Modification and experimental calibration of ADM1 for modelling the anaerobic digestion of solid wastes in demand driven applications

• Main Author: Poggio, Davide Antonio
• Other Authors: Pourkashanian, M. ; Nimmo, W. ; Ingham, D. ; Gale, W.
• Published: University of Leeds 2015
• Subjects: 628.4
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.687008

This thesis is an exploration into the modelling of anaerobic digestion (AD) with a focus on its integration into a microgrid for rural electrification. The work investigated the improvement of Anaerobic Digestion Model No 1 (ADM1) in order to better describe the kinetics of biogas production in an AD system with particular focus on substrate characterisation, codigestion and the mechanisms of inhibition. The resulting model was used to investigate the possible role of AD in microgrid systems. A novel biochemical and kinetic fractionation method was developed in order to fully characterise any substrate and produce the required input parameters into the a modified version of ADM1. The method uses a combination of analytical and digestion batch tests and was applied to food waste, green waste, pig manure and oat processing residues. The fractionation method was validated using measurements from semi-continuous laboratory scale digesters, operated with varying substrate combinations and loading rates. The model was able to suitably predict the methane production rate and the typical off-line measurements in AD systems, except during periods of high organic loading rate where biochemical inhibition became an important phenomenon. Possible inhibiting mechanisms were investigated by model based analysis of the experimental data characterised by inhibition, and a possible inhibition mechanism was proposed and integrated in the ADM1 model. Microgrid modelling software HOMER was used alongside the updated version of ADM1 in order to perform a benchmark of various operational and control strategies for the demand-driven operation of an AD system integrated in a microgrid. Different biogas demand profiles were considered. In the case of a biogas demand profile with low variability it was found that simple operational strategies could be used, with limited required biogas storage buffer and without causing process instabilities. With more variable demand profiles, an expert control system was needed in order to reduce the biogas storage requirements and guarantee process stability.