Two-stage aqueous two-phase extractions: selection of system composition using a genetic algorithm

Aqueous Two-Phase Systems at Large Scale: Challenges and Opportunities

Aqueous two-phase systems (ATPS) have proved to be an efficient and integrative operation to enhance recovery of industrially relevant bioproducts. After ATPS discovery, a variety of works have been published regarding their scaling from 10 to 1000 L. Although ATPS have achieved high recovery and purity yields, there is still a gap between their bench-scale use and potential industrial applications. In this context, this review paper critically analyzes ATPS scale-up strategies to enhance the potential industrial adoption. In particular, large-scale operation considerations, different phase separation procedures, the available optimization techniques (univariate, response surface methodology, and genetic algorithms) to maximize recovery and purity and economic modeling to predict large-scale costs, are discussed. ATPS intensification to increase the amount of sample to process at each system, developing recycling strategies and creating highly efficient predictive models, are still areas of great significance that can be further exploited with the use of high-throughput techniques. Moreover, the development of novel ATPS can maximize their specificity increasing the possibilities for the future industry adoption of ATPS. This review work attempts to present the areas of opportunity to increase ATPS attractiveness at industrial levels.

Examination of a genetic algorithm for the application in high-throughput downstream process development

Compared to traditional strategies, application of high-throughput experiments combined with optimization methods can potentially speed up downstream process development and increase our understanding of processes. In contrast to the method of Design of Experiments in combination with response surface analysis (RSA), optimization approaches like genetic algorithms (GAs) can be applied to identify optimal parameter settings in multidimensional optimizations tasks. In this article the performance of a GA was investigated applying parameters applicable in high-throughput downstream process development. The influence of population size, the design of the initial generation and selection pressure on the optimization results was studied. To mimic typical experimental data, four mathematical functions were used for an in silico evaluation. The influence of GA parameters was minor on landscapes with only one optimum. On landscapes with several optima, parameters had a significant impact on GA performance and success in finding the global optimum. Premature convergence increased as the number of parameters and noise increased. RSA was shown to be comparable or superior for simple systems and low to moderate noise. For complex systems or high noise levels, RSA failed, while GA optimization represented a robust tool for process optimization. Finally, the effect of different objective functions is shown exemplarily for a refolding optimization of lysozyme.