CPFD modeling of hydrodynamics,combustion and NOx emissions in an industrial CFB boiler

The ultra-low NOx emission requirement (50 mg/m³) brings great challenge to CFB boilers in China. To further tap the NOx abatement potential, full understanding the fundamentals behind CFB boilers is needed. To achieve this, a comprehensive CPFD model is established and verified; gas-solid flow, combustion, and NOx emission behavior in an industrial CFB boiler are elaborated; influences of primary air volume and coal particle size on furnace performance are evaluated. Simulation results indicate that there exists a typical core-annular flow structure in the boiler furnace. Furnace temperature is highest in the bottom dense-phase zone (about 950 °C) and decreases gradually along the furnace height. Oxygen-deficient combustion results in high CO concentration and strong reducing atmosphere in the lower furnace. NOx concentration gradually increases in the bottom furnace, reaches maximum at the elevation of secondary air inlet, and then decreases slightly in the upper furnace. Appropriate decreasing the primary air volume and coal particle size would increase the CO concentration and intensify the in-furnace reducing atmosphere, which favors for NOx reduction and low NOx emission from CFB boilers.     

Bayesian Optimization-guided Discovery of High-performance Methane Combustion Catalysts based on Multi-component PtPd@CeZrOx Core–Shell Nanospheres

Formula regulation of multi-component catalysts by manual search is undoubtedly a time-consuming task, which has severely impeded the development efficiency of high-performance catalysts. In this work, PtPd@CeZrO_x core–shell nanospheres, as a successful case study, is explicitly demonstrated how Bayesian optimization (BO) accelerates the discovery of methane combustion catalysts with the optimal formula ratio (the Pt/Pd mole ratio ranges from 1/2.33–1/9.09, and Ce/Zr from 1/0.22–1/0.35), which directly results in a lower conversion temperature (T₅₀ approaching to 330 °C) than ones reported hitherto. Consequently, the best sample obtained could be efficiently developed after two rounds of iterations, containing only 18 experiments in all that is far less than the common human workload via the traditional trial-and-error search for optimal compositions. Further, this BO-based machine learning strategy can be straightforward extended to serve the autonomous discovery in multi-component material systems, for other desired properties, showing promising opportunities to practical applications in future.