The aim of this work was to evaluate the performance of two optimisation methods applied for the ABE (acetone, butanol, ethanol) fermentation. The flash fermentation process consists of three interconnected units, as follows: fermentor, cell retention system (tangential microfiltration) and vacuum flash vessel (responsible for the continuous recovery of butanol from the broth). A deterministic method (Sequential Quadratic Programming (SQP)) and a stochastic global search method (Genetic Algorithm) were used to solve the optimisation problem. The objective of the optimisation was the search of the process inputs that maximise the productivity of butanol for a desired substrate conversion. The optimisation problem is characterised by its high dimension since the equality constraints are composed by differential equations. With both optimisers, similar solutions to the optimisation problem were obtained. The optimised process ran on concentrated sugar solution (approximately 140 g/l), reaching a high butanol productivity (9.0 g/l.h). In relation to the computational effort, the time elapsed for solution was around 20 minutes for the SQP method and 120 minutes for the GA method. Although SQP was faster, it did not always converge. On the other hand, the GA method was robust and did not present this problem. Thus, the GA method was considered more suitable for the optimisation of butanol productivity in the flash fermentation process.
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