Bibliographic citations
Huasupoma, E., (2019). Diseño de un controlador de PH basado en redes neuronales para el proceso de hidrólisis enzimática de producción de bioetanol [Tesis, Universidad Nacional de Ingeniería]. http://hdl.handle.net/20.500.14076/18991
Huasupoma, E., Diseño de un controlador de PH basado en redes neuronales para el proceso de hidrólisis enzimática de producción de bioetanol [Tesis]. : Universidad Nacional de Ingeniería; 2019. http://hdl.handle.net/20.500.14076/18991
@misc{renati/264813,
title = "Diseño de un controlador de PH basado en redes neuronales para el proceso de hidrólisis enzimática de producción de bioetanol",
author = "Huasupoma Malca, Enrique",
publisher = "Universidad Nacional de Ingeniería",
year = "2019"
}
Biofuels have emerged as a renewable alternative to conventional fuels, driven by the reduction of oil reserves and the environmental pollution that incurs in the usage of fossil fuels, however cost reduction for biofuels production, specifically second generation bioethanol, evolves at a very slow pace despite many contributions made by researchers, universities and the industry. This study is particularly interested in designing control systems capable of supporting the use of novel enzymes to maximize the generation of sugars that serve as substrate for ethanol fermentation as a mean to reduce costs. For that purpose, the aim of this work is to design a neural network based pH controller for the enzymatic hydrolysis process, this key process requires an accurate pH level control for enzymes to do their work successfully, as small deviations from the pH optimal value cause a significant drop in the process efficiency. A direct inverse neural controller, an adaptive direct inverse neural controller, an internal model control with neural models, a NARMA-L2 controller and an adaptive NARMA-L2 controller are proposed for this control problem and their performances compared both with and without perturbations. Finally, the adaptive NARMA-L2 controller is selected as the best controller among the proposed ones because of its accuracy in tracking the optimal pH level depending of the enzyme employed.
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