Prediction of thermal conversion characteristics for co-combustion of waste tire-lignite coal using machine learning algorithms

Soyler N., Şahin D. Ö., Demirci S., Ceylan S.

CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 2025 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1002/cjce.25746
  • Dergi Adı: CANADIAN JOURNAL OF CHEMICAL ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Aqualine, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Pollution Abstracts, Civil Engineering Abstracts
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

Co-combustion of coal with various waste resources is an effective energy recovery strategy that integrates waste-derived fuels while reducing dependence on fossil fuels. In this study, machine learning algorithms were used to predict thermogravimetric data for the co-combustion process of waste tire (WT) and lignite coal (LC) blends to improve the understanding of the thermal conversion characteristics. The study analyzed the combustion behaviour of WT, LC, and their mixtures at four different heating rates (10, 20, 30, and 40 degrees C/min) and various mixing ratios (100:0, 20:80, 40:60, 50:50, 60:40, 80:20, and 0:100) using thermogravimetry-derivative thermogravimetry/differential scanning calorimetry (TG-DTG/DSC) techniques. To improve the prediction accuracy, eight machine learning algorithms-adaptive boosting regression, decision tree regression, k-nearest neighbour regression, linear regression, multi-layer perceptron, random forest regression, support vector machine regression, and XGBoost-were applied to model the co-combustion process. The results showed a strong correlation between experimental data and machine learning predictions, confirming the effectiveness of these models. By enabling accurate real-time prediction of thermal conversion characteristics, this study reduces the reliance on labour-intensive thermogravimetric analysis (TGA) and facilitates cost-effective, adaptive, and scalable optimization of combustion processes for industrial applications.