Using ML to Predict Energy Recovery from Textile Waste through Pyrolysis

What is it about?

Textile sludge is a toxic by-product from fabric dyeing and printing. It is usually dumped in landfills, harming the environment. This study explores a cleaner solution: using heat (a process called pyrolysis) to break down the sludge and recover useful energy products like biochar, gas, and oil. To make this process more efficient, we have used machine learning models to predict how the sludge behaves when heated. The findings show that textile sludge can be transformed into valuable energy sources, offering a sustainable way to manage industrial waste.

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Photo by Francois Le Nguyen on Unsplash

Photo by Francois Le Nguyen on Unsplash

Why is it important?

This study is one of the first to apply advanced machine learning models—like neural networks and decision trees—to predict how toxic textile sludge breaks down during pyrolysis. While previous research focused on sewage sludge or used simpler models, this work dives deeper into the complex behavior of industrial textile waste. By combining thermogravimetric analysis with AI, the study achieves highly accurate predictions of energy recovery potential, especially activation energy values. This approach not only advances scientific understanding but also supports cleaner industrial practices and circular economy goals.