Evolutionary algorithms for offline and online optimization of fed-batch fermentation processes

In this work, Evolutionary Algorithms (EAs) were used to control a recombinant bacterial fed-batch fermentation process that aims to produce a biopharmaceutical product. Initially, a novel EA, based on real-valued representations and that makes use of individuals with variable sized chromosomes, was used to optimize the process, prior to its run (offline optimization), by simultaneously adjusting the feeding trajectory, the duration of the fermentation and the initial conditions of the process2. A white box mathematical model derived from literature1 and fine tuned by practice was used in the fitness function, based on differential equations and kinetic algebraic equations. Outstanding productivity levels were obtained and the results are validated by practice. Finally, online optimization is proposed, where the EA is running simultaneously with the fermentation process, receiving information regarding the process, updating its internal model and reaching new solutions that will be used to online control. Results obtained by simulation of the system show that without online optimization minor changes cause the process to reach sub-optimal levels in the long run. On the other hand, when online optimization is performed, minor changes are corrected and the behaviour of the system is near optimal

Evolutionary algorithms for static and dynamic optimization of fed-batch fermentation processes

In this work, Evolutionary Algorithms (EAs) are used to control a recombinant bacterial fed-batch fermentation process, that aims at producing a bio-pharmaceutical product. In a first stage, a novel EA is used to optimize the process, prior to its start, by simultaneously adjusting the feeding trajectory, the duration of the fermentation and the initial conditions of the process. In a second stage, dynamic optimization is proposed, where the EA is running simultaneously with the fermentation process, receiving information regarding from the process, updating its internal model, reaching new solutions that will be used for online control

Embedding the DIRECT algorithm in a penalty approach for solving engineering design problems

Publicado em CD-ROMIn this paper we investigate the performance of DIRECT algorithm when solving constrained engineering design problems. For this purpose, the hyperbolic penalty approach is employed and the algorithm is modified in order to preserve feasibility of solutions. The algorithm is illustrated on six well–known engineering problems with promising results. Comparisons with other global optimization solvers are reported and discussed

Identification of yield coefficients in an E. coli model : an optimal experimental design using genetic algorithms

An optimal experimental design for yield coefficients estimation in an unstructured growth model of fed-batch fermentation of E. coli is presented. The feed profile is designed by optimisation of a scalar function based on the Fischer Information Matrix. A genetic algorithm is proposed as the optimisation method due to its efficiency and independence on the initial values.Programa de Desenvolvimento Educativo para Portugal (PRODEP).Fundação para a Ciência e a Tecnologia (FCT) – PRAXISXXI/BD/16961/98

Design of on-line state estimators for a recombinant E. coli fed-batch fermentation

In recent years a remarkable effort has been made in the development of new sensors and process analytical technology. However, it is still difficult to find reliable and low cost commercial sensors for on-line measurements of important variables. Therefore, considerable attention has been focused on the development of on-line software sensors. Nevertheless, the application of those algorithms to complex biological processes is still very incipient. In this work two different state estimators have been studied regarding their applicability to the recombinant Escherichia coli fed-batch fermentation. Both algorithms showed the ability to estimate on-line biomass and acetate concentrations. However, the extended Kalman observer exhibited a better convergence in spite of being less flexible regarding the combination of the measured and estimated variables.PRODEP ; Fundação para a Ciência e a Tecnologia (FCT

Design of interval observers for an E. coli fed-batch fermentation with uncertain inputs

In bioreactors, the measurement of variables that play a key role in the quality and productivity of fermentations, is of major importance. However, their direct measurement is often expensive or even impossible considering the current sensor technology. Therefore, on-line estimation of unmeasured variables in bioreactors can be an interesting approach. The objective of this work is to introduce an alternative solution for the state observation of bioprocesses in cases where the kinetic model is unclear and the concentration of the influent substrates is badly known, a situation that is common in many practical applications. The high-cell density fed-batch fermentation of Escherichia coli is studied in terms of applicability of a simple interval observer for the estimation of relevant variables of the process, when uncertainties of the process inputs exist. The simple interval observer is designed on the basis of the cooperativity properties of the observer error dynamics (Rapaport and Dochain, 2005). Further assumptions are the knowledge of the (lower and upper) bounds of the influent substrate concentration. Furthermore, an appropriate state transformation and conditions that guarantee system cooperativity have been introduced for that purpose. The performance of the interval observer is illustrated through numerical simulation.Programa de Desenvolvimento Educativo para Portugal (PRODEP)

On-line estimation of biomass in an E. coli fed-batch fermentation

In this work, an Extended Kalman Observer is applied to the on-line determination of biomass concentration in a high-cell density fed-batch fermentation of Escherichia coli. Although the importance of this fermentation process for the biopharmaceutical industry is widely recognized, there are still several difficulties associated with the design of monitoring and control algorithms that could improve the performance of the process by decreasing the production costs and increasing the yield. In this process, biomass concentration has an important role for model predictive control, estimation of specific growth rates, prevention of acetate accumulation and optimization of the production of recombinant proteins (regarding both productivity and moment of induction). However, nowadays it is still determined using off-line laboratory analysis, making it of limited use for control purposes. For the development of the Extended Kalman Observer, a dynamical mathematical model of the process was used, which includes balance equations for the main state variables (biomass, glucose, acetate, dissolved oxygen and carbon dioxide concentrations) together with a complex kinetic model describing the 3 main metabolic pathways of Escherichia coli. The observer applied in this work requires the on-line measurement of a subset of state variables (dissolved oxygen and carbon dioxide concentrations) together with broth weight and gaseous mass transfer rates. State-of-the-art sensors were used for measuring dissolved oxygen and carbon dioxide concentrations and gaseous transfer rates were determined on-line using commercial gas analysers. The calculations were performed on-line in a developed LabVIEW data acquisition and control system. The extended Kalman observer exhibited a good convergence to the real values of biomass concentration, with a very low quadratic difference between experimental and estimated data. Also, the sampling frequency for the measured variables is compatible with the existing experimental data.Programa de Desenvolvimento Educativo para Portugal (PRODEP)

Monitoring of fed-batch E. coli fermentations with software sensors

Accurate monitoring and control of industrial bioprocess requires the knowledge of a great number of variables, being some of them not measurable with standard devices. To overcome this difficulty, software sensors can be used for on-line estimation of those variables and, therefore, its development is of paramount importance. An Asymptotic Observer was used for monitoring Escherichia coli fed-batch fermentations. Its performance was evaluated using simulated and experimental data. The results obtained showed that the observer was able to predict the biomass concentration profiles showing, however, less satisfactory results regarding the estimation of glucose and acetate concentrations. In comparison with the results obtained with an Extended Kalman Observer, the performance of the Asymptotic Observer in the fermentation monitoring was slightly better.recSysBioPrograma de Desenvolvimento Educativo para Portugal III (PRODEP

Estimation of biomass concentration using interval observers in an E. coli fed-batch fermentation

In bioreactors, the measurement of variables that play a key role in the quality and productivity of fermentations, is of major importance. However, their direct measurement is often expensive or even impossible considering the current sensor technology. Therefore, on-line estimation of unmeasured variables in bioreactors can be an interesting approach. The objective of this work is to introduce an alternative solution for the observation of biomass concentration in E. coli fed-batch fermentations, in cases where the kinetic model is unclear and several variables, like the concentration of the influent substrates and the initial values of the state variables are badly known, a situation that is common in many practical applications. The simple interval observer is designed on the basis of the cooperativity properties of the observer error dynamics (Rapaport and Dochain, 2005). The performance of the interval observer is illustrated through numerical simulation and it was found that the observer deal well with uncertainties up to 50% and with white noise in the variables measured on-line. The interval obtained for the biomass estimation is also quite narrow, indicating that it is possible to accurately predict biomass concentration under the presence of uncertainties.Programa de Desenvolvimento Educativo para Portugal (PRODEP)Fundação para a Ciência e a Tecnologia (FCT) - Projecto recSysBio POCI/BIO/60139/200