Smart Combustion of Textile Sludge with ML Models

What is it about?

Textile factories produce a lot of sludge—a messy mix of leftover dyes, chemicals, and organic waste. Disposing of this sludge is a major environmental challenge. Instead of dumping it, this study explores how we can burn it to recover energy. Using advanced thermal testing and smart computer models (like artificial intelligence), the research shows how textile sludge behaves when heated. It identifies the energy needed to start combustion and how different components break down. The study also uses machine learning to predict these behaviors more accurately. This approach could help design better systems to turn textile waste into usable energy, reducing pollution and supporting cleaner industrial practices.

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Photo by Sergio Gonzalez on Unsplash

Photo by Sergio Gonzalez on Unsplash

Why is it important?

This research is the first to combine detailed thermal analysis with multiple machine learning models—ANN, C&RT, BRT, and MARS—to predict how textile sludge burns. While past studies focused on basic combustion or co-combustion, this work dives deeper into combustion behavior across varied heating rates, offering a more precise understanding of its energy potential. With growing global pressure to manage industrial waste sustainably, textile sludge remains an underutilized resource. This study shows how AI-driven modeling can optimize waste-to-energy conversion—supporting cleaner industry practices and circular economy goals. By revealing how textile sludge can be efficiently combusted and modeled, this work could guide the design of smarter industrial combustors, inform waste management policies, and inspire future research in AI-powered sustainability. It’s a leap toward turning waste into a valuable energy source.