Management and design of biogas digesters: A non-calibrated heat transfer model
ELSEVIER, Bioresource Technology, vol. 296, 122264, January 2020
Authors: S.Vilms Pedersen a ; J.Martí-Herrero c,d ; A.K.Singh b ; S.G.Sommer a ; S.D.Hafner a
a University of Southern Denmark, Department of Chemical Engineering, Biotechnology and Environmental Technology, 5230 Odense, Denmark
b University of Southern Denmark, Centre for Energy Informatics, 5230 Odense, Denmark
c Centre Internacional de Mètodes Numérics en Enginyeria (CIMNE), Building Energy and Environment Group, Terrassa, Barcelona, Spain
c Biomass to Resources Group, Universidad Regional Amazónica Ikiam, Va Tena-Muyuna, Km.7, Tena, Napo, Ecuador
A thermal balance modeling framework is developed, based on heat transfer-resistance networks. The heat transfer model accounts for effects of digester- design, location and operation, including effects of solar irradiance, external heating and ambient climate. We demonstrate extendibility of the framework by using the model in dynamic simulations of substrate temperature for digesters comprising two very different designs. Digester designs modeled include fixed-dome, buried, uninsulated and unheated household digesters in Hanoi, Vietnam, and an industrial-scale anaerobic digester located at a wastewater treatment plant in Esbjerg, Denmark. The modeled temperature profiles were evaluated against measured substrate temperatures over long periods, from 7 months and up. For the two Hanoi digesters, root-mean-square-error were 1.43 °C and 0.92 °C, with Nash-Sutcliffe model efficiency coefficients (NS-C) of 0.87 and 0.93 respectively. For the industrial digester in Esbjerg root-mean-square-error was 0.48 °C with an NS-C of 0.94. The model was not calibrated prior simulation, suggesting good predictive performance.
- Development of heat transfer model based on heat transfer-resistance networks.
- Model includes ambient effects, digester design-, volume- and operation effects.
- Dynamic simulations on digester designs ranging from household to industrial scale.
- Validation with measured temperature profiles over long periods, up to 16 months.
- Predictive power demonstrated without model calibration to training data.