Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization

•QLS was not capable of fitting adequate models that could explain the variability.•RBF allowed obtaining more reliable models in comparison with QLS.•Through PSO, optimal combinations to maximize the responses were obtained.•Different biomass hydrolyzed yielded different results due to its different structure.

The concentrations of glucose and total reducing sugars obtained by chemical hydrolysis of three different lignocellulosic feedstocks were maximized. Two response surface methodologies were applied to model the amount of sugars produced: (1) classical quadratic least-squares fit (QLS), and (2) artificial neural networks based on radial basis functions (RBF). The results obtained by applying RBF were more reliable and better statistical parameters were obtained. Depending on the type of biomass, different results were obtained. Improvements in fit between 35% and 55% were obtained when comparing the coefficients of determination (R2) computed for both QLS and RBF methods. Coupling the obtained RBF models with particle swarm optimization to calculate the global desirability function, allowed to perform multiple response optimization. The predicted optimal conditions were confirmed by carrying out independent experiments.

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