A contribution to optimal control of fed-batch biochemical processes

The problem of looking for high efficient modern control strategies in fermentation technology is very urgent, nowdays. Particular attention should be paid to the processes in fed-batch mode. Both, optimal feedforward and feedback control approaches are suggested. A contribution is considered to have been made in the feedback control where continuous and discrete versions are treated as well. The control laws are carried out by a variation calculus problem and a polynomial pole placement synthesis solution, respectively. All the algorithms result in an optimal substrate feed rate profile. On the basis of recursive least squares identification of the model coefficients an adaptive discrete-time control strategy is proposed. Some satisfying simulation results are dealt with.     

On-line adaptive control of a fed-batch fermentation of Saccharomyces cerevisiae

The feasibility of applying an adaptive control technique to a fermentation process is investigated. The nonlinear, time-variant parameters of a fermentation process were estimated on-line as a series of linearized describing matrices. The matrices were used to update a suboptimal feedback law which controlled the process in real time over the linear region. Experiments were performed on a small-scale fully instrumented fermenter with the online, real-time adaptive control package. Results are presented for both single- and multivariable control, and indicate successful control of yeast cell growth.     

An algorithm for operating a fed-batch fermentor at optimum specific-growth rate

An algorithm for operating a fed-batch fermentor at an optimum specific fermentation rate is proposed. It does not require on-line measurement of nutrient concentration in the culture medium. An on-line estimate of the specific fermentation rate is sufficient for implementation of this scheme. The algorithm is model independent and works well even with poor estimates of the product yields and the specific fermentation rate. Results of a detailed simulation study are presented for a simple case of optimization of cell-mass production in a fed-batch fermentor. The results clearly demonstrate the efficacy of this algorithm under a wide range of fermentation situations.     

Closed-loop control of fed-batch cultures of recombinant Escherichia coli using on-line HPLC

This article describes a fully automated system for the on-line monitoring and closed-loop control of a fed-batch fermentation of recombinant Escherichia coli, and presents two case studies of its used in limiting production of unwanted byproducts such as acetic in fed-batch fermentations. The system had two components. The first components, on-line monitoring, comprised an aseptic sampling device, a microcentrifuge, and HPLC System. These instruments removed a Sample from a fermentor, spun it at high speed to separate solid and liquid components, and then automatically injected the supernatant onto an HPLC column for analysis. The second component consisted of control algorithms programmed using the LabView visual programming environment in a control computer that was linked via a remote components were linked so that results from the on-line HPLC were captured and used by the control algorithm was designed to demonstrate coarse feedback control to confirm the operability of the controller. The second case study showed how the system could be used in a more sophisticated feedings strategy providing fine control and limiting acetate concentration to a low level throughout the fermentation. (c) 1994 John Wiley & Sons, Inc.     

Dynamic reoptimization of a fed-batch fermentor

Traditionally, fed-batch biochemical process optimization and control use complicated models and off-line optimizers with no on-line model adaptation and re-optimization. This work demonstrates the applicability, effectiveness, and economic potential, of a simple phenomenological model for modeling, and a novel optimizer for on-line re-optimization and control of an aerobic fed-batch fermentor.     

An Algorithm for operating a fed-batch fermentation using quasi-steady state

The determination of an optimum feeding profile of a fed-batch fermentation requires the solution of a singular optimum control problem, which is often complicated by changes in the process kinetics during the fermentation. The procedure of optimization may be sufficiently simple, if the feeding part of fermentation is carried out in the quasi-steady state. In this work an algorithm for operating a fed-batch fermentation using mentioned regime is offered. The algorithm supposes a periodical correction of the feeding strategy. Applying to fed-batch lysine fermentation demonstrate efficacy of this algorithm over frequently used strategies.     

Continuous Cellulosic Bioethanol Fermentation by Cyclic Fed-Batch Cocultivation

Cocultivation of cellulolytic and saccharolytic microbial populations is a promising strategy to improve bioethanol production from the fermentation of recalcitrant cellulosic materials. Earlier studies have demonstrated the effectiveness of cocultivation in enhancing ethanolic fermentation of cellulose in batch fermentation. To further enhance process efficiency, a semicontinuous cyclic fed-batch fermentor configuration was evaluated for its potential in enhancing the efficiency of cellulose fermentation using cocultivation. Cocultures of cellulolytic Clostridium thermocellum LQRI and saccharolytic Thermoanaerobacter pseudethanolicus strain X514 were tested in the semicontinuous fermentor as a model system. Initial cellulose concentration and pH were identified as the key process parameters controlling cellulose fermentation performance in the fixed-volume cyclic fed-batch coculture system. At an initial cellulose concentration of 40 g liter⁻¹, the concentration of ethanol produced with pH control was 4.5-fold higher than that without pH control. It was also found that efficient cellulosic bioethanol production by cocultivation was sustained in the semicontinuous configuration, with bioethanol production reaching 474 mM in 96 h with an initial cellulose concentration of 80 g liter⁻¹ and pH controlled at 6.5 to 6.8. These results suggested the advantages of the cyclic fed-batch process for cellulosic bioethanol fermentation by the cocultures.     

Optimal feeding strategies by adaptive mesh selection for fed-batch bioprocesses

The objective of this contribution is the design of optimal feeding strategies for fed-batch bioprocesses, where complex dynamic models with input and state constraints are present. For the solution of this dynamic optimization problem a transformation to a finite dimensional optimization problem is made using piecewise linear control profiles. The optimization of these profiles is performed by a sequential approach, that includes an ODE solver for the solution of the model ODE's. Further an adaptive mesh selection algorithm was investigated for an appropriate discretization of the control profiles. The implementation of the resulting optimal feeding profiles is shown for a process example, namely the production of nikkomycin by Streptomyces tendae. This implementation uses a hierarchical process control framework, that consists of components for process monitoring, state estimation, and trajectory control.     

模拟青霉素分批补料发酵过程的细胞自动机模型

根据青霉素产生菌的生长机理和青霉素分批补料发酵过程的动力学特性,在Paull等建立的形态学结构动力学模型的基础上,建立了模拟青霉素分批补料发酵过程的细胞自动机模型。模型采用三维细胞自动机作为菌体生长空间,采用Moore型邻域作为细胞邻域,其演化规则根据青霉素分批补料发酵过程中菌体生长机理和简化动力学结构模型设计。模型中的每一个细胞既可代表单个产黄青霉菌体细胞,又可代表特定数量的这种菌体细胞,它具有不同的状态。对模型进行的仿真实验结果表明：模型不但能一致地复现形态学结构动力学模型所描述的青霉素分批补料发酵过程的演化特性,而且较形态学结构动力学模型更加直观地刻画了青霉素分批补料发酵过程的演化行为。最后,对所建模型在实际生产过程中的应用问题进行了分析,指出了需要进一步研究的问题。     

Mixed-feed exponential feeding for fed-batch culture of recombinant methylotrophic yeast

A simple, accurate model capable of predicting cell growth and methanol utilization during the mixed substrate fed-batch fermentation of Mut^S recombinant Pichia pastoris was developed and was used to design an exponential feeding strategy for mixed substrate fed-batch fermentation at a constant specific growth rate. Mixed substrate feeding has been shown to boost productivity in recombinant fed-batch culture of P. pastoris, while fixed growth rate exponential feeding during fed-batch culture is a useful tool in process optimization and control.     

Multi-objective optimization in Aspergillus niger fermentation for selective product enhancement

A multi-objective optimization formulation that reflects the multi-substrate optimization in a multi-product fermentation is proposed in this work. This formulation includes the application of ε-constraint to generate the trade-off solution for the enhancement of one selective product in a multi-product fermentation, with simultaneous minimization of the other product within a threshold limit. The formulation has been applied to the fed-batch fermentation of Aspergillus niger that produces a number of enzymes during the course of fermentation, and of these, catalase and protease enzyme expression have been chosen as the enzymes of interest. Also, this proposed formulation has been applied in the environment of three control variables, i.e. the feed rates of sucrose, nitrogen source and oxygen and a set of trade-off solutions have been generated to develop the pareto-optimal curve. We have developed and experimentally evaluated the optimal control profiles for multiple substrate feed additions in the fed-batch fermentation of A. niger to maximize catalase expression along with protease expression within a threshold limit and vice versa. An increase of about 70% final catalase and 31% final protease compared to conventional fed-batch cultivation were obtained. Novel methods of oxygen supply through liquid-phase H₂O₂ addition have been used with a view to overcome limitations of aeration due to high gas–liquid transport resistance. The multi-objective optimization problem involved linearly appearing control variables and the decision space is constrained by state and end point constraints. The proposed multi-objective optimization is solved by differential evolution algorithm, a relatively superior population-based stochastic optimization strategy.     

Experimental adaptive on-line optimization of cellular productivity of a continuous bakers' yeast culture

An adaptive on-line optimization method that utilizes dynamic model identification has been applied to maximize the cellular productivity of a continuous bakers' yeast culture. Experiments were conducted on a sophisticated computerized fermentation system. Experimental results show that the adaptive on-line optimization method requires very little a priori information, is easy to implement, converges quickly, adapts to changes in the process, and is stable even when operational difficulties are encountered.     

Generally applicable fed-batch culture concept based on the detection of metabolic state by on-line balancing

In many microorganisms, flux limitations in oxidative metabolism lead to the formation of overflow metabolites even under fully aerobic conditions. This can be avoided if the specific growth rate is controlled at a low enough value. This is usually accomplished by controlling the substrate feeding profile in a fed-batch process. The present work proposes a control concept which is based on the on-line detection of metabolic state by on-line calculation of mass and elemental balances. The advantages of this method are: 1) the check of measurement consistency based on all of the available measurements, 2) the minimum requirement of a priori knowledge of metabolism, and 3) the exclusive use of simple and established on-line techniques which do not require direct measurement of the metabolite in question. The control concept has been linked to a simple adaptive controller and applied to fed-batch cultures of S. cerevisiae and E. coli, organisms which express different overflow metabolites, ethanol and acetic acid, respectively. Oxidative and oxidoreductive states of S. cerevisiae and E. coli cultures were detected with high precision. As demonstrated by the formation of acetic acid in E. coli cultures, metabolic states could be correctly distinguished for systems for which traditional methods, such as respiratory quotient (RQ), are insensitive. Hence, it could be shown that the control concept allowed avoidance of overflow metabolite formation and operation at maximum oxidative biomass productivity and oxidative conversion of substrate into biomass. Based on mass and elemental balances, the proposed method additionally provides a richness of additional information, such as yield coefficients and estimation of concentrations and specific conversion rates. These data certainly help the operator to additionally evaluate the state of the process on-line.     

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.     

Operational strategies, monitoring and control of heterologous protein production in the methylotrophic yeast Pichia pastoris under different promoters: A review

The methylotrophic yeast Pichia pastoris has been widely reported as a suitable expression system for heterologous protein production. The use of different phenotypes under PAOX promoter, other alternative promoters, culture medium, and operational strategies with the objective to maximize either yield or productivity of the heterologous protein, but also to obtain a repetitive product batch to batch to get a robust process for the final industrial application have been reported. Medium composition, kinetics growth, fermentation operational strategies from fed-batch to continuous cultures using different phenotypes with the most common PAOX promoter and other novel promoters (GAP, FLD, ICL), the use of mixed substrates, on-line monitoring of the key fermentation parameters (methanol) and control algorithms applied to the bioprocess are reviewed and discussed in detail.     

Neural network predictive control by MIMS monitored 2,3-butanediol fermentation by Klebsiella oxytoca

With the aid of a membrane introduction mass spectrometer (MIMS), the major product 2,3-butanediol (2,3-BDL) as well as the other metabolites from the fermentation carried by Klebsiella oxytoca can be measured on-line simultaneously. A backpropagation neural network (BPN) being recognized with superior mapping ability was applied to this control study. This neural network adaptive control differs from those conventional controls for fermentation systems in which the measurements of cell mass and glucose are not included in the network model. It is only the measured product concentrations from the MIMS that are involved. Oxygen composition was chosen to be the control variable for this fermentation system. Oxygen composition was directly correlated to the measured product concentrations in the controller model. A two-dimensional (number of input nodes by number of data sets) moving window for on-line, dynamic learning of this fermentation system was applied. The input nodes of the network were also properly selected. Number of the training data sets for obtaining better control results was also determined empirically. Two control structures for this 2,3-BDL fermentation are discussed and compared in this work. The effect from adding time delay element to the network controller was also investigated.     

Comparative evaluation of various control schemes for fed-batch fermentation

The crucial problem associated with control of fed-batch fermentation process is its time-varying characteristics. A successful controller should be able to deal with this feature in addition to the inherent nonlinear characteristics of the process. In this work, various schemes for controlling the glucose feed rate of fed-batch baker's yeast fermentation were evaluated. The controllers evaluated are fixed-gain proportional-integral (PI), scheduled-gain PI, adaptive neural network and hybrid neural network PI. The difference between the specific carbon dioxide evolution rate and oxygen uptake rate (Q_c-Q_o) was used as the controller variable. The evaluation was carried out by observing the performance of the controllers in dealing with setpoint tracking and disturbance rejection. The results confirm the unsatisfactory performance of the conventional controller where significant oscillation and offsets exist. Among the controllers considered, the hybrid neural network PI controller shows good performance.     

Advanced control of glutathione fermentation process

A study was performed to understand the fermentation process for production of glutathione fermentation (GSH) with an improved strain of baker's yeast. Simultaneous utilization of sugar and ethanol has been found to be a key factor in the industrial process to produce GSH using Saccharomyces cerevisiae KY6186. Based on this observation, the optimal sugar feed profile for the fed-batch operation has been determined. A feedforward/feedback control system was developed to regulate the sugar feed rate so as to maximize GSH production yields. Using the feedforward/feedback control system and the on-line data of oxygen and ethanol concentration in exhaust gas, the successful scaleup to the production level was accomplished. An average of 40% improvement of glutathione production compared to a conventionally programmed control of exponential fed-batch operation was found in the new process. (c) 1992 John Wiley & Sons, Inc.     

Experimental studies of network parameters and operational variables on recurrent backpropagation neural network adaptive control of penicillin acylase fermentation by Arthrobacter viscosus

This work is to investigate the on-line control of the fermentation by Arthrobacter viscosus. This species of bacteria can secrete penicillin acylase which is a key enzyme in pharmaceutical industry. The growth of more cells during the fermentation will obtain more enzyme. Both the enzyme activity and the cell growth are rather sensitive to the change of pH. Once the pH during a fermentation is not properly controlled, the decay of cells' activity will irreversibly occur. Two peristaltic pumps for supplying acidic and basic solutions, respectively, were connected for the regulation of pH. With superior ability in identification and prediction of dynamic time series, recurrent backpropagation network (RBPN), instead of conventional controllers, was used as the adaptive controller model for the fermentation with dynamic characteristics. Based on a 1-3-1 BPN, a corresponding 4-4-1 RBPN was determined. The deviation of the pH measured at current time from the set point of 7.0, denoted as ( pH(t), was chosen as the input node of the network controller. The output node of this network controller was the predicted flow rate of the peristaltic pump for next control time interval. Such a model was operated by two phases. During the first phase, the network was set as the process model and trained by a fixed set of on-line acquired data. During the second phase, the network was stopped learning and switched to become a predictor, the predicted control action was hence obtained. The optimum sampling time was determined experimentally. To enhance the effective computation of this network, the number of training data was limited. A moving-window type of supplying training data to the network was applied for the on-line learning. The window size was also determined for each learning. With properly chosen network parameters as well as operation conditions, pH of the fermentation was thus well controlled by the RBPN controller.     

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.