Unified machine learning dataset for hydrogen yield prediction from microwave-assisted pyrolysis of waste

Microwave‑assisted pyrolysis of waste feedstocks is a promising way to produce hydrogen while handling waste. Getting the highest hydrogen yield is difficult because many factors interact: the type of waste, the microwave settings, the dielectric properties of the material that absorbs the microwaves, and the catalyst used. This project helps you set up and test machine‑learning models using a unified dataset of 241 experimental data points gathered from 16 peer‑reviewed studies. The dataset contains 25 input features grouped into four key categories: – feedstock composition – microwave system parameters – absorber dielectric properties – catalyst specifications With clear research guidance, you can apply algorithms such as random forests, gradient boosting,

and neural networks to predict hydrogen yield. The framework lets you pinpoint the most important process drivers and work toward better microwave‑assisted pyrolysis designs. It also provides step‑by‑step instructions for data cleaning, feature engineering, model training, and validation, enabling a systematic assessment of predictive performance for green chemical‑engineering applications.

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