Mathematical model for a continuous hydrogen production system: Stirred fermenter connected to a biocatalyzed electrolysis cell

This paper presents a mathematical model applied to a continuous hydrogen production system, composed of a stirred fermenter connected to a biocatalyzed electrolysis cell (BEC). The model contemplates two differential equation systems which describe the adaptation (start-up) and continuous phases between the fermenter and the BEC. The proposed model describes the dynamics of hydrogen and volatile fatty acid (VFA) production and substrate consumption (glucose for the stirred fermenter and acetate in the BEC), based on a Tessiertype bacterial kinetic which simulates the lag phase in the bacteria. A hybrid evolutionary algorithm and least squares method were used to estimate the parameters. Model validation and simulation were achieved by obtaining the volumes of hydrogen and VFAs produced and the statistical bacterial density via the most probable number (MPN) method.

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Bibliographic Details
Main Author: LILIANA MARIA ALZATE GAVIRIA
Format: info:eu-repo/semantics/article biblioteca
Subjects:info:eu-repo/classification/cti/7,
Online Access:http://cicy.repositorioinstitucional.mx/jspui/handle/1003/56
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Summary:This paper presents a mathematical model applied to a continuous hydrogen production system, composed of a stirred fermenter connected to a biocatalyzed electrolysis cell (BEC). The model contemplates two differential equation systems which describe the adaptation (start-up) and continuous phases between the fermenter and the BEC. The proposed model describes the dynamics of hydrogen and volatile fatty acid (VFA) production and substrate consumption (glucose for the stirred fermenter and acetate in the BEC), based on a Tessiertype bacterial kinetic which simulates the lag phase in the bacteria. A hybrid evolutionary algorithm and least squares method were used to estimate the parameters. Model validation and simulation were achieved by obtaining the volumes of hydrogen and VFAs produced and the statistical bacterial density via the most probable number (MPN) method.