Nonlinear model of leachate anaerobic digestion treatment process
In this report a continuous adaptive high-gain observer method is presented for the estimation of state variables that could not be measurable online and unknown time-varying parameters of leachate anaerobic digestion treatment process. The high-gain observer is a variant of the Luenberger extended...
| Autores: | , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2014 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/27973 |
| Acceso en línea: | https://hdl.handle.net/2117/27973 |
| Access Level: | acceso abierto |
| Palabra clave: | automation mathematical programming nonlinear programming. Classificació INSPEC::Optimisation::Mathematical programming Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
| Sumario: | In this report a continuous adaptive high-gain observer method is presented for the estimation of state variables that could not be measurable online and unknown time-varying parameters of leachate anaerobic digestion treatment process. The high-gain observer is a variant of the Luenberger extended observer and involves an adjustable gain parameter. It is characterized by easy implementation and calibration, is stable and exhibit exponential convergence. The observer is based on a simplified mathematical model of the system. Calibration of the model was performed with real data from the Upflow Anaerobic Sludge Blanket (UASB) reactor for landfill leachate treatment in open loop under normal operational conditions. The model performance is evaluated via numerical simulations showing adequate results. The criteria used for considering the model as acceptable is to calculate the values of Mean Magnitude of Relative Error (MMRE) and Prediction at level l. |
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