This paper presents a method to model and optimise the substrate feeding rate of an anaerobic digestion (AD) system. The method is demonstrated for a case study plant in Bangalore, India, using onsite kitchen waste to provide biogas for cooking. The AD system is modelled using Anaerobic Digestion Model No. 1 (ADM1) and a genetic algorithm (GA) is applied to control the substrate feeding rate in order to simultaneously minimise the volume of flared biogas, unmet gas demand and energy cost. Our results show that ADM1 can predict biogas yield from a continuously operated digester well with mean percentage error between daily predicted and measured data values of only 5.7% for March 2017 and 17.8% for July 2017. When biogas flaring and unmet gas demand were minimised, the amount of biogas flared reduced from 886.62 m3 to 88.87 m3 in March and from 73.79 m3 to 68.49 m3 in July. When the energy cost was also considered within an objective function, the biogas flared reduced from 886.62 m3 to 281.27 m3 for March, but increased from 73.79 m3 to 180.11 m3 for July. The amount of flaring increased in July as the energy cost function increased biogas yield without considering surplus gas production beyond demand and storage capacity. As AD systems are often operated to maximise biogas production, these results highlight the need for multi-objective optimisation, particularly for off-grid AD systems.
|Number of pages||11|
|Early online date||16 Apr 2022|
|Publication status||Published - Aug 2022|
Bibliographical note© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
FunderThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 801604 . Funding Information: This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No 801604.
- Substrate feeding rate
- Multi-objective optimisation
- Case study
- Utility function
- Genetic algorithm (GA)
- Anaerobic digestion
- Systems modelling