Decision tree regression model to predict low-rank coal moisture content during convective drying process

Pekel E., Akkoyunlu M. C., Akkoyunlu M. T., PUSAT Ş.

INTERNATIONAL JOURNAL OF COAL PREPARATION AND UTILIZATION, cilt.40, sa.8, ss.505-512, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 40 Sayı: 8
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1080/19392699.2020.1737527
  • Dergi Adı: INTERNATIONAL JOURNAL OF COAL PREPARATION AND UTILIZATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Agricultural & Environmental Science Database, Chemical Abstracts Core, Communication Abstracts, Compendex, Environment Index, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.505-512
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Coal is still a significant energy source for the world. Due to the utilization of low-rank coal, drying is a key issue. There are lots of attempts to develop efficient drying processes. The most prominent method seems as thermal drying. For thermal drying processes, the most important subject is the coal moisture content change with time. In this study, convective drying experiments were utilized to develop a new model based on decision tree regression method to predict coal moisture content. The developed model gives satisfactory results in prediction of instant coal moisture content with changing drying conditions. With the decision tree depth of six, the best test results were achieved as 0.056 and 0.802 for MSE and R-2 analyses, respectively.