Simulation of Anaerobic Digestion Plants Using SimBiology at DBFZ
SimBiology Models Provide Adaptability to Meet Evolving Research Needs
“The built-in consistency check in SimBiology is an invaluable feature during model development. It ensures that units align correctly, helping to identify and resolve potential inconsistencies and reducing the risk of errors arising from incorrect unit conversions early on.”
Key Outcomes
- Models developed in SimBiology were more flexible and iterative when it came to estimating parameters and using different data sets, making them more adaptable to evolving research needs
- Transitioning to SimBiology has enabled a more streamlined modeling workflow by helping the DBFZ team resolve previous issues with event handling, which often required debugging and patching models for each new data set
Simulation results of ADM1-R3, solved with ode15s in MATLAB and SimBiology, including pH (upper half) and biogas production rate (lower half).
With the ability to convert organic substrates into biogas, anaerobic digestion (AD) plants have significant potential when it comes to enhancing sustainability and contributing to climate change mitigation. A junior research group at Deutsches Biomasseforschungszentrum (DBFZ), the German Institute for Biomass Research, is working on improving models and monitoring algorithms for AD plants to enhance process knowledge and understanding. The goal of the research team is more efficient and flexible plant operation, which not only reduces the environmental footprint of AD plants but also improves the ability to compensate for fluctuations in electricity supply from other renewable sources.
Initially developed in MATLAB® and Simulink®, simplified AD process models have recently been reimplemented in SimBiology®. This change was motivated by several factors: enhanced performance due to SUNDIALS solvers, built-in functions for handling time-based events in SimBiology, and the capability to estimate parameters for objective functions using data with varying sampling frequencies. The latter feature, in particular, enables rapid iterations to explore diverse scenarios.
MathWorks helped DBFZ researchers familiarize themselves with the parameters, species, equations, and algebraic rules that make up the syntax of SimBiology. As a result, their SimBiology models can now be more effectively refined, while also being more accurate, robust, and adaptable to evolving research needs. Model execution is faster compared to standard procedures implemented in MATLAB, enhancing parameter estimation, uncertainty quantification, and scenarios that demand frequent model evaluations. Additionally, the previous challenges with event handling, which often necessitated debugging and patching models for each new data set, have been resolved.
Furthermore, additional toolboxes, such as Statistics and Machine Learning Toolbox™ or Symbolic Math Toolbox™, are also frequently used for model development and process simulation within the junior research group at DBFZ.
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