Benchmark Dataset¶
The benchmark dataset is a collection of described biogas plants. It is used to test how well an artificial intelligence (AI) can rebuild a working PyADM1ODE model from the description of a plant.
In a nutshell
An AI reads the description of a biogas plant – as text or as a sketch – and is supposed to rebuild that same plant in the computer. The benchmark measures how accurately it succeeds.
What is this about?¶
PyADM1ODE is software that can simulate biogas plants. To make the software compute a specific plant, you first have to describe that plant: How many vessels are there? How big are they? How are they connected? Is there a combined heat and power unit?
Exactly this step – from a description in words to a finished plant in the software – is the task the benchmark evaluates. The AI receives a description and produces the plant from it. Its result is then compared against a known reference solution.
flowchart LR
A["📝 Plant<br>description"] --> B["🤖 AI rebuilds<br>the plant"]
B --> C["⚖️ Compare with<br>the reference solution"]
C --> D["📊 Score<br>in percent"]
Why is this useful?¶
- Comparability: Different AI models solve the same tasks – you can objectively
see which one performs better. - Realism: The plants are based on real agricultural biogas plants.
- Understanding: The dataset vividly shows what a biogas plant consists of and
what matters when modelling one.
Who is this documentation for?¶
This introduction is aimed at readers without programming experience. No coding knowledge is required – technical terms are explained along the way.
Where to go next¶
-
Dataset Structure
Which plants exist, and how are the tasks organised?
-
A Data Point in Detail
What exactly is contained in a single task?
-
How does the Oracle work?
How the AI may ask about missing values – and how the oracle answers.
-
Scoring & Workflow
How does a test run, and how is the score produced?