ABSTRACT:The high contents of sodium and calcium in Zhundong coal induce severe slagging and ash deposition in boilers. In this study, the ash deposition mechanism was investigated based on the results obtained from a full-scale boiler (350 MW) burning Zhundong coal, anda fixed bed reactor used for ash evaporating-condensing. In the full-scale boiler, the condensing and depositing of sodium and calcium sulfates play an important role on ash depositing on convection heating surfaces. Sulfates start to significantly condense and deposit at the flue gas temperature of about 850 o C (at Medium and High Temperature Reheater). Ash evaporating tests proved that, with the increasing in temperature from 400 o C to 1200 o C, the ash evaporating process is divided into three stages: 1) 400-800 o C, 80% of sodium, and 100% of chlorine are released; 2) 800-1000 o C, all the left sodium evaporates and sulfur starts to be released with the formation of partial aluminosilicates; 3) 1000-1200 o C, all the left sulfur is released through the decomposition of calcium sulfates and then calcium starts to evaporate, while silicon oxides disappears due to the formation of new complex silicates. Ash condensing tests further proved that, the sodium in Zhundong coal was released mainly in the forms of atom, oxide, and chloride, in which sodium chloride account for about 50%. When the evaporating temperature increased higher than 1000 o C, partial alkali and alkaline earth metals were released as gaseous sulfates, and afterwards condense and deposit on the heating surfaces. At last, a temperature-dependent ash deposition mechanism in Zhundong coal combustion was proposed.

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Alloy melts of the intermetallic compounds FeSi and CoSi were deeply undercooled using the melt fluxing technique. Crystal-growth velocities were measured as a function of undercooling by a highspeed photosensing infrared device. The experimental results were analyzed in the framework of dendrite growth theories with special emphasis placed on the kinetic interface undercooling. This analysis gives insight into nonequilibrium e6'ects occurring during rapid solidification of undercooled melts of intermetallic compounds due to short-range difusion-limited dendritic growth.

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Textual representations play an important role in the field of natural language processing (NLP). The efficiency of NLP tasks, such as text comprehension and information extraction, can be significantly improved with proper textual representations. As neural networks are gradually applied to learn the representation of words and phrases, fairly efficient models of learning short text representations have been developed, such as the continuous bag of words (CBOW) and skip-gram models, and they have been extensively employed in a variety of NLP tasks. Because of the complex structure generated by the longer text lengths, such as sentences, algorithms appropriate for learning short textual representations are not applicable for learning long textual representations. One method of learning long textual representations is the Long Short-Term Memory (LSTM) network, which is suitable for processing sequences. However, the standard LSTM does not adequately address the primary sentence structure (subject, predicate and object), which is an important factor for producing appropriate sentence representations. To resolve this issue, this paper proposes the dependency-based LSTM model (D-LSTM). The D-LSTM divides a sentence representation into two parts: a basic component and a supporting component. The D-LSTM uses a pre-trained dependency parser to obtain the primary sentence information and generate supporting components, and it also uses a standard LSTM model to generate the basic sentence components. A weight factor that can adjust the ratio of the basic and supporting components in a sentence is introduced to generate the sentence representation. Compared with the representation learned by the standard LSTM, the sentence representation learned by the D-LSTM contains a greater amount of useful information. The experimental results show that the D-LSTM is superior to the standard LSTM for sentences involving compositional knowledge (SICK) data.

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