Prediction of the proximate analysis parameters of refuse-derived fuel based on deep learning approach

Günkaya Z., Özkan M., Özkan K., Bekgöz B. O., Yorulmaz Ö., Özkan A., ...Daha Fazla

Environmental Science and Pollution Research, cilt.30, sa.7, ss.17327-17341, 2023 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 30 Sayı: 7
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s11356-022-23272-6
  • Dergi Adı: Environmental Science and Pollution Research
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, ABI/INFORM, Aerospace Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, EMBASE, Environment Index, Geobase, MEDLINE, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.17327-17341
  • Anahtar Kelimeler: Ash content, Deep learning, Moisture content, Refuse-derived fuel (RDF), Volatile matter content, INDUCED BREAKDOWN SPECTROSCOPY, HIGHER HEATING VALUE, INFRARED-SPECTROSCOPY, MOISTURE-CONTENT, VOLATILE MATTER, COAL PROPERTIES, RAPID ANALYSIS, FIXED CARBON, ASH CONTENT, QUALITY
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Determination of proximate characteristics can be achieved using conventional analyses methods that require a certain amount of time. In cement factories, refuse-derived fuel (RDF) is continuously fed to a kiln by a conveyor belt, so even if an inappropriate proximate characteristic is determined, it would be too late to prevent the feeding of RDF to the kiln. To overcome this problem, there is a need for instant measurement of the proximate characteristics (moisture, volatile matter, ash) that enables the feeding to be stopped. In such cases, the deep learning (DL) is a useful method based on the prediction of proximate characteristics. Therefore, in this study, the aim is to estimate the mentioned parameters developed by near-infrared spectroscopy (NIR) combined with deep learning models. For this purpose, the spectrographic measurements taken from RDF samples with an NIR spectrometer, and the results of proximate analysis in a laboratory, were used together as a dataset. A fully convolutional neural network (FCNN) and ResNet were used as a network, and they were trained using images of RDF samples and proximate analysis values. The FCNN model was more successful in prediction studies. According to the FCNN model, the results show that the models in the study can predict the moisture, ash, and volatile matter content of RDF with satisfactory R2 values between 0.979, 0.983, and 0.952.