Implementation of an early warning system with hyperspectral imaging combined with deep learning model for chlorine in refuse derived fuels

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

Waste Management, cilt.142, ss.111-119, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 142
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.wasman.2022.02.010
  • Dergi Adı: Waste Management
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, EMBASE, Environment Index, Geobase, INSPEC, MEDLINE, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.111-119
  • Anahtar Kelimeler: Chlorine content, Deep learning, Early warning, Hyperspectral Imaging, Refuse derived fuel (RDF), Validation, NEAR-INFRARED SPECTROSCOPY, FOOD WASTE, PYROLYSIS, BEHAVIOR, QUALITY
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

© 2022 Elsevier LtdChlorine content is one of the most important parameters in Refuse Derived Fuels (RDFs) used as a fuel in cement kilns. The main problem with the use of RDF is that chlorine in the waste weakens the cement, increases the risk of corrosion in the kiln and forms toxic gas emissions. Alternative fuels containing high amounts of chlorine, such as plastic waste should be used in limited quantities with the quality of the kiln used and the cement being should be preserved by preparing the appropriate RDF mixture. Analyses conducted on the samples taken before the RDF is given to the furnace are time consuming and costly. Therefore, in this study, the aim is to present a more efficient solution to classify by using chlorine analysis results with hyperspectral imaging and a deep learning model study. For this purpose, a model was created using validated laboratory results and spectral data from samples, the model was tested on a prototype conveyor belt, and was implemented using an online early warning system for high chlorine concentrations. The chlorine content of the RDF samples used in the study ranged from 0.10% to 1.41%, with an average of 0.27%. According to the results, the accuracy, precision, Recall and F1 Score related to the early warning system were found to be 0.8909, 0.8889, 0.8889, 0.8889, respectively. In addition, chlorine measurements were performed at 200, 500 and 1000 mm/s belt speeds and accuracy values of 78.39%, 76.35% and 69.94 %, respectively were obtained.