How Much NOx Is the Gas Turbine Emitting?

From the weather and the turbine's operating readings, how much nitrogen oxide is the plant putting out? A good estimate backs up the emissions analyser and helps tune the turbine to stay within limits.

Use caseEnergyPredictionModel evaluation

The table

Five years of hourly readings from a gas turbine power plant, with ambient conditions, turbine operating values and measured NOx. It's in Welt's research catalogue as Gas Turbine CO and NOx Emissions (2011-2015), 36,733 rows by 12 columns, licensed CC BY 4.0. The catalogue measures its signal at 0.83. At 36,733 rows and 11 features the table is larger than the model limits allow, so the walkthrough evaluates and fits on a seeded sample of 8,000 rows. TabICLv2 and Kumo Tabular Medium compete.

Walkthrough

Load it from the catalogue

Find the table, read its recorded target and print the column names.

import pandas as pd
from welt import Client

with Client() as client:
    item = client.research_datasets(q="gas turbine CO NOx emissions", limit=1)["items"][0]
    info = client.research_dataset(item["id"])
    data = client.load_research_dataset(item["id"])

target = info["targets"][0]
target = target["name"] if isinstance(target, dict) else target
print([column["name"] for column in info["schema"]], target)

Pick the rows
Set aside 20 rows to predict at the end. The models learn from a seeded sample of 8,000 of the rest, the most this table's width allows.
```
known = data.dropna(subset=[target])
new_rows = known.sample(20, random_state=1)
table = known.drop(new_rows.index).sample(8000, random_state=7)
```

Compare the models and predict

The models compete on the same folds against a simple guess, then the top-ranked one is fitted on the whole table. RMSE and MAE are average errors in the target's own units, so lower is better. The last table puts each prediction next to the real answer.

with Client() as client:
    evaluation = client.evaluate(table, target, task="regression", seed=7,
                                 models=["tabicl-v2", "kumo-medium"])
    model = evaluation.fit()

print(evaluation.summary()[["name", "rank", "rmse_mean", "mae_mean"]])
features = new_rows.drop(columns=target)
print(pd.DataFrame({"actual": new_rows[target].values, "predicted": model.predict(features)}))

What you get

An emissions estimateNOx predicted from routine readings.

A check on the analyserSpot hours when the measured value looks wrong.

Settings to trySee how predicted emissions change with operating conditions.

Try it

Use Open with to run the research datasets notebook in Colab, Kaggle or Jupyter and search for "gas turbine CO NOx emissions", or run the code above after pip install "welt-client[parquet]".

Open with

Read the docs

Type: Use case
Area: Energy
Question: How Much NOx Is the Gas Turbine Emitting?
Model: TabICLv2, Kumo Tabular Medium
Data: Gas Turbine CO and NOx Emissions (2011-2015), 36,733 rows
For: Power plant engineers and environmental managers
Licence: CC BY 4.0