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. 2021 Feb;44(2):329-342.
doi: 10.1007/s00449-020-02445-y. Epub 2020 Sep 29.

Hybrid neural network modeling and particle swarm optimization for improved ethanol production from cashew apple juice

Andréa da Silva Pereira  1 , Álvaro Daniel Teles Pinheiro  2 , Maria Valderez Ponte Rocha  1 , Luciana Rocha B Gonçalves  3 , Samuel Jorge Marques Cartaxo  1
Affiliations

Hybrid neural network modeling and particle swarm optimization for improved ethanol production from cashew apple juice

Andréa da Silva Pereira et al. Bioprocess Biosyst Eng. 2021 Feb.

Abstract

A hybrid neural model (HNM) and particle swarm optimization (PSO) was used to optimize ethanol production by a flocculating yeast, grown on cashew apple juice. HNM was obtained by combining artificial neural network (ANN), which predicted reaction specific rates, to mass balance equations for substrate (S), product and biomass (X) concentration, being an alternative method for predicting the behavior of complex systems. ANNs training was conducted using an experimental set of data of X and S, temperature and stirring speed. The HNM was statistically validated against a new dataset, being capable of representing the system behavior. The model was optimized based on a multiobjective function relating efficiency and productivity by applying the PSO. Optimal estimated conditions were: S0 = 127 g L-1, X0 = 5.8 g L-1, 35 °C and 111 rpm. In this condition, an efficiency of 91.5% with a productivity of 8.0 g L-1 h-1 was obtained at approximately 7 h of fermentation.

Keywords: Artificial neural network (ANN); Cashew apple juice; Ethanol production; Hybrid neural model (HNM); Particle swarm optimization (PSO).

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