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

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.

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

Chlorine 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.