Modeling and Simulation of Alternative Energy Generation Processes using HONN

Modeling and Simulation of Alternative Energy Generation Processes using HONN

Salvador Carlos Hernández (Cinvestav del IPN, Unidad Saltillo, México), Edgar Nelson Sanchez Camperos (Cinvestav del IPN, Unidad Guadalajara, México), Rocío Carrasco Navarro (Cinvestav del IPN, Unidad Guadalajara, México), Joel Kelly Gurubel Tun (Cinvestav del IPN, Unidad Guadalajara, México) and José Andrés Bueno García (Cinvestav del IPN, Unidad Guadalajara, México)
DOI: 10.4018/978-1-4666-2175-6.ch008
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This chapter deals with the application of Higher Order Neural Networks (HONN) on the modeling and simulation of two processes commonly used to produce gas with energy potential: anaerobic digestion and gasification. Two control strategies for anaerobic digestion are proposed in order to obtain high biomethane flow rate from degradation of organic wastes such as wastewater. A neurofuzzy scheme which is composed by a neural observer, a fuzzy supervisor, and two control actions is presented first. After that, a speed-gradient inverse optimal neural control for trajectory tracking is designed and applied to an anaerobic digestion model. The control law calculates dilution rate and bicarbonate in order to track a methane production reference trajectory under controlled conditions and avoid washout. A nonlinear discrete-time neural observer (RHONO) for unknown nonlinear systems in presence of external disturbances and parameter uncertainties is used to estimate the biomass concentration, substrate degradation, and inorganic carbon. On the other side, a high order neural network structure is developed for the process identification in a gasification reactor; the gas, composed mainly of hydrogen and carbon monoxide (synthesis gas or syngas), is produced from thermo chemical transformation of solid organic wastes. The identifier is developed in order to reproduce a kinetic model of a biomass gasifier. In both cases (biological and thermo chemical processes), the Extended Kalman Filter (EKF) is used as a training algorithm. The proposed methodologies application is illustrated via numerical simulations.
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The energy and environmental situation around the world makes the power generation system based on renewable sources take more relevance. These systems have many advantages since they allow the energy diversification reducing dependence on oil; using renewable raw material it is possible to decrease CO2 global emissions. Processes using organic wastes allow environmental risks to be minimized by the valorization of that kind of materials.

Biomass is a resource with high energy potential and represents an opportunity area for both, developed and emerging countries. Then, study of processes for wastes transformation is an important topic all around the world. Biological and thermo chemical systems are well identified to take advantage of biomass potential for energy generation.

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