Chemical
pathways for converting biomass into fuels produce compounds
for which key physical and chemical property data are unavailable.
We developed an artificial neural network based group contribution
method for estimating cetane and octane numbers that captures the
complex dependence of fuel properties of pure compounds on chemical
structure and is statistically superior to current methods.
A simple, inexpensive catalyst system (Amberlyst 15 and Ni/SiO -Al O ) is described for the upgrading of acetone to a range of chemicals and potential fuels. Stepwise hydrodeoxygenation of the produced ketones can yield branched alcohols, alkenes, and alkanes. An analysis of these products is provided, which demonstrates that this approach can provide a product profile of valuable bioproducts and potential biofuels.
All process designs are subject to uncertainties which make it impossible to state with complete certainty that a design will work. This study presents a measure of design confidence which considers the nature of the uncertainty and the operability of the process as a whole. This measure, called design reliability, quantifies the likelihood that a design will work. Detailed analyses are presented for three special cases: designs subject to random uncertainties, designs subject to fuzzy uncertainties, and designs subject to both random and fuzzy uncertainties. Practical procedures for estimating design reliability in these special cases are also presented.
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