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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 R 2 values between 0.979, 0.983, and 0.952., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In this study, pyrolysis of low-density polyethylene (LDPE) and LDPE with aluminum (C/LDPE) wastes was carried out with different heating rates (5, 10, 20 °C/min) at different temperatures (400, 600, 800 °C). Product yields of LDPE and C/LDPE wastes were compared, and optimum liquid products were analyzed to utilize as commercial waxes for future use. The properties of pyrolyzed wastes was investigated with proximate, elemental analysis, and TGA. The as-produced liquid from pyrolysis of wastes was characterized by different characteristic tools, such as elemental analyses, GC-MS analyses, 1H-NMR tests, FT-IR spectra, the density, melting point, and carbon residue to compare commercial waxes. As a result of pyrolysis, the highest liquid product yield was achieved at 800 °C with 5 °C/min heating rate (85.87%) and at 600 °C with 5 °C/min heating rate (71.3%) for LDPE and C/LDPE, respectively. The results indicated that the derived liquid products are similar to commercial heavy wax., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

An energy audit was realized for a building group located on a university campus to measure the environmental sustainability and efficient usage of natural resources. As a result of energy audit, exterior insulation and double-glazing application were came to the front for energy-saving options. Although energy audit provides energy-saving options as output, it is not enough to provide information about how environmental impacts will change if the defined options are used. To determine the improvements in terms of environmental indicators, these options were assessed with life cycle assessment (LCA). LCA was realized for 50 years life span for 1 m 3 of the building. CML-IA method was used to conduct LCA analyses. Ecovalue08 was applied as a monetary weighting method since the used CML-IA method has no weighting function. Results show that there are significant improvements (> 5%) on ADP ff (11-12.5%) and GWP100 (8.5-9.7%) impacts provided by both of the energy-saving options. Additionally, double glazing would provide a 10.5% improvement on ODP. On the other hand, the exterior insulation application would increase the ODP value of EB by 34%. The impact category of GWP100 is found as the most dominant impact according to Ecovalue08. The ranking of the other impact categories from higher to lower value is HTP, ADP ff , and AP.

Municipal solid waste (MSW) landfill leachate is a highly contaminated liquid effluent. Leachate has a complex nature that needs to be appropriately treated before being discharged into the environment. There are various options for leachate treatment. Deciding which option should be applied is a complex process, since it depends on many factors that need to provide a balance between the technical, economic, and environmental aspects of sustainability. Multi-criteria decision-making (MCDM) methods are useful techniques to solve complex problems that cannot be easily solved. In this study, MCDM techniques are used for an evaluation of four different leachate treatment options: recirculation of leachate to a landfill site (A1), combined treatment with municipal wastewater (A2), anaerobic and aerobic sequential treatment (A3), and advanced leachate treatment based on membrane processes (A4). The selection of the most appropriate one, based on the criteria, analytic network process (ANP), and preference ranking organization method for enrichment evaluations (PROMETHEE) methods, was applied as MCDM techniques using the Super Decisions software and D-Sight software, respectively. Both the ANP and the PROMETHEE analysis results demonstrate that option A2 is the most appropriate for all of the decision-makers.