---
license: mit
tags:
  - sklearn
  - tabular-regression
  - distillation
  - chemical-engineering
---

# Pyrolysis Distillation Predictor

Predicts NAPTHA and DIESEL purity from distillation column operating conditions.

## Inputs
| Feature | Description |
|---|---|
| Distillate_To_Feed_Ratio | Ratio of distillate to feed flow |
| Feed_Stage | Feed stage number |
| top_stage_pressure_(bar) | Top stage pressure |
| Temp_of_Field_(C) | Field temperature |
| Feed_Flow_Rate_(Kg/hr) | Feed flow rate |

## Outputs
- `NAPTHA`: predicted purity (0–1)
- `DIESEL`: predicted purity (0–1)

## Feasible Operating Zone
Both outputs ≥ 80% when Distillate_To_Feed_Ratio is between 0.20 and 0.44.

## Usage
```python
import joblib
import numpy as np
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="lastcode/pyrolysis-distillation-predictor",
    filename="pyrolysis_model.joblib"
)
model = joblib.load(model_path)

# [Distillate_To_Feed_Ratio, Feed_Stage, top_stage_pressure, Temp, Feed_Flow_Rate]
X = np.array([[0.35, 10, 2.5, 150, 1000]])
pred = model.predict(X)
print(f"NAPTHA: {pred[0][0]:.3f}")
print(f"DIESEL: {pred[0][1]:.3f}")
```