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

Abstract

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.

Optimization of Bioethanol In Silico Production Process in a Fed-Batch Bioreactor Using Non-Linear Model Predictive Control and Evolutionary Computation Techniques

“…This could be explained for the simplicity of the microbial growth model, which does not account for deleterious effects such as overflow metabolism or inhibitory shocks for substrate feeding. It is worthwhile to mention that the obtained results, in terms of GA and DE comparison, corroborates with the empirically observed in the literature, as the genetic algorithm exhibits in general more result variability than the differential evolution [50,62]. Also, it is important to notice that for both algorithms the exponential parametrization of the feed rate exhibited larger variaton among the results (expressed in terms of its standard deviation), especially for the assays with higher time interval between the optimization studies.…”

“…The GA and the DE represent two important members of the evolutionary computation techniques, which are referred to for their good performance on optimization problems applied to biotechnology [18,50]. The generic form of the GA and DE methods algorithms are schematically outlined in Figures 1 and 2, respectively.…”

“…In order to evaluate the performance of both algorithms in terms of its intrinsic properties, a trial run was performed for each one using an open-loop feed rate optimization concept, as presented by several authors, such as Banga et al Ochoa, Rocha et al [14,15,18,50], for the initial configurations presented in Table 5, and a fixed batch time of 50, 54, 59.05 and 62.9 h, using the cosinoidal parametrization for the feed rate (14). The results are presented in Table 11, from the information presented in the work of Ochoa [15].…”

The use of differential evolution algorithm for solving chemical engineering problems

“…As the dynamic optimization of the fermentation processes is a problem requiring not only powerful algorithms but also carefully chosen methodologies, Rocha et al (2007) proposed an online optimization procedure based on DE. Three different case studies were considered: (1) fed-batch recombinant Escherichia coli fermentation, where microorganisms follow three different metabolic paths (oxidative growth on glucose, fermentative growth on glucose, and oxidative growth on acetic acid); (2) a hybridoma reactor; and (3) fed-batch bioreactor for the production of ethanol by Saccharomyces cerevisiae.…”

“…However, when noisier settings were employed, the DE algorithm performance decreased. Using the same case studies and objectives (from Rocha et al 2007), Mendes et al (2008) performed online and offline optimizations of input variables by applying three distinct algorithms, namely DE, a real value EA, and a fully informed particle swarm (FIPS). The results obtained for the offline optimization indicated that DE/Rand/1/ bin has the best results, followed closely by DE/best/2/ bin and FIPS.…”