Catalytic performance and reaction mechanisms of NO removal with NH3 at low and medium temperatures on Mn-W-Sb modified siderite catalysts

Iron-based catalysts have been explored for selective catalytic reduction (SCR) of NO due to environmentally benign characters and good SCR activity. Mn-W-Sb modified siderite catalysts were prepared by impregnation method based on siderite ore, and SCR performance of the catalysts was investigated. The catalysts were analyzed by X-ray diffraction, H₂-temperature-programmed reduction, Brunauer-Emmett-Teller, Thermogravimetry-derivative thermogravimetry and in-situ diffused reflectance infrared Fourier transform spectroscopy (DRIFTS). The modified siderite catalysts calcined at 450°C mainly consist of Fe₂O₃, and added Mn, W and Sb species are amorphous. 3Mn-5W-1.5Sb-siderite catalyst has a wide temperature window of 180-360°C and good N₂ selectivity at low temperatures. In-situ DRIFTS results show NH₄⁺, coordinated NH₃, NH₂, NO₃⁻ species (bidentate), NO₂⁻ species (nitro, nitro-nitrito, monodentate), and adsorbed NO₂ can be discovered on the surface of Mn-W-Sb modified siderite catalysts, and doping of Mn will enhance adsorbed NO₂ formation by synergistic catalysis with Fe³⁺. In addition, the addition of Sb can inhibit sulfates formation on the surface of the catalyst in the presence of SO₂ and H₂O. Time-dependent in-situ DRIFTS studies also indicate that both of Lewis and Brønsted acid sites play a role in SCR of NO by ammonia at low temperatures. The mechanism of NO removal on the 3Mn-5W-1.5Sb-siderite catalyst can be discovered as a combination of Eley-Rideal and Langmuir-Hinshelwood mechanisms with three reaction pathways. The mechanism of NO, oxidized by synergistic catalysis of Fe³⁺ and Mn^4+/3+ to form NO₂ among three pathways, reveals the reason of high NO_x conversion of the catalyst at medium and low temperatures.

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An improved grey wolf optimizer algorithm for identification and location of gas emission

Identification of the leakage of hazardous gases plays an important role in the environment protection, human health and safety of industry production. However, lots of current optimization algorithms, such as particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO), suffer from poor global optimization capability and estimation accuracy. In this work, a hybrid differential evolutionary and GWO (DE-GWO) algorithm is proposed. Tested by simulation cases and Prairie Grass emission experimental data, DE-GWO shows higher estimation accuracy than GWO. Compared with the other four optimization algorithms, DE-GWO exhibits finer robust stability under different population sizes, fewer iterations, as well as higher estimation accuracy with fewer search agents. Importantly, simulation results demonstrate that DE-GWO is more suitable to apply in the scene with a small number of sensors. Therefore, the proposed in this paper outperforms other optimization algorithms for the gas emission inverse problem. DE-GWO can provide reliable estimation towards gas emission identification and positioning, which shows huge potential as the data analysis module of real-time monitoring and early warning system.