Efficient cumulative breakage distribution and breakage rate computation with minimal experiment intervention incorporating optimal time determination for fine grinding simulation

Employing an Artificial Neural Network in Correlating a Hydrogen-Selective Catalytic Reduction Performance with Crystallite Sizes of a Biomass-Derived Bimetallic Catalyst

A predictive model correlating the properties of a catalyst with its performance would be beneficial for the development, from biomass waste, of new, carbon-supported and Earth-abundant metal oxide catalysts. In this work, the effects of copper and iron oxide crystallite size on the performance of the catalysts in reducing nitrogen oxides, in terms of nitrogen oxide conversion and nitrogen selectivity, are investigated. The catalysts are prepared via the incipient wetness method over activated carbon, derived from palm kernel shells. The surface morphology and particle size distribution are examined via field emission scanning electron microscopy, while crystallite size is determined using the wide-angle X-ray scattering and small-angle X-ray scattering methods. It is revealed that the copper-to-iron ratio affects the crystal phases and size distribution over the carbon support. Catalytic performance is then tested using a packed-bed reactor to investigate the nitrogen oxide conversion and nitrogen selectivity. Departing from chemical characterization, two predictive equations are developed via an artificial neural network technique—one for the prediction of NOx conversion and another for N2 selectivity. The model is highly applicable for 250–300 °C operating temperatures, while more data are required for a lower temperature range.