Evolutionary Algorithms for Optimal Control in Fed-Batch Fermentation Processes

Advances in Differential Evolution

Pinto

et al.

Self Cite

Summary. The optimization of input variables (typically feeding trajectories over time) in fed-batch fermentations has gained special attention, given the economic impact and the complexity of the problem. Evolutionary Computation (EC) has been a source of algorithms that have shown good performance in this task. In this chapter, Differential Evolution (DE) is proposed to tackle this problem and quite promising results are shown. DE is tested in several real world case studies and compared with other EC algorihtms, such as Evolutionary Algorithms and Particle Swarms. Furthermore, DE is also proposed as an alternative to perform online optimization, where the input variables are adjusted while the real fermentation process is ongoing. In this case, a changing landscape is optimized, therefore making the task of the algorithms more difficult. However, that fact does not impair the performance of the DE and confirms its good behaviour.

“…In previous work by the authors, a fed-batch recombinant Escherichia coli fermentation process was optimized by EAs [16] [17]. This was considered as the first case study in this work.…”

Section: Case Study Imentioning

confidence: 99%

“…A real-valued Evolutionary Algorithm (EA) was also considered, since it provided good results in previous work [17] [18]. The overall structure of the EA is given by:…”

Section: Real-valued Eamentioning

confidence: 99%

Differential Evolution for the Offline and Online Optimization of Fed-Batch Fermentation Processes

Mendes

Advances in Differential Evolution

Pinto

et al.

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“…As previously presented [4], a white box mathematical model was developed, based on differential equations that represent the mass balances to the state variables, namely F in,S the substrate feeding rate (in kg/h), W the culture medium weight (in kg), S the glucose, X the biomass, O the dissolved oxygen, C the dissolved carbon dioxide and A the acetate. Real experiments were used as the basis for the model derivation, being conducted in a fermentation laboratory with a 5-L bioreactor and the experimental results were consistent with the model.…”

Section: The Fed-batch Fermentation Processmentioning

confidence: 99%

“…In this work, a fed-batch recombinant E. coli fermentation process to produce a bio-pharmaceutical product [4] was studied. In fed-batch fermentations there is an addition of certain nutrients along the process, in order to prevent the accumulation of toxic sub-products.…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary Algorithms for Static and Dynamic Optimization of Fed-batch Fermentation Processes

Adaptive and Natural Computing Algorithms

Neves

Veloso

et al.

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In this work, Evolutionary Algorithms (EAs) are used to control a recombinant bacterial fed-batch fermentation process, that aims to produce a bio-pharmaceutical product. Initially, a novel EA was used to optimize the process, prior to its run, by simultaneously adjusting the feeding trajectory, the duration of the fermentation and the initial conditions of the process. Finally, dynamic 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 for online control.

Evaluating Evolutionary Algorithms and Differential Evolution for the Online Optimization of Fermentation Processes

Pinto

Lecture Notes in Computer Science

et al.

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Abstract. 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.