Control Of Fed-batch Fermentation Process Research Articles

Model based estimation and optimal control of fed-batch fermentation processes for the production of antibiotics

This contribution addresses the application and comparison of model based estimation, optimization, and control methods for fed-batch bioprocesses. For the application of model based control, appropriate knowledge of the system’s state is required. The estimation quality of two constrained optimization based state estimation algorithms, namely the Bayesian maximum a posteriori based Constrained Extended Kalman-Filter (CEKF) and the Moving-Horizon-State-Estimation (MHE) is compared to the classical unconstrained Extended Kalman-Filter (EKF). The comparison is based on Monte Carlo simulations of a small mechanical and a high order grey-box model of a biological system. Moreover, EKF, CEKF, and MHE usually introduced separately, are described in a coherent setting starting from the same maximum a posteriori estimation problem. Finally the well-known nonlinear model predictive control (NMPC) is compared with a control strategy that uses online optimization of the complete future trajectory. Extensive Monte Carlo simulation studies and a real world application are considered.

An Approach for Optimal Control of Fed-Batch Fermentation Process with Multi Control Variables

ABSTRACTIn this paper a fermentation process of E. Coli production is presented by using real laboratory experimental data. The adequate model of the process is proved. An effective algorithm for process optimization in the presence of three optimal control variables: rotation speed, gas flow rate and substratum floating rate is developed by using a modified approach of method of the dynamic programming. The analysis of results shows that the complex investigation with more control variables vastly raises efficiency of the process.

Integrated condition monitoring and control of fed-batch fermentation processes

This paper investigates the benefits that the partial least squares (PLS) modelling approach offers engineers involved in the operation of fed-batch fermentation processes. It is shown that models developed using PLS can be used to provide accurate inference of quality variables that are difficult to measure on-line, such as biomass concentration. It is further shown that this model can be used to provide fault detection and isolation capabilities and that it can be integrated within a standard model predictive control framework to regulate the growth of biomass within the fermenter. This model predictive controller is shown to provide its own monitoring capabilities that can be used to identify faults within the process and also within the controller itself. Finally it is demonstrated that the performance of the controller can be maintained in the presence of fault conditions within the process.

Phase-based supervisory control for fermentation process development

Abstract Supervisory control of fed-batch fermentation processes is a difficult problem because the process is inherently non-linear, operated at unsteady state, and often poorly modeled. This problem is exacerbated in laboratory and pilot-plant applications due to lack of historical data from similarly operated batches. In this paper, we propose an approach for the supervisory control of fed-batch fermentation during process development. Transitions in the process are explicitly modeled and characterized using simple multivariate rules. Online data is then used to identify the occurrence of transitions and thus track the process across different phases. Supervisory control actions such as sequence control, phase-specific regulatory controllers and alarm settings are then subsequently executed based on this knowledge of the current phase. One key advantage of this approach is that it does not require detailed process knowledge or extensive process data and is thus well suited for application in process development. This approach has been implemented as an online expert system, called i Prophet, and successfully tested on two laboratory-scale process development case studies—a bacterial ( Escherichia coli ) fermentation and laboratory-scale yeast ( Pichia pastoris ) fermentation. The approach and results from the two case studies are presented.

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 (Qc–Qo) 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.

Model-Based Adaptive Control of Fed-Batch Fermentation Process with the Substrate Consumption Rate Application

This paper deals with the model-based adaptive predictive control of the fed-batch fermentation process. For this system the theoretical approach to the substrate consumption rate is given and the estimation procedure with application of the recursive least-squares method is proposed. On the basis of this estimated value two nonlinear model-based adaptive predictive controllers for substrate control have been designed. Both estimation procedure and adaptive controllers have been derived only on the basis of the general form of the state equation written for the substrate concentration so there is no necessity to know the form of the nonlinear part of the model of the system. The estimation accuracy and the control performance have been demonstrated by means of computer simulation.

Modelling and control of fed-batch fermentation processes: towards improved quality and productivity

This paper describes research into modelling, estimation and control of fed batch fermentation and an application to a large fermentation plant producing secondary metabolites. The problems are examined in a general sense from the viewpoints of both biological science and control engineering. Very extensive modelling and estimation work has been carried out. Only the most significant outcomes are reported here but references to the detailed research are given. Some of the research has resulted in the development of a novel advanced supervisory system- the Intelligent Process Management System (IPMS). Experience of applying the IPMS to a large-scale production process is described. The software has been designed on a modular basis and although implemented initially at one production plant, the approach is generic and fundamentally plant-independent so that further application to a range of other plants is envisaged.

Optimal adaptive control of fed-batch fermentation processes

This paper presents a unifying methodology for optimization of biotechnological processes, namely optimal adaptive control, which combines the adbantages of both the optimal control and the adaptive control approaches. As an example, the design of a substrate feeding rate controller for a class of biotechnological processes in stirred tank reactors characterized by a decoupling between biomass growth and product formation is considered. More specifically, the most common case is considered of a process with monotonic specific growth rate and non-monotonic specific production rate as functions of substrate concentration. The main contribution is to illustrate how the insight, obtained by preliminary optimal control studies, leads to the design of easy-to-implement adaptive controllers. The controllers derived in this way combine a nearly optimal performance with good robustness properties against modeling uncertainties and process disturbances. Since they can be considered model-independent, they may be very helpful also in solving the model discrimination problem, which often occurs during biotechnological process modeling. To illustrate the method and the results obtained, simulation results are given for the penicillin G fed-batch fermentation process.

061 Singular optimal control of fed-batch fermentation processes with state inequality constrainst

061 Singular optimal control of fed-batch fermentation processes with state inequality constrainst

139 Optimal adaptive control of fed-batch fermentation processes with growth/production decoupling: J.F. Van Impe, G. Bastin, B. De Moor, V. Van Breusegem, J. Vandewalle, pp 351–354

139 Optimal adaptive control of fed-batch fermentation processes with growth/production decoupling: J.F. Van Impe, G. Bastin, B. De Moor, V. Van Breusegem, J. Vandewalle, pp 351–354

Singular Optimal Control of Fed-batch Fermentation Processes with State Inequality Constraints

We consider the design of a substrate feed rate controller for a class of biotechnical processes in stirred tank reactors characterized by growth/production decoupling, for the most common case of monotonic specific growth rate and non-monotonic specific production rate. With the volumetric substrate feed rate as the manipulated variable, this yields a singular optimal control problem. Due to state inequality constraints complete solutions are especially hard to obtain and almost nonexistent in the literature. However, the techniques proposed in the literature to convert the problem into a non-singular one all suffer from problems with respect to practical implementation. Therefore, we adopt the traditional singular control viewpoint. The algorithm presented has some marked advantages over algorithms reported in the literature. In addition, we present a complete general solution in the realistic case of input and state inequality constraints. This approach leads in a natural way to the design of robust optimal adaptive control strategies (Van Impe 1993).

On adaptive control of fedbatch fermentation processes

This paper presents an experimental evaluation of the partial state reference model adaptive control approach proposed by M'Saad et al. The plant to be controlled is a fedbatch fermentation process of alcohol production. Experimental results are presented to demonstrate the performances of the adaptive controller considered. Particular emphasis is placed on the choice of the predictor dynamics and the initialization of the parameter estimator

Control of Fed-Batch Fermentation Process Using a Self-Organizing Fuzzy Controller

An algorithm has been developed for controlling the substrate concentration at a predetermined set point or following a trajectory in a fed-batch fermenur. It is a model-independent self-organizing fuzzy controller with a backpropagation neural network. The neural network is used to classify process error patterns. The classified error patterns form a basis for rule formation and tuning of the fuzzy controller. Different tuning strategies are used for different patterns of the process error. The tuning operation is accomplished by varying scaling factors. Through simulation studies with a model system of lysine fermentation. the performance of the self-organizing fuzzy controller was evaluated and compared with that of a best tuned PID controller. The classified error patterns by neural nets are well corresponded to intuitive patterns. The overall performance of the self-organizing fuzzy controller was observed to be satisfactory for regulatory and servo problems despite measurement noise.

Optimal Adaptive Control of Fed-batch Fermentation Process with Growth/Production Decoupling

We consider the design of a substrate feeding rate controller for a class of biotechnological processes in continuous stirred tank reactors, characterized by a decoupling between biomass growth and product formation. The main contribution is to illustrate how the insight, obtained by preliminary optimal control studies, leads to the design of an easy to implement adaptive controller. The controller derived this way combines a near optimal performance with good robustness properties against modeling uncertainties and process disturbances. As an example, simulation results are given for the penicillin G fed-batch fermentation process.

ADAPTIVE CONTROL OF FEDBATCH BIOREACTORS

Abstract In this paper, we shall deal with the control of fedbatch fermentation processes exhibiting substrate inhibition phenumenons. A simple adaptive control scheme based on the nonlinear structure of the system is proposed. It is shown how this adaptive controller can contribute to optimize the process yield without the knowledge of the process kinetics being necessary.

Adaptive Control of Fed-Batch Fermentation Processes

In this paper, it is shown how a simple adaptive control algorithm can contribute to optimize the yield of fermentation processes operated in fed-batch. The proposed algorithm does not require any prior analytical expression for the fermentation parameters (like the specific growth rate or the specific production rate). Its performances are compared in simulation to other control solutions (optimal control, PI regulator).

The control of fed-batch fermentation processes—A survey

A survey of the control of fermentation processes operated in fed-batch mode is presented. Truely optimal conditions can only be found by minimizing (or maximizing) a cost criterion subject to the constraints imposed by a mathematical model of the process. This approach leads to the design of a tracking controller, or servomechanism as it is also called. A different approach uses a physiological model of the process to determine a control strategy, which results in keeping a measured or estimated variable at its set-point by means of a regulator.