . During the fed-batch biotransformation the reduction rate continuously decreased and reduction ceased after 80 h, due to biocatalyst inactivation after prolonged use at increasing high product concentrations. The continuous decrease in reducing activity led to very high ethyl 3-oxobutanoate levels in the reactor resulting in an increase of the undesired specific ethyl ( R )-3-hydroxybutanoate production rate. Therefore, the enantiomeric excess of the product decreased, from initially 100 to ~75% at 80 h. It is concluded that the design of processes for efficient asymmetric bioreduction cannot solely be based on initial rate kinetics, but require detailed knowledge of the effects on activity and enantioselectivity upon long-term exposure to process conditions.     

Knowledge based control applied to fixed bed pulsed bioreactors

In this work, an expert system was developed and applied for on-line control and supervision of ethanolic fermentation by immobilized Saccharomyces cerevisiae in a fixed-bed pulsed bioreactor of 1.2 l of working volume. A number of experiments with different substrate concentrations (75, 100, 150 and 200 g/l) and hydraulic residence times (2.4, 1.2 and 0.8 h) were carried out. Knowledge-based computer-aided supervision of this process involves accurate on-line measurement of the relevant process variables (temperature, pH, flow rate, carbon dioxide production, etc.). Carbon dioxide production was used for the estimation of the ethanol productivity. The analysis of the measured data allowed to detect states or trends that may be indicative of process or system failures, providing advices and/or alarms. The results showed the reliability of the control system. In previous works, it was proven that pulsing the feed stream highly improves the productivity of fermentation processes carried out in fixed-bed bioreactors [14, 15, 16]. The amplitude and frequency of the pulsation, which is a key factor in the performance of a pulsed feed bioreactor [13], was selected by the control system by using an algorithm allowing the ethanol productivity to be optimized. The pulsation frequency which maximizes the ethanol productivity, presents a high dependency on the hydraulic residence time and the feeding substrate concentration. When increasing the substrate concentration the optimum pulsation frequency also increases; when increasing the hydraulic residence time the optimum pulsation frequency decreases.     

Stoichiometric growth model for riboflavin-producing Bacillus subtilis

Rate equations for measured extracellular rates and macromolecular composition data were combined with a stoichiometric model to describe riboflavin production with an industrial Bacillus subtilis strain using errors in variables regression analysis. On the basis of this combined stoichiometric growth model, we explored the topological features of the B. subtilis metabolic reaction network that was assembled from a large amount of literature. More specifically, we simulated maximum theoretical yields of biomass and riboflavin, including the associated flux regimes. Based on the developed model, the importance of experimental data on building block requirements for maximum yield and flux calculations were investigated. These analyses clearly show that verification of macromolecular composition data is important for optimum flux calculations.     

On-line optimization of recombinant product in a fed-batch bioreactor

In this paper, an efficient scheme for on-line optimization of a recombinant product in a fed-batch bioreactor is presented. This scheme is based on the parametrization of the system states and the elimination of a subset of the dynamic equations in the mathematical model of the fed-batch bioreactor. The fed-batch bioreactor considered here involves the production of chloramphenicol acetyltransferase (CAT) in a genetically modified E. coli. The optimal inducer and the glucose feed rates are obtained using the proposed optimization approach. This approach is compared with the traditional optimization approach, where all the states and the manipulated variables are parametrized. The approach presented in this paper results in a 5-fold improvement in the computational time for the recombinant product optimization. The optimization technique is employed in an on-line optimization scheme, when parametric drift and a disturbance in the manipulated variable is present. Feedback from the process is introduced through resetting the initial conditions of the model and through an observer for estimating the time varying parameter. The simulation results indicated improvement in the amount of product formed, when the optimal profile is regenerated during the course of the batch.     

Process and economic analysis of pretreatment technologies

Five pretreatment processes (dilute acid, hot water, ammonia fiber explosion (AFEX), ammonia recycle percolation (ARP), and lime) for the liberation of sugars from corn stover are compared on a consistent basis. Each pretreatment process model was embedded in a full bioethanol facility model so that systematic effects for variations in pretreatment were accounted in the overall process. Economic drivers influenced by pretreatment are yield of both five and six carbon sugars, solids concentration, enzyme loading and hemicellulase activity. All of the designs considered were projected to be capital intensive. Low cost pretreatment reactors in some pretreatment processes are often counterbalanced by higher costs associated with pretreatment catalyst recovery or higher costs for ethanol product recovery. The result is little differentiation between the projected economic performances of the pretreatment options. Additional process performance data, especially involving the identification of optimal enzyme blends for each pretreatment approach and conditioning requirements of hydrolyzates at process-relevant sugar concentrations resulting from each pretreatment may lead to greater differentiation in projected process economics.     

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.     

Defining process design space for a hydrophobic interaction chromatography (HIC) purification step: Application of quality by design (QbD) principles

The concept of design space has been taking root under the quality by design paradigm as a foundation of in‐process control strategies for biopharmaceutical manufacturing processes. This paper outlines the development of a design space for a hydrophobic interaction chromatography (HIC) process step. The design space included the impact of raw material lot‐to‐lot variability and variations in the feed stream from cell culture. A failure modes and effects analysis was employed as the basis for the process characterization exercise. During mapping of the process design space, the multi‐dimensional combination of operational variables were studied to quantify the impact on process performance in terms of yield and product quality. Variability in resin hydrophobicity was found to have a significant influence on step yield and high‐molecular weight aggregate clearance through the HIC step. A robust operating window was identified for this process step that enabled a higher step yield while ensuring acceptable product quality. Biotechnol. Bioeng. 2010;107: 985–997. © 2010 Wiley Periodicals, Inc.