MULTIOBJECTIVE OPTIMIZATION OF AN UPFLOW ANAEROBIC SLUDGE BLANKET REACTOR

Descripción del Articulo

The purpose of this paper is to optimiza the operation of an upflow anaerobic sludge blanket (UASB) reactor. In this kind of processes, besides to maximiza organic matter removal, it is attractive to capture the biogas and to use it to provide energy services. For this purpose, the biogas has to be...

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Detalles Bibliográficos
Autores: Tomita, Rosana K., Sotomayor, Oscar A. Z., Park, Song W., Tisza Contreras, Juan F.
Formato: artículo
Fecha de Publicación:2007
Institución:Universidad Nacional Mayor de San Marcos
Repositorio:Revistas - Universidad Nacional Mayor de San Marcos
Lenguaje:español
OAI Identifier:oai:ojs.csi.unmsm:article/4096
Enlace del recurso:https://revistasinvestigacion.unmsm.edu.pe/index.php/quim/article/view/4096
Nivel de acceso:acceso abierto
Materia:Reactor UASB
Tratamiento de aguas residuales
optimización multiobjetivo
Multiplex
métodos lnterior puntos
algoritmos genéticos
modelos multivariados
UASB reactor
Wastewater treatment
Multiobjective optimization
lnterior-point methods
Genetic algorithms
Multivariate modelling.
Descripción
Sumario:The purpose of this paper is to optimiza the operation of an upflow anaerobic sludge blanket (UASB) reactor. In this kind of processes, besides to maximiza organic matter removal, it is attractive to capture the biogas and to use it to provide energy services. For this purpose, the biogas has to be produced in large quantities. Thus, we have two clear objectives to be achieved: to maximiza both the organic matter removal and the biogas production. Three multiobjective optimization techniques are used to solve this problem. The first optimization approach is multiplex, which is based on the simplex method for single objective optimization. Other used approach is an interior point method, which is proved to be an efficient linear programming technique. Finally, it is applied an evolutionary algorithm, namely the elitist non-dominated sorting genetic algorithm (NSGA 11), which is considerad a very attractive heuristic method. Formulation of the multiobjective optimization problem is based on a multivariate regression model, which is built using experimental data from a full-scale UASB reactor, at CETESB, in Sao Paulo City, Brazil. Obtained optimization solutions are comparad and discussed.
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