This thesis deals with a project of steam boiler to combustion of refused-derived fuel. With respect of required fuel and output parameters of the steam was worked out stoichiometry, energy loss and boiler efficiency, heat flows was allocate to individual heat exchange surfaces. The heat exchange surfaces was calculate and project in detail.

This degree project focused on examining new possible application of near-infrared (NIR) spectroscopy for quantitative and qualitative characterization of refuse derived fuel (RDF). Particularly, two possible applications were examined as part of the project. Firstly, use of NIR hyperspectral imaging for classification of common materials present in RDF. The classification was studied on artificial mixtures of materials commonly present in municipal solid waste and RDF. Data from hyperspectral camera was used as an input for machine learning models to train them, validate them, and test them. Three classification machine learning models were used in the project; partial least-square discriminant analysis (PLS-DA), support vector machine (SVM), and radial basis neural network (RBNN). Best results for classifying the materials into 11 distinct classes were reached for SVM (accuracy 94%), even though its high computational cost makes it not very suitable for real-time deployment. Second best result was reached for RBNN (91%) and the lowest accuracy was recorded for PLS-DA model (88%). On the other hand, the PLS-DA model was the fastest, being 10 times faster than the RBNN and 100 times faster than the SVM. NIR spectroscopy was concluded as a suitable method for identification of most common materials in RDF mix, except for incombustible materials like glass, metals, or ceramics. The second part of the project uncovered a potential in using NIR spectroscopy for identification of inorganic chlorine content in RDF. Experiments were performed on samples of textile impregnated with a water solution of kitchen salt representing NaCl as inorganic chlorine source. Results showed that contents of 0.2-1 wt.% of salt can be identified in absorbance spectra of the samples. Limitation appeared to be water content of the examined samples, as with too large amount of water in the sample, the influence of salt on NIR absorbance spectrum of water was too small to be recognized. / FUDIPO

This work includes two unrelated areas of research. The first portion of this work involved combusting densified refuse derived fuel (dRDF) with coal and studying the effect that Ca(0H)2 binder had on reducing polycyclic aromatic hydrocarbon (PAH) and polychlorinated biphenyl (PCB) emissions. The second area of work was directed at developing nondestructive infrared techniques in order to aid in the analysis of postage stamp adhesives. With Americans generating 150-200 million tons a year of Municipal Solid Waste (MSW) and disposing of nearly ninety percent of it in landfills, it is easy to understand why American landfills are approaching capacity. One alternative to landfilling is to process the MSW into RDF. There are technical and environmental problems associated with RDF. This work provides some answers concerning the amount of PAH and PCB emissions generated via the combustion of RDF with coal. It was found that the Ca(OH)2 binder greatly reduced both the PAH and the PCB emissions. In fact, PAH emissions at the ten-percent level were reduced more by using the binder than by the pollution control equipment. If the Ca(0H)2 binder can reduce not only PAH and PCB emissions, but also other noxious emissions, such as acid gases or dioxin, RDF technology could soon be the answer to the current landfill problems. The second portion of this work focused on developing a method to analyze stamp adhesives nondestructively. Using this method, it was fairly easy to differentiate among the three different types of adhesives that have been used by the United States Postal Service: gum arabic, dextrin, and polyvinyl alcohol. Differences caused by changes in chemicals added to the adhesives were also detected. Also, forgeries were detected with as much success, if not more, than by conventional methods. This work also led to the construction of equipment that allows large samples to be analyzed by reflectance infrared methods.