Non-linear adaptive control of fermentation processes utilizing a priori modelling knowledge (original) (raw)
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Adaptive Control for a Nonlinear Fermentation Process
Adaptive Systems in Control and Signal Processing 1983, 1984
Linearization via optimal control (O.C.) of a discrete nonlinear multivariable system is analysed and an adaptive linear control is presented. The performances of the adaptive control applied to a simulated fermentation process linearized by Taylor series expansion and via O.C. are comoared , showing that the second option gives a very robust control structure and a better performance. ~eywords. Adaptive control; optimal control; nonlinear systems; fermentation process.
Adaptive predictive control of a multistage fermentation process
Biotechnology and Bioengineering, 1990
Theoretical and experimental studies concerning the application of modern adaptive techniques for the control of continuous alcoholic fermentation of glucose by a yeast strain conducted in a multistage reactor are reported. The practical control objective was the regulation of the substrate concentration in the process effluent. Mathematical expressions for the control scheme are given, with the underlying control engineering argumentation. Suitable feeding of sugar for stage 2 was controlled to minimize the effluent sugar concentration.
Takagi-Sugeno multiple-model controller for a continuous baking yeast fermentation process
2007
The purpose of this work is to design a fuzzy integral controller to force the switching of a bioprocess between two different metabolic states. A continuous baking yeast culture is divided in two sub-models: a respiro-fermentative with ethanol production and a respirative with ethanol consumption. The switching between both different metabolic states is achieved by means of tracking a reference substrate signal. A substrate fuzzy integral controller model using sector nonlinearity was built for both nonlinear models.
Optimal adaptive control of fed-batch fermentation processes
1995
This paper presents a unifying methodology for optimization of biotechnological processes, namely optimal adaptive control, which combines the advantages 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 biotechnologlcal 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.
Modelling and adaptive predictive control of a continuous fermentation process
1992
This paper deals with the modelling and adaptive predictive control of a continuous stirred tank reactor (CSTR). The problem o fsimtdtaneoas regulation and trac'king of biomass concentration is studies. A discrete adaptive controller using on-line estimution of the specific grou,th rate is developed. Stability and rohastness of this adaptive scheme is provided in the sense o['signal boandedness. Good simulation results have been obtained in regulation and tracking, disturbance rejection, and transient behavior, showing the effic'ienc3' of this a&~ptive predictive control scheme.
Optimising control of fermentation processes
IEE Proceedings D Control Theory and Applications, 1992
The high costs associated with many fermentation processes in an increasingly competitive industry make the optimisation of bioreactor performance very desirable. Attempts to improve performance have primarily been focused on the development of online adaptive methods. Although these techniques can provide the optimum solution, each had limitations in practical implementation. The paper addresses these problems and considers their effects on the optimisation of bioreactor performance. An online time series model is sought in order to evaluate the steady-state performance of the bioreactor. A simple algorithm is developed around this theme and is analysed to show convergence to the optimum solution. Nonlinear simulation results, using the Monod equations for yeast fermentations, are presented. For this processes, two approaches using an online identified linear model or nonlinear model, are shown to give optimum results. List of symbols ai, bi , ci = coefficients of the A, B, and C polynomials q p l = backward shift operator Y = model output Y* = process output A(q I), B(q-I), C(q-I) = polynomials in the backward 60 fi, fi, = Kuhn-Tucker multipliers r, y = point-to-set mappings (42 = algorithmic mapping z, E, 6 = constants
Robust compensator control of continuous fermentation processes
Bioprocess Engineering, 1996
The paper deals with the robust compensator control ~'u, fiij of continuous fermentation processes described by a set of cq +,fiif three non-linear differential equations. For the design 0 purposes the non-linear model is transformed into linear one with interval parameters. Robust state space compensator is Subscripts designed by the internal model principle, which ensures robust i, j step-wise set points asymptotic tracking and external disturbances rejection in the wide working range. The effectiveness of the algorithm designed is performed by computer simulation experiments. An important feature of the proposed algorithms is their robustness over parameter uncertainties in the process models.
Nonlinear analysis and control of a continuous fermentation process
Computers & Chemical Engineering, 2002
Different types of nonlinear controllers are designed and compared for a simple continuous bioreactor operating near optimal productivity. This operating point is located close to a fold bifurcation point. Nonlinear analysis of stability, controllability and zero dynamics is used to investigate open-loop system properties, to explore the possible control difficulties and to select the system output to be used in the control structure. A wide range of controllers are tested including pole placement and LQ controllers, feedback and input-output linearization controllers and a nonlinear controller based on direct passivation. The comparison is based on time-domain performance and on investigating the stability region, robustness and tuning possibilities of the controllers. Controllers using partial state feedback of the substrate concentration and not directly depending on the reaction rate are recommended for the simple fermenter. Passivity based controllers have been found to be globally stable, not very sensitive to the uncertainties in the reaction rate and controller parameter but they require full nonlinear state feedback.
A new adaptive scheme for the adaptive linearizing control of bioprocesses
1996
This work deals with the development of model-based adaptive control algorithms for bioprocess operation. Non-linear adaptive control laws are proposed for single input single output regulation. Parameters are continuously adapted following a new adaptive scheme which ensures second-order dynamics of the parameter error system. A computational study is presented of the application of this theory to baker's yeast fermentation. Results put in evidence the efficient performance both of the adaptive scheme and of the related control laws.
Journal of Process Control, 2004
In many industrial fermentation processes oxygen availability is the main limiting factor for product production. Typically the dissolved oxygen (DO) concentration decreases continuously at the beginning of the batch until it reaches a critical level where the oxygen transfer rate is very close to the vessel's maximum transfer capacity. The process may be further driven close to this sensitive operating point with a controller that manipulates the carbon source feed rate. This operating strategy is linked with important productivity issues and is still frequently realised in open-loop at production scale. The main purpose of the present study is to derive an effective closed-loop control solution and to demonstrate its economical advantage in relation to the open-loop form of operation. A stable model reference adaptive controller (MRAC) was designed based on a phenomenological model of the process. The implementation requires two on-line measurements: the DO tension and oxygen transfer rate (OTR) between gas-liquid phases, which are nowadays standard and easily available in production facilities. The controller performance is accessed with a simulation case study. The main results show that the adaptive controller is precise, stable and robust to disturbances and to inaccuracies like variability in raw materials typical in fermentations run in complex media. The controller is simple, easy to implement, and could possibly improve productivity in processes for which oxygen transfer capacity is limiting growth and product production.