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Incinerators do not achieve a complete mineralization of organic constituents of municipal solid waste. The solid residues (bottom ash, boiler ash and air pollution control residues) contain carbon which can be determined as total organic carbon (TOC). This work focuses on the TOC composition and its significance to the genesis and diagenesis of the solid residues. An analytical procedure is presented to characterize carbon species by different chemical and microscopic analytical methods. The procedure is based on two steps. In the first step a quantitative classification of TOC into four different carbon species (elemental carbon, water extractable organic carbon, dichloromethane extractable organic carbon and non extractable organic carbon) is performed to obtain a first survey of the TOC composition. Based on this survey a further characterization of individual carbon species is performed. The results of the qualitative and quantitative characterization of carbon species allow to postulate hypotheses on the influence of organic carbon on the long-term behavior of the solid residues.

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An approximating neural model, called hierarchical radial basis function (HRBF) network, is presented here. This is a self-organizing (by growing) multiscale version of a radial basis function (RBF) network. It is constituted of hierarchical layers, each containing a Gaussian grid at a decreasing scale. The grids are not completely filled, but units are inserted only where the local error is over threshold. This guarantees a uniform residual error and the allocation of more units with smaller scales where the data contain higher frequencies. Only local operations, which do not require any iteration on the data, are required; this allows to construct the network in quasi-real time. Through harmonic analysis, it is demonstrated that, although a HRBF cannot be reduced to a traditional wavelet-based multiresolution analysis (MRA), it does employ Riesz bases and enjoys asymptotic approximation properties for a very large class of functions. HRBF networks have been extensively applied to the reconstruction of three-dimensional (3-D) models from noisy range data. The results illustrate their power in denoising the original data, obtaining an effective multiscale reconstruction of better quality than that obtained by MRA.