Adaptive optimal operation of a parallel robotic liquid handling station (original) (raw)
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Engineering in Life Sciences, 2016
Bioprocess development, optimization, and control in mini-bioreactor systems require information about essential process parameters, high data densities, and the ability to dynamically change process conditions. We present an integration approach combining a parallel mini-bioreactor system integrated into a liquid handling station (LHS) with a second LHS for offline analytics. Non-invasive sensors measure pH and DO online. Offline samples are collected every 20 min and acetate, glucose, and OD 620 subsequently analyzed offline. All data are automatically collected, analyzed, formalized, and used for process control and optimization. Fed-batch conditions are realized via a slow enzymatic glucose release system. The integration approach was successfully used to apply an online experimental redesign method to eight Escherichia coli fed-batch cultivations. The method utilizes generated data to select the following experimental actions online in order to reach the optimization goal of estimating E. coli fed-batch model parameters with as high accuracy as possible. Optimal experimental designs were recalculated online based on the experimental data and implemented by introducing pulses via the LHS to the running fermentations. The LHS control allows for various implementations of advanced control and optimization strategies in milliliter scale.
Heuristic Parameter Estimation for a Continuous Fermentation Bioprocess
Revista Facultad de Ingeniería Universidad de Antioquia, 2018
Zymomonas mobilis continuous fermentation bioprocess has the ability of producing energy from glucose catabolism, which promises a relevant application for biomass conversion into fuel, and therefore it represents an industrial scale production alternative for our country. However, it has demonstrated high complexity regarding the non-linear and non-Gaussian characteristics of its dynamics. Several works have been dealing not only with the bioprocess modeling but also with controller design and implementation. These works have developed state and parameter estimation strategies based on particle filters and Gaussian methods, as well as closing the loop with nonlinear controllers. Nevertheless, there is a need to improve previous parameter estimation results, enabling future design of control strategies for industrial applications. We present a set of heuristics algorithms for the non-linear system parameter estimation evaluated with data from 150 hours of fermentation. Some algorithms such as local search methods, simulated annealing, population heuristics, differential evolution, bacterial chemotaxis and other techniques were tested for the bioprocess. Simulations of the microorganism model and experimental verifications showed the good performance in parameter accuracy and convergence speed of some of the heuristic methods proposed here. Moreover, the reliability and acceptable computational costs of these methods demonstrate that they could also be applied as parameter estimators for other bioprocesses of a similar complexity. RESUMEN: El bioproceso de fermentación continua Zymomonas mobilis tiene la capacidad de producir energía a partir del catabolismo de la glucosa, lo que promete una aplicación
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.
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
Optimal parametric sensitivity control for the estimation of kinetic parameters in bioreactors
Mathematical Biosciences, 2002
In this paper the well-known problem of optimal input design is considered. In particular, the focus is on input design for the estimation of kinetic parameters in bioreactors. The problem is formulated as follows: given the model structure (f,g), which is assumed to be affine in the input, and the specific parameter of interest theta;(k) find a feedback law that maximizes the sensitivity of the model output to the parameter under different flow conditions in the bioreactor and, possibly, minimize the input or state costs. Analytical solutions to these problems are presented. As an example a bioreactor with a biomass that grows according to the well-known Monod kinetics is considered.
Optimal Experiment Design in Bioprocess Modeling: From Theory to Practice
IFAC Proceedings Volumes, 2006
In this paper the problem of parameter identification for the Monod model is considered. As known for a long time, noisy batch measurements do not allow unique and accurate estimation of the kinetic parameters of the Monod model. Techniques of optimal experiment design are, therefore, addressed to design informative experiments and improve the parameter estimation accuracy. During the design process, practical feasibility has to be kept in mind. In this paper it is demonstrated how a theoretical optimal design can successfully be translated to a feasible optimal design. Both design and validation of informative fed batch experiments are illustrated with a case study that models the growth of the nitrogen fixing bacteria Azospirillum brasilense.
Multi-scale models for the optimization of batch bioreactors
Chemical Engineering Science, 2013
Process models play an important role in the bioreactor design, optimisation and control. In previous work, the bioreactor models have mainly been developed by considering the microbial kinetics and the reactor environmental conditions with the assumption that the ideal mixing occurs inside the reactor. This assumption is relatively difficult to meet in the practical applications. In this paper, we propose a new approach to the bioreactor modelling by expanding the so-called Herbert's Microbial Kinetics (HMK) model so that the developed models are able to incorporate the mixing effects via the inclusion of the aeration rate and stirrer speed into the microbial kinetics. The expanded models of Herbert's microbial kinetics allow us to optimize the bioreactor's performances with respects to the aeration rate and stirrer speed as the decision variables, where this optimisation is not possible using the original HMK model of microbial kinetics. Simulation and experimental studies on a batch ethanolic fermentation demonstrates the use of the expanded HMK models for the optimisation of bioreactor's performances. It is shown that the integration of the expanded HMK model with the Computational Fluid Dynamics (CFD) model of mixing, which we call it as a Kinetics Multi-Scale (KMS) model, is able to predict the experimental values of yield and productivity of the batch fermentation process accurately (with less than 5% errors).
Lecture Notes in Computer Science, 2005
The performance of a continuous-time Recursive Least Squares (CRLS) and a discrete-time Recursive Least Squares (DRLS) algorithms are examined for the growth medium temperature control of a cooling batch bioreactor in which Saccharomyces cerevisiae growth at aerobic condition by using Continuous-time Generalised Predictive Control (CGPC) algorithm. MATLAB programme was utilized for recursive parameter identification algorithms (CRLS and DRLS). The success or otherwise of these algorithms are estimated using parameter norm criterion for the various order of models and several input signals. There is a considerable improvement of identification algorithms with the reduced order of models. It has been shown that the performance of a DRLS algorithm is as successful as the other recursive parameter identification of a continuous-time system model.
Intelligent adaptive control of bioreactors
Journal of Intelligent Manufacturing, 2003
Supervised model-based self-tuning control of fermentation processes is addressed. The diversity, nonlinearity and time-varying nature of these processes make their control a challenging task. Conventional linear (PID) controllers with fixed parameters cannot meet the increasing performance requirements over the whole operating range. In the approach pursued in this research, a local linear model is identified at the current working point by using a limited amount input–output data obtained through an identification experiment. A linear controller is then tuned on the basis of this model. To minimize the intervention into the process operation, this tuning procedure is only initiated if the performance of the current controller deteriorates. To this end, a supervisory expert system is designed whose tasks are to monitor the process performance, design an appropriate identification experiment, validate the obtained model and tune the controller. The supervisory system is based on a combination of a state automaton with a rule-based fuzzy inference system. Experimental results have demonstrated the feasibility of this approach.
2001
Together with some on-line measurements, a reliable process model is the key ingredient of a successful state observer design. In common practice, the model parameters are inferred from experimental data so as to minimize a model prediction error, e.g. so as to minimize an output least-squares criterion. In this procedure, no care is actually exercised to ensure that the unmeasured model states are sensitive to the measured states. In turn, if sensitivity is too low, the resulting state observer will probably generate poor estimates of the unmeasured states. To alleviate these problems, a new parameter identiÿcation procedure is proposed in this study, which is based on a cost function combining a conventional prediction error criterion with a state estimation sensitivity measure. Minimization of this combined cost function produces a model dedicated to state estimation purposes. A thorough analysis of the procedure is presented in the context of bioreactor modeling, including parameter identiÿcation, model validation and design of extended Kalman ÿlters and full horizon observers. ?