Evolutionary algorithms for optimal control in fed-batch fermentation processes

In this work, Evolutionary Algorithms (EAs) are used to achieve optimal feedforward control in a recombinant bacterial fed-batch fermentation process, that aims at producing a bio-pharmaceutical product. Three diferent aspects are the target of the optimization procedure: the feeding trajectory (the amount of substrate introduced in a bioreactor per time unit), the duration of the fermentation and the initial conditions of the process. A novel EA with variable size chromosomes and using real-valued representations is proposed that is capable of simultaneously optimizing the aforementioned aspects. Outstanding productivity levels were achieved and the results are validated by practice

A new representation in evolutionary algorithms for the optimization of bioprocesses

Evolutionary Algorithms (EAs) have been used to achieve optimal feedforward control in a number of fed-batch fermentation processes. Typically, the optimization purpose is to set the optimal feeding trajectory, being the feeding profile over time given by a piecewise linear function, in order to reduce the number of parameters to the optimization algorithm. In this work, a novel representation scheme for the encoding of the feeding trajectory over time is proposed. Each gene in the variable sized chromosome has two components: a time label and the real value of the variable. The new approach is compared with a traditional real-valued EA, with chromosomes of constant size and fixed discretization steps. Three distinct case studies are presented, taken from previous work from the authors and literature, all considering the optimization of fed-batch fermentation processes. The experimental results show that the proposed approach is capable of results better or at the same level of quality of the best traditional EAs and is able to automatically evolve the best discretization steps for each case, thus simplifying the EA's setup.Fundação para a Ciência e Tecnologia (FCT) - 59899/EIA/POSC/2004

Evolutionary optimization of a fed-batch penicillin fermentation process

This paper presents a genetic algorithms approach for the optimization of a fed-batch penicillin fermentation process. A customized float-encoding genetic algorithm is developed and implemented to a benchmark fed-batch penicillin fermentation process. Off-line optimization of the initial conditions and set points are carried out in two stages for a single variable and multiple variables. Further investigations with online optimization have been carried out to demonstrate that the yield can be significantly improved with an optimal feed rate profile. The results have shown that the proposed approaches can be successfully applied to optimization problems of fed-batch fermentation to improve the operation of such processes

A Genetic Algorithm for Feeding Trajectory Optimisation of Fed-batch Fermentation Processes

In this work a genetic algorithm is proposed with the purpose of the feeding trajectory optimization during a fed-batch fermentation of E. coli. The feed rate profiles are evaluated based on a number of objective functions. Optimization results obtained for different feeding trajectories demonstrate that the genetic algorithm works well and shows good computational performance. Developed optimal feed profiles meet the defined criteria. The ration of the substrate concentration and the difference between actual cell concentration and theoretical maximum cell concentration is defined as the most appropriate objective function. In this case the final cell concentration of 43 g·l-1 and final product concentration of 125 g·l-1 are achieved and there is not significant excess of substrate

Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm

Fed-batch fermentation has gained attention in recent years due to its beneficial impact in the economy and productivity of bioprocesses. However, the complexity of these processes requires an expert system that involves swarm intelligence-based metaheuristics such as Artificial Algae Algorithm (AAA), Artificial Bee Colony (ABC), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Differential Evolution (DE) for simulation and optimization of the feeding trajectories. DE traditionally performs better than other evolutionary algorithms and swarm intelligence techniques in optimization of fed-batch fermentation. In this work, an improved version of DE namely Backtracking Search Algorithm (BSA) has edged DE and other recent metaheuristics to emerge as superior optimization method. This is shown by the results obtained by comparing the performance of BSA, DE, CMAES, AAA and ABC in solving six fed batch fermentation case studies. BSA gave the best overall performance by showing improved solutions and more robust convergence in comparison with various metaheuristics used in this work. Also, there is a gap in the study of fed-batch application of wastewater and sewage sludge treatment. Thus, the fed batch fermentation problems in winery wastewater treatment and biogas generation from sewage sludge are investigated and reformulated for optimization

Optimization of fed-batch fermentation processes with bio-inspired algorithms

The optimization of the feeding trajectories in fed-batch fermentation processes is a complex problem that has gained attention given its significant economical impact. A number of bio-inspired algorithms have approached this task with considerable success, but systematic and statistically significant comparisons of the different alternatives are still lacking. In this paper, the performance of different metaheuristics, such as Evolutionary Algorithms (EAs), Differential Evolution (DE) and Particle Swarm Optimization (PSO) is compared, resorting to several case studies taken from literature and conducting a thorough statistical validation of the results. DE obtains the best overall performance, showing a consistent ability to find good solutions and presenting a good convergence speed, with the DE/rand variants being the ones with the best performance. A freely available computational application, OptFerm, is described that provides an interface allowing users to apply the proposed methods to their own models and data.The work is partially funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within projects Ref. COMPETE FCOMP-01-0124-FEDER-015079 and PEst-OE/ES/UI0752/2011

Evaluating evolutionary algorithms and differential evolution for the online optimization of fermentation processes

Although important contributions have been made in recent years within the field of bioprocess model development and validation, in many cases the utility of even relatively good models for process optimization with current state-of-the-art algorithms (mostly offline approaches) is quite low. The main cause for this is that open-loop fermentations do not compensate for the differences observed between model predictions and real variables, whose consequences can lead to quite undesirable consequences. In this work, the performance of two different algorithms belonging to the main groups of Evolutionary Algorithms (EA) and Differential Evolution (DE) is compared in the task of online optimisation of fed-batch fermentation processes. The proposed approach enables to obtain results close to the ones predicted initially by the mathematical models of the process, deals well with the noise in state variables and exhibits properties of graceful degradation. When comparing the optimization algorithms, the DE seems the best alternative, but its superiority seems to decrease when noisier settings are considered.Fundo Europeu de Desenvolvimento Regional (FEDER)Fundação para a Ciência e a Tecnologia (FCT

OptFerm - a computational platform for the optimization of fermentation processes

We present OptFerm, a computational platform for the simulation and optimization of fermentation processes. The aim of this project is to offer a platform-independent, user-friendly, open-source and extensible environment for Bioengineering process optimization that can be used to increase productivity. This tool is focused in optimizing a feeding trajectory to be fed into a fed-batch bioreactor and to calculate the best concentration of nutrients to initiate the fermentation. Also, a module for the estimation of kinetic and yield parameters has been developed, allowing the use of experimental data obtained from batch or fed-batch fermentations to reach the best possible model setup. The software was built using a component-based modular development methodology, using Java as the programming language. AlBench. a Model-View-Control based application framework was used as the basis to implement the different data objects and operations, as well as their graphical user interfaces. Also, this allows the tool to be easily extended with new modules, currently being developed

A comparison of algorithms for the optimization of fermentation processes

The optimization of biotechnological processes is a complex problem that has been intensively studied in the past few years due to the economic impact of the products obtained from fermentations. In fed-batch processes, the goal is to find the optimal feeding trajectory that maximizes the final productivity. Several methods, including Evolutionary Algorithms (EAs) have been applied to this task in a number of different fermentation processes. This paper performs an experimental comparison between Particle Swarm Optimization, Differential Evolution and a real-valued EA in three distinct case studies, taken from previous work by the authors and literature, all considering the optimization of fed-batch fermentation processes.Portuguese Foundation for Science and Technology - Project POSC/EIA/59899/2004; Project SeARCH (Services and Advanced Research Computing with HTC/HPC clusters)/Contract CONCREEQ/443/2001

Modelling of Biotechnological Processes - An approach based on Artificial Neural Networks

In this chapter we describe a software tool for modelling fermentation processes, the FerMoANN, which allows researchers in biology and biotechnology areas to access the potential of Artificial Neural Networks (ANNs) for this task. The FerMoANN is tested and validated using two fermentation processes, an Escherichia coli recombinant protein production and the production of a secreted protein with Saccharomyces cerevisiae in fed-batch reactors. The application to these two case studies, tested for different configurations of feedforward ANNs, illustrate the usefulness of these structures, when trained according to a supervised learning paradigm