The formation mechanism of ammonium bisulfate (ABS) in the process of SCR flue gas denitrification and some research progresses on ABS are reviewed in the paper. The main factors affecting the formation of ABS are investigated, the formation temperature of ABS is in the range of about 190°C−240°C; ABS exists in vapor phase at temperature between 320°C and 345°C, and begins to decompose when the temperature is higher than 345°C; the formation of the ABS can be reduced by reducing the amount of NH3 slip and SO3, in the flue gas. The adverse effects of ABS on catalyst and air preheater are discussed: ABS will cover the surface of catalyst and Bronsted acid sites, decreasing denitrification efficiency; it will deposit in the air preheater, fouling and corroding the air preheater and reduce its operational effectiveness; it also can reduce the resistivity of fly ash and the quality of the fly ash as well. The methods to control ABS formation and the measures to reduce its negative effects are proposed: Select appropriate operating temperature to reduce the ABS deposition over the SCR catalyst; replace the material of the air preheater components from steel to ceramic coating type and change the structure of the air preheater; Take proper methods to reduce NH3 slip and the oxidation rate of SO2, etc. © 2016 American Institute of Chemical Engineers Environ Prog, 35: 1664–1672, 2016

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Multi-source domain adaptation has attracted great attention in machine learning community. Most of these methods focus on weighting the predictions produced by the adaptation networks of different domains. Thus the domain shifts between certain of domains and target domain are not effectively relieved, resulting in that these domains are not fully exploited and even may have a negative influence on multi-source domain adaptation task. To address such challenge, we propose a multi-source domain adaptation method to gradually improve the adaptation ability of each source domain by producing more high-confident pseudo-labels with self-paced learning for conditional distribution alignment. The proposed method first trains several separate domain branch networks with single domains and an ensemble branch network with all domains. Then we obtain some high-confident pseudo-labels with the branch networks and learn the branch specific pseudo-labels with self-paced learning. Each branch network reduces the domain gap by aligning the conditional distribution with its branch specific pseudo-labels and the pseudo-labels provided by all branch networks. Experiments on Office31, Office-Home and DomainNet show that the proposed method outperforms the state-of-the-art methods.

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