Principal Investigator: Dr. Matthew Wade, School of Civil Engineering and Geosciences
Presenter at Institute for Sustainability Conference: Robert Pattinson, School of Civil Engineering and Geosciences
The Robert Pattinson presentation (PDF: 479KB) from the Institute Conference is available to view.
The project focuses on the analysis and understanding of dynamical systems related to the anaerobic digestion process. Current investigations have shown that treating components of the AD system in a simplified way allows for the determination of dynamic and steady-state behaviours of fundamental biological processes that are key in degrading organic substrates and converting them to useful by-products, such as methane.
Dynamic models derived from empirical observations have allowed engineers and scientists to probe the behaviour of a system to gain understanding of its transient response and limits. However, these models (eg ADM1) are generally complex, requiring numerous parameters to describe the kinetics and transformations, which are often based on assumptions or agglomerated, resulting in a poor fit to real world data.
The purpose of reducing deterministic models to simple components describing a subset of functions in a lower order system, is that these models can be easily defined, adapted and interrogated to provide fundamental knowledge of the behaviour from both an analytical and empirical/numerical perspective.
The models are solved mathematically to determine their transient response to forces in the system, or to find conditions under which equilibrium is observable. The stability of the system around these equilibria reveals sensitivity of the system to perturbations, with implications for real world applications in identifying conditions necessary to maintain process stability or even to improve performance.
The project draws on mathematical, biological and engineering knowledge to address the very specific challenge of working with these type of systems and should result in a robust methodology for scaling to more complex or coupled models that are pertinent for engineers and AD practitioners.