Estimation of higher heating value of refuse derived fuel from proximate and ultimate analysis

Abstract

Waste production, which is constantly increasing as a result of industrial and technological developments, is becoming an important problem worldwide. Especially with the increase in human population and urbanization, the increase in waste production is inevitable. With the increasing waste production day by day, the resulting wastes must be disposed of in an environmentally friendly way. On the other hand, studies are carried out on obtaining energy from waste in order to reduce dependence on fossil fuels and increase renewable energy production. For this reason, studies are carried out on refuse derived fuel (RDF), which is obtained as a result of physical treatment of municipal solid waste. RDF not only ensures the disposal of waste in an environment friendly way, but also contributes to the production of renewable energy by reducing the dependence on fossil fuels. In addition, the use of fossil fuels in energy production reduces the emission of harmful emissions. RDF contains many different types of waste such as textile, food, rubber, paper, metal, battery, glass. The reason why it is composed of many different components is that there is a wide variety of wastes in municipal solid waste. The compositions in RDF and MSW vary according to factors such as climatic conditions, geographical location and income level. A more homogeneous fuel is obtained by converting municipal solid waste to refuse derived fuel. As a result of obtaining a more homogeneous fuel by converting the wastes to RDF, higher efficiency in energy production is provided. At the same time, RDF has a higher calorific value than MSW. Also, the amount of air required for RDF combustion is less than the amount of air required by MSW. RDF is obtained as a result of various physical processes applied to municipal solid wastes and includes materials such as packaging waste, municipal waste and industrial waste. RDF is a solid fuel with high calorific value and is used as a fuel in many areas such as cement factories and power generation facilities. There are many different methods used to generate energy from RDF. With the thermochemical conversion methods used in obtaining energy from RDF, both waste disposal is ensured and heat or electrical energy is produced. Combustion, pyrolysis, gasification and plasma-based technologies can be given as examples to these thermochemical methods. RDF transforms into solid, liquid and gaseous products through energy production methods. The heating value of the RDF used in energy production is an important factor in terms of the efficiency of the processes. For the design of facilities using RDF, the calorific value must be analyzed correctly. Therefore, within the scope of the thesis, it is aimed to develop correlations that can predict the higher heating value according to the amount of components of the RDF. Proximate and ultimate analysis data were used to determine the components effects on higher heating value and to create a mathematical model. The effects of weight percentage of moisture, ash, volatile matter and fixed carbon on the higher heating value were investigated with the proximate analysis data of 46 different RDF samples obtained as a result of the literature research. According to the results, it was observed that the higher heating values increased with the increase in the amount of volatile matter. On the other hand, the increase in moisture, ash and fixed carbon caused the higher heating values to decrease. In addition, the effects of carbon, hydrogen, oxygen, nitrogen, sulfur and chlorine components on the higher heating value were investigated by using the ultimate analysis data of 74 different RDF samples. It was determined that the higher heating values increased with the increase in the weight percentages of carbon and hydrogen. The increase in the oxygen percentage causes the higher heating value to decrease. By applying the multiple linear regression method to the proximate and ultimate analysis data, equations were obtained to estimate the higher heating value of RDF. In regression calculations, higher heating value in the proximate analysis data as the dependent variable; moisture, ash, volatile matter and fixed carbon were considered as independent variables. In the ultimate analysis data, calculations were made with carbon, hydrogen, oxygen and nitrogen components as independent variables. As a result, three different higher heating value estimation equations were obtained with the multiple linear regression method. The higher heating values were calculated with the obtained equations and compared with the actual higher heating values. The error percentage of the calculated higher heating value according to actual values has been found and it has been determined that the equations can predict the higher heating value with an error of less than 10%.