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.

Application of Multi-Criteria Decision analysis including sustainable indicators for prioritization of public transport system.

Paratransit modes dominate the public transportation industry in the majority of developing countries, especially those in Sub-Saharan Africa. The number of these modes has increased rapidly over the years. Authorities have not yet been able to provide a better alternative for passengers, whose demand is ever-growing. In this research, the main modes of public transport in a Sub-Saharan African city, Kampala, have been studied. Here we used multi-criteria decision-making methods to find out which among the four major public transport modes in Kampala, Uganda, are best from a transportation operator's perspective. The evaluation of alternatives was conducted using Analytic Network Process (ANP) and Elimination Et Choice Translating Reality (ELECTRE III) methods. Both the ANP and the ELECTRE III analysis results demonstrate that alternative "Coaster" is the most appropriate alternative from a public transport operator's point of view. A sensitivity analysis consisting of four different scenarios was considered in the changing of weighting percentages (for benefit, cost, and risk clusters) in the ANP and the ELECTRE III analyses, to show the robustness of the ranking results. According to the sensitivity analysis results, there was no change in the final ranking. The findings of this paper are crucial to policymakers within the transportation sectors of developing countries. Integr Environ Assess Manag 2022;18:25-38. © 2021 SETAC.

Utilization of liquid product through pyrolysis of LDPE and C/LDPE as commercial wax.

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.

The effect of energy-saving options on environmental performance of a building: a combination of energy audit-life cycle assessment for a university building.

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 m3 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 ADPff (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, ADPff, and AP.

Decision-making for the selection of different leachate treatment/management methods: the ANP and PROMETHEE approaches.

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.

A heating value estimation of refuse derived fuel using the genetic programming model.

Refuse Derived Fuel (RDF) makes an increasingly important contribution to sustainable waste management as an energy source in cement kilns. The most important parameter of RDF in an evaluation of its performance as a fuel is Higher Heating Value (HHV). The two methods of HHV determination are the direct method and the indirect method. The direct method requires the use of a calorimetric bomb and the indirect method requires ultimate or proximate analysis. As in the direct method, the ultimate analysis based indirect method requires the use of specific equipment and a skilled analyst. Most cement plants do not have special equipment. From this point of view, this study aims to predict the HHVs of RDF samples using the results of proximate analysis. Two Genetic Programming (GP) Models, namely GP Model #1 and GP Model #2 are used for the prediction. GP Model #1 denotes a modest nonlinear mapping function used for the prediction of HHVs, whereas GP Model #2 is a more inclusive nonlinear correlation analysis model as an improved version of GP Model #1. To assess the developed models, the test data is simulated and statistical results to the estimation of HHVs are reported as R2 equal to 0.9951 and 0.9988, Root Mean Square Error (RMSE) equal to 1.4126 and 0.6971 and Average Absolute Error (AAE) equal to 0.0543 and 0.0251, for GP Model #1 and GP Model #2, respectively. It can be seen that GP Model #2 may be confidently used for HHV estimation.

Food loss and waste management in Turkey.

Food waste can be an environmental and economic problem if not managed properly but it can meet various demands of a country if it is considered as a resource. The purpose of this report is to review the existing state of the field in Turkey and identify the potential of food waste as a resource. Food loss and waste (FLW) was examined throughout the food supply chain (FSC) and quantified using the FAO model. Edible FLW was estimated to be approximately 26milliontons/year. The amount of biodegradable waste was estimated based on waste statistics and research conducted on household food waste in Turkey. The total amount of biodegradable waste was found to be approximately 20milliontons/year, where more than 8.6milliontons/year of this waste is FLW from distribution and consumption in the FSC. Options for the end-of-life management of biodegradable wastes are also discussed in this review article.

Pyrolysis of Plants After Phytoremediation of Contaminated Soil with Lead, Cadmium and Zinc.

The aim of this study was to remediate lead (Pb), cadmium (Cd) and zinc (Zn) from contaminated soil and stabilize to pyrolysis solid product. To accomplish this, phytoremediation of soil contaminated with Pb, Cd and Zn by different plants (sunflower, corn and rape) was performed with and without ethylenediaminetetraacetic acid (EDTA). According to phytoremediation results, rape was the most effective plant with 72 %, 76 % and 77 % removal efficiency for Pb, Cd and Zn, respectively. Also, EDTA addition had no significant effect on translocation of the metals from roots to stems. According to pyrolysis results, Pb, Cd and Zn in the contaminated plants were stabilized in the ash/char fraction. In addition, the solid product can be safely landfilled as inert waste since its toxicity leaching value is lower than the limit values given in the Turkish Regulation on Landfilling of Wastes.

Life cycle assessment of solid waste management options for Eskisehir, Turkey.

Life cycle assessment (LCA) methodology was used to determine the optimum municipal solid waste (MSW) management strategy for Eskisehir city. Eskisehir is one of the developing cities of Turkey where a total of approximately 750tons/day of waste is generated. An effective MSW management system is needed in this city since the generated MSW is dumped in an unregulated dumping site that has no liner, no biogas capture, etc. Therefore, five different scenarios were developed as alternatives to the current waste management system. Collection and transportation of waste, a material recovery facility (MRF), recycling, composting, incineration and landfilling processes were considered in these scenarios. SimaPro7 libraries were used to obtain background data for the life cycle inventory. One ton of municipal solid waste of Eskisehir was selected as the functional unit. The alternative scenarios were compared through the CML 2000 method and these comparisons were carried out from the abiotic depletion, global warming, human toxicity, acidification, eutrophication and photochemical ozone depletion points of view. According to the comparisons and sensitivity analysis, composting scenario, S3, is the more environmentally preferable alternative. In this study waste management alternatives were investigated only on an environmental point of view. For that reason, it might be supported with other decision-making tools that consider the economic and social effects of solid waste management.

Characterization of an urban landfill soil by using physicochemical analysis and solid phase microextraction (SPME)-GC/MS.

We have aimed at characterizing top soil samples taken in-situ from five different locations of the unregulated dumping site in Eskisehir/Turkey for a period of six months. The study is the first attempt in the city and in Turkey, regarding particularly the SPME (Solid Phase Microextraction Technique) analysis method utilized. A comprehensive research has been conducted to produce critical soil data to be used for indicating current risks as well as the urgency of rehabilitating the site and establishing a sanitary landfill in the site. Conventional physicochemical analytical methods and SPME technique were used to analyze the samples. Physicochemical analyses were performed for determining the pH, total dried matter, volatile matter, total nitrogen, phosphorus, macro elements and heavy metals. Meteorological data were also recorded for the same period. SPSS.10.0 statistical program was used to determine the correlation between meteorological data and physicochemical analysis results. Mean values were used in the correlation analyses. These data indicated that the air temperature and precipitation have significant effects on soil characteristics. SPME, coupled with GC/MS, was used to identify eighty six volatile and semi-volatile organic compounds contained in soil samples. The samples were extracted by headspace SPME with heating (DeltaHS-SPME). SPME analyses were conducted using a commercially available polydimethylsiloxane/divinylbenzene (PDMS/DVB) fiber having a film thickness of 65 microm (Supelco) as a capture medium. The experimentally optimized headspace sampling conditions were arranged (15 min. at 50 degrees C) before a 30 min. sampling period.

Characterization of the leachate in an urban landfill by physicochemical analysis and solid phase microextraction-GC/MS.

The aim of this study is to evaluate extensively the characterization and identification of major pollutant parameters by paying attention to the organic chemical pollution for unregulated dumping site leachate in Eskisehir/Turkey. The study that is first and only one research has been very important data related with before new sanitary landfill site in Eskisehir city. For this purpose, in this study leachate samples were collected in-situ at monthly interval for a period of 8 months. Firstly, thirty three physicochemical parameters were monitored. Secondly, SPME technique was used for identification of organic pollutants. Meteorological data were also recorded for the same sampling period to correlate meteorological data and physicochemical parameters. Mean values are used in the correlation analysis. Correlation is shown only for the relationship between air temperature and NO(3) (-). No correlation has been found between rain and leachate quality parameters since the amount of rain was very low during the sampling period. However, analysis results were generally decreased in winter season when each parameter and each sampling point are examined separately. According to correlation between every parameter, especially solid content and dissolved oxygen concentration of leachate is affecting to other parameters. Also, sodium and potassium are changing proportionally with same parameters (suspended solids, fixed solids, dissolved oxygen) and high correlation between chloride and heavy metal concentration is showing. The results were statistically evaluated by use of SPSS 10.0 program. Second part of the study, the leachate was extracted by Solid Phase Microextraction (SPME) technique and then analyzed. Of the methodologies tested in this study, the best one selected was based on 100 micro m polydimethylsiloxane coated fiber (PDMS), headspace with heating (Delta HS) sampling mode and an extraction time of 15 min. at a temperature of 50 degrees C. Thirty three organic compounds in leachate were identified by GC/MS.