Facile preparation of high-performance Fe-doped Ce–Mn/TiO2 catalysts for the low-temperature selective catalytic reduction of NOx with NH3†

A Ce–Mn–Fe/TiO2 catalyst has been successfully prepared using a single impregnation method, and excellent low-temperature NH3-SCR activity was demonstrated in comparison with other typical SCR catalysts including Mn–Ce/TiO2 and metal-doped Mn–Ce/TiO2. The crystal structure, morphology, textural properties, valence state of the metals, acidity and redox properties of the novel catalyst were investigated comprehensively by X-ray diffraction (XRD), N2 adsorption and desorption analysis, X-ray photoelectron spectroscopy (XPS), NH3-temperature-programmed desorption (NH3-TPD), and H2-temperature-programmed reduction (H2-TPR). The Fe-doped Ce–Mn/TiO2 catalyst boosted the low-temperature NH3-SCR activity effectively under a broad temperature range (100–280 °C) with a superior NO conversion rate at low temperatures (100 °C, 96%; 120–160 °C, ∼100%). Fe doping caused this improvement by enlarging the catalyst pore volume, improving the redox properties, and increasing the amount of acidic sites. These properties enhanced the ability of the catalyst to adsorb NH3 and improved the low-temperature SCR performance, especially at temperatures lower than 150 °C. Moreover, redox cycles of Ce, Mn, and Ti (Mn4+ + Ce3+ ↔ Mn3+ + Ce4+, Mn4+ + Ti3+ ↔ Mn3+ + Ti4+) also played an important role in enhancing the low-temperature SCR efficiency by accelerating the electron transfer. The excellent NH3-SCR result is promising for developing environmentally-friendly and more effective industrial catalysts in the future

A Hybrid Wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series