CuFeAl Nanocomposite Catalysts for Coal Combustion in Fluidized Bed.

A method of oil-drop granulation was suggested for the preparation of spherical CuFeAl nanocomposite catalysts. The catalysts were characterized by a set of physicochemical methods (X-ray diffraction, temperature-programmed reduction by H2, low-temperature nitrogen adsorption, crushing strength) and tested in the oxidation of CO and burning of brown coal in a fluidized bed. It was found that the catalysts have high mechanical strength (16.2 MPa), and their catalytic properties in the oxidation of CO are comparable to the characteristics of industrial Cr-containing catalysts. It was shown that the addition of pseudoboehmite at the stage of drop formation contributes to the production of uniform spherical high-strength granules and facilitates the stabilization of the phase state of the active component. The use of CuFeAl nanocomposite catalysts for the burning of brown coal provides a low emission of CO (600 ppm) and NOx (220 ppm) and a high degree of coal burnout (95%), which are close to those of the industrial Cr-containing catalysts (emission of CO is 700 ppm, NOx-230 ppm, and degree of coal burnout is 95%).

Crystal Structure Representation for Neural Networks using Topological Approach.

In the present work we describe a new approach, which uses topology of crystals for physicochemical properties prediction using artificial neural networks (ANN). The topologies of 268 crystal structures were determined using ToposPro software. Quotient graphs were used to identify topological centers and their neighbors. The topological approach was illustrated by training ANN to predict molar heat capacity, standard molar entropy and lattice energy of 268 crystals with different compositions and structures (metals, inorganic salts, oxides, etc.). ANN was trained using Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Mean absolute percentage error of predicted properties was ≤8 %.