On The Use of Genetic Algorithms for Parameter Identification in Anaerobic Digestion Modelling


Autoria(s): Cavaleiro Costa, Sérgio; Janeiro, Fernando M.; Malico, Isabel
Data(s)

02/01/2017

02/01/2017

01/06/2016

Resumo

Anaerobic digestion (AD) of wastewater is a very interesting option for waste valorization, energy production and environment protection. It is a complex, naturally occurring process that can take place inside bioreactors. The capability of predicting the operation of such bioreactors is important to optimize the design and the operation conditions of the reactors, which, in part, justifies the numerous AD models presently available. The existing AD models are not universal, have to be inferred from prior knowledge and rely on existing experimental data. Among the tasks involved in the process of developing a dynamical model for AD, the estimation of parameters is one of the most challenging. This paper presents the identifiability analysis of a nonlinear dynamical model for a batch reactor. Particular attention is given to the structural identifiability of the model, which considers the uniqueness of the estimated parameters. To perform this analysis, the GenSSI toolbox was used. The estimation of the model parameters is achieved with genetic algorithms (GA) which have already been used in the context of AD modelling, although not commonly. The paper discusses its advantages and disadvantages.

Identificador

Cavaleiro Costa, S., Janeiro, F. M., Malico, I., (2016). (Resumo). On The Use of Genetic Algorithms for Parameter Identification in Anaerobic Digestion Modelling. 12th International Conference on Diffusion in Solids and Liquids: Mass Transfer, Heat Transfer and Microstructure and Properties – DSL 2016, Split, Croácia, 27-30 de Junho.

http://hdl.handle.net/10174/19483

nao

nao

nao

CEM, DFIS

sergiocavaleirocosta@gmail.com

fmtj@uevora.pt

imbm@uevora.pt

275

Idioma(s)

por

Direitos

restrictedAccess

Palavras-Chave #inverse methods #genetic algorithms #anaerobic digestion #biogas #parameter estimation #dynamical model
Tipo

lecture