Vitrification is a promising treatment for municipal solid waste incineration fly ash (MSWI-FA); however, high energy consumption due to the high MSWI-FA fusion temperature limits the development and application of this technique. In this study, fine slag ash (FSA) derived from coal gasification and coal gangue ash (CGA) were mixed with MSWI-FA to reduce the ash fusion temperature. The transformation of minerals in ash during thermal treatment was examined via X-ray diffraction and thermodynamic equilibrium calculations. The ash flow behaviour was observed using a thermal platform microscope, and the silicate structure was quantified using Raman spectra. The co-melting mechanisms for the mixed ash were systematically investigated. Results indicate that the flow temperature (FT) of the mixed ash exhibited an initial decrease and subsequent increase as a function of the addition ratio of FSA or CGA. Lowest ash FT of 1215 °C and 1223 °C were recorded for addition of 50% FSA and 50% CGA, respectively; further, these temperatures were lowered by > 285 °C and >277 °C respectively, relative to FT of the MSWI-FA. The transformation of minerals and silicate structure during mixed ash heating was responsible for the variation in the ash fusion temperature. CaO in MSWI-FA tended to react with mullite, quartz and haematite in FSA and CGA, forming minerals such as anorthite, gehlenite, and andradite with relatively low melting points. The addition of FSA or CGA caused changes in the silicate network structure of the mixed ash. In particular, 50% FSA incorporation caused the transformation of Q and Q to Q, whereas 50% CGA introduction resulted in the conversion of Q and Q into Q and Q + Q, respectively. The silicate network depolymerised, causing reduction in the ash fusion temperature and increasing the melting rate.
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http://dx.doi.org/10.1016/j.jenvman.2024.122035 | DOI Listing |
Prog Addit Manuf
July 2024
Empa Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland.
Fast and accurate representation of heat transfer in laser powder-bed fusion of metals (PBF-LB/M) is essential for thermo-mechanical analyses. As an example, it benefits the detection of thermal hotspots at the design stage. While traditional physics-based numerical approaches such as the finite element (FE) method are applicable to a wide variety of problems, they are computationally too expensive for PBF-LB/M due to the space- and time-discretization requirements.
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January 2025
School of Food and Pharmacy, Zhejiang Ocean University, Zhoushan, 316022, People's Republic of China.
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January 2025
Tohoku Medical and Pharmaceutical University, 4-4-1 Komatsushima, Aoba-ku, Sendai, Miyagi 981-8558, Japan. Electronic address:
Dectin-1 (CLEC7A), a C-type lectin-like receptor that recognizes β-1,3 glucans, has a key role in the innate immune system. While the lectin domain of mouse Dectin-1 has been solubilized and refolded from inclusion bodies in Escherichia coli, similar refolding of the human Dectin-1 lectin domain is hindered by the formation of misfolded multimers with aberrant intermolecular disulfide bonds. The aim of this study was to develop a method for the large-scale production of the human Dectin-1 lectin domain.
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January 2025
College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model.
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January 2025
Key Laboratory of Artificial Intelligence of Sichuan Province, Yibin 644000, China.
Accurately predicting the remaining useful life (RUL) is crucial for ensuring the safety and reliability of aircraft engine operation. However, aircraft engines operate in harsh conditions, with the characteristics of high speed, high temperature, and high load, resulting in high-dimensional and noisy data. This makes feature extraction inadequate, leading to low accuracy in the prediction of the RUL of aircraft engines.
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