Investigation of the dehydration of ulexite ore with different parameters and modeling with artificial neural network (ANN) method


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Kocadağistan M. E.

TURKISH JOURNAL OF CHEMISTRY, cilt.47, sa.1, ss.218-231, 2023 (SCI-Expanded) identifier identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 47 Sayı: 1
  • Basım Tarihi: 2023
  • Doi Numarası: 10.55730/1300-0527.3531
  • Dergi Adı: TURKISH JOURNAL OF CHEMISTRY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Chemical Abstracts Core, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.218-231
  • Atatürk Üniversitesi Adresli: Evet

Özet

Study experiments were conducted considering the temperature, time, and sample weight parameters in order to model 

the dehydration by applying dehydration processes to ulexite ores. Data obtained from the dehydration processes of ulexite ore were 

compared with TG analyzes. It was observed as the result of the heat treatment that the fastest water removal was provided in the 

temperature range of 150–250 °C and it was very low in the range of 400–750 °C. In order to design the ANN method, 4 different models 

were proposed with the same parameters in the dehydration experiments and the network structure was determined. The performance 

of the ANN model was assessed by means of error measurements i.e. absolute error (AE), absolute relative error (ARE) and coefficient 

of determination (R2). The mean value of R2 was 99%. It was found that the independent variables explained the dependent variable 

efficiently and the models were very successful. It was shown that the new models can be created using genetic algorithms or hybrid 

methods in the future studies requiring fewer experiments by following the same process in the present study.