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Biogas production management systems with model predictive control of anaerobic digestion processes.
Yoshida, Kazuto; Shimizu, Naoto.
Afiliación
  • Yoshida K; Graduate School of Agriculture, Hokkaido University, Sapporo, Hokkaido, 060-8589, Japan.
  • Shimizu N; Agricultural Engineering Laboratory, Research Faculty of Agriculture, Hokkaido University, Kita 9 Nishi 9, Kita-ku, Sapporo, Hokkaido, 060-8589, Japan. shimizu@bpe.agr.hokudai.ac.jp.
Bioprocess Biosyst Eng ; 43(12): 2189-2200, 2020 Dec.
Article en En | MEDLINE | ID: mdl-32683505
ABSTRACT
We developed a biogas production management system to control biogas production by determining the feedstock inputs to the anaerobic digestion process according to fluctuations of the renewable energy supply. The developed system consists of three functions a prediction model for the anaerobic digestion processes, a parameter-estimation system, and a feedstock-determination controller. A prediction model for the anaerobic digestion processes in a state-space representation was constructed for the input-output relationship of biogas generation from organic compounds and the state of methane fermentation. A parameter-estimation system that estimated the parameters included in the prediction model from actual operating process data was built based on adaptive identification theory. The feedstock-determination controller was established based on model predictive control as a method to control biogas production. From the results of the identification experiment, the least square estimator of the parameters converged as the training data increased, and a reliable parameter was given in 1 week. From the results of the numerical simulation and the control experiment, it was confirmed that the biogas production management system developed in this study had a high prediction accuracy and control performance.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Reactores Biológicos / Biocombustibles / Metano Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Bioprocess Biosyst Eng Asunto de la revista: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Año: 2020 Tipo del documento: Article País de afiliación: Japón

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Reactores Biológicos / Biocombustibles / Metano Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Bioprocess Biosyst Eng Asunto de la revista: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Año: 2020 Tipo del documento: Article País de afiliación: Japón
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