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Titlebook: Handbook on Characterization of Biomass, Biowaste and Related By-products; Ange Nzihou Book 2020 Springer Nature Switzerland AG 2020 Bioma

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Syngas,. Bio-syngas can be produced from different biomass feedstock (e.g. lignocellulosic biomass, pyrolysis oil, bioethanol, biogas) through thermal and/or catalytic reforming and gasification processes. The typical components of raw syngas can be classified into three groups: non-condensable gases (e.g.
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Condensable and Liquid Compounds from Biomass and Waste Thermal Degradation,umber of species, mainly organic, but also inorganic. These species may vary according to biomass or waste type, and even more according to process operating conditions, namely temperature, heating rate, reactive gas or pressure. For instance, liquids obtained during torrefaction and their related y
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Dioxins and Dioxin-like Compounds,nvironment, resistant towards metabolism, hydrophobic and lipophilic, and hence bioaccumulating in the fatty tissues of animals and humans. Traditionally, waste incineration and iron ore sintering were the largest sources of dioxins. Biomass burning, especially uncontrolled burning, is becoming incr
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Particulate Matter,tion with a size ranging from 2.5 to 10 µm (PM.–PM.), and the fine fraction with a size smaller than 2.5 µm (PM.). PM can be generated from various sources such as unpaved roads, vehicles, agricultural processes, uncovered soil, mining operations, as well as burning of fuels. Statistical data show t
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,Solid Residues (Biochar, Bottom Ash, Fly Ash, …), materials. These solids are carbon-based or mineral-based materials, and for their suitable use in a number of fields, various properties should be determined. This chapter addresses advanced techniques used to determine physical and chemical properties of these solid residues. For each technique,
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values in the data based on other available entries. Imputation methods have an influence on the learning performance on incomplete data. Therefore, the choice of a right imputation method has an important role when constructing prediction models. It is common to use one imputation method to impute
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