Understanding catalytic reactions over zeolites: A density functional theory study of selective catalytic reduction of NOX by NH3 over Cu-SAPO-34

Metal exchanged CHA-type (SAPO-34 and SSZ-13) zeolites are promising catalysts for selective catalytic reduction (SCR) of NOx by NH3. However, the understanding of the process at the molecular level is still limited, which hinders the identification of its mechanism and the design of more efficient zeolite catalysts. In this work, modelling the reaction over Cu-SAPO-34, a periodic density functional theory (DFT) study of NH3-SCR was performed using hybrid functional with the consideration of van der Waals (vdW) interactions. A mechanism with a low N–N coupling barrier is proposed to account for the activation of NO. The redox cycle of Cu2+ and Cu+, which is crucial for the SCR process, is identified with detailed analyses. Besides, the decomposition of NH2NO is shown to readily occur on the Brønsted acid site by a hydrogen push-pull mechanism, confirming the collective efforts of Brønsted acid and Lewis acid (Cu2+) sites. The special electronic and structural properties of Cu-SAPO-34 are demonstrated to play an essential role the reaction, which may have a general implication on the understanding of zeolite catalysis.

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A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.