Multi-objective optimization through artificial intelligence for designing of an Agave angustifolia leaf shredder

Abstract

A neural network and a genetic algorithm were used in a hybrid method to get the optimal design parameters of an Agave angustifolia Haw. green leaf shredder. First, a prototype of an experimental machine was built using the design parameters recommended by the literature and calculated using linear equations. Then, the shredder prototype was subjected to experiments. The defibration data with different blade adjustments were obtained with experimental values. The data was configured and trained with an artificial neural network to establish a correlation between the defibration quality and the design parameters. The multi-objective optimization method based on genetic algorithms determined the optimal design parameters of the shredder’s functional mechanical elements. The best point was obtained from the least number of broken fibers (2.83%) and the most waste (73.15%). The method used proved suitable to optimize the design parameters; this was based on actual data obtained by experiments performed with the prototype and then modeled through artificial intelligence methods such as neural networks to determine an optimal solution using evolutionary genetic algorithm methods.