To
overcome the disadvantages of Cu-based catalysts, such as the
low dispersion of active components and insufficient active species,
several 15% Cu-L
x
/AC catalysts for acetylene
hydrochlorination were synthesized based on strong interactions between
a ligand and CuCl2 precursors. The introduction of the
methyldiphenyloxophosphine (MDPO) ligand effectively modulated the
electronic properties of the metal centers, which contributed to the
construction of a highly dispersed Cu–P/Cl local structure
with Cu1+/Cu2+ as a plausible active center.
The sintering of active components in the catalyst may be one of the
main reasons for the decrease in catalytic performance. Meanwhile,
the enhanced adsorption and activation of the catalyst for C2H2 and HCl molecules resulted in improved coking resistance.
The most active catalyst (15% Cu8MDPO1/AC) could
achieve a stable acetylene conversion of 97% at 180 °C, a gas
hourly space velocity (GHSV) (C2H2) of 180 h–1, and a feed volume ratio (V
HCl/V
C2H2
) of 1.15, outperforming the benchmark catalyst. The excellent activity
and stability in a 300 h laboratory test at a high GHSV and a 3414
h industrial sideline test at an industrial GHSV render the 15% Cu8MDPO1/AC catalyst as a reference for the construction
of other catalysts from an environmental, economic, and application
prospect perspective.
In opportunistic networks, the requirement of QoS (quality of service) poses several major challenges to wireless mobile devices with limited cache and energy. This implies that energy and cache space are two significant cornerstones for the structure of a routing algorithm. However, most routing algorithms tackle the issue of limited network resources from the perspective of a deterministic approach, which lacks an adaptive data transmission mechanism. Meanwhile, these methods show a relatively low scalability because they are probably built up based on some special scenarios rather than general ones. To alleviate the problems, this paper proposes an adaptive delay-tolerant routing algorithm (DTCM) utilizing curve-trapezoid Mamdani fuzzy inference system (CMFI) for opportunistic social networks. DTCM evaluates both the remaining energy level and the remaining cache level of relay nodes (two-factor) in opportunistic networks and makes reasonable decisions on data transmission through CMFI. Different from the traditional fuzzy inference system, CMFI determines three levels of membership functions through the trichotomy law and evaluates the fuzzy mapping from two-factor fuzzy input to data transmission by curve-trapezoid membership functions. Our experimental results show that within the error interval of 0.05~0.1, DTCM improves delivery ratio by about 20% and decreases end-to-end delay by approximate 25% as compared with Epidemic, and the network overhead from DTCM is in the middle horizon.
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