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This study targets the research gap of degradation mechanism in hydrothermal gasification of recycling waste rubber. The depolymerization process of natural rubber in supercritical water, Gibbs free energy of the reaction pathway and the bond dissociation energy of C–C bonds were determined based on reactive force field molecular dynamics (ReaxFF-MD) simulation and density functional theory (DFT). The bond dissociation energy of the C–C bond between two adjacent repeat units of natural rubber monomer was 213 kJ mol−1, which was the lowest compared with other C–C bonds. The degradation process of the natural rubber in the hydrothermal gasification was divided into two stages from the simulation. In the first stage, each natural rubber molecule was cleaved into a left end molecular, a right end molecular and a large number of isoprene monomers. In the second stage, the deep cracking of a large number of isoprene monomers occurred. Water molecules could stimulate coke hydrocracking in the hydrothermal gasification of the natural rubber. At 2000 K (simulated temperature), a large number of isoprene monomers produced, which could not be further cracked. At 3000 K, the degradation rate increased by about 29% compared with 2500 K. The degradation efficiency of the natural rubber at the feedstock concentration of 11.20 mass% increased by 10.4% compared with that of 27.45 mass%. This work could provide theoretical support and basic data for recycling of waste rubber products by the hydrothermal gasification. [ABSTRACT FROM AUTHOR]

As a major air pollutant, SO2 has negative effect on the human health and environment. The desulfurization characteristics of two CaO samples (commercial one and the other one calcined from CaCO3) with high purity were examined by a thermo-gravimetric analyzer under circulating fluidized bed combustion-like condition. The influences of SO2 concentration (1000–4000 ppm), CO2 concentration (0–45%) and temperatures (800–950 °C) on the sulfation conversion degree of CaO samples were addressed, and sulfation kinetic parameters for the two samples were estimated based on the unreacted shrinking core model. The sulfation conversion degree of CaO calcined from CaCO3 at 900 °C and 2000 ppm SO2 was 68% higher than the commercial CaO. The sulfation conversion degree for the commercial CaO at 950 °C with 2000 ppm SO2 was one time higher than at 800 °C, and the sulfation conversion degree for the sample calcined from CaCO3 at 950 °C increased by about 31% compared to that at 800 °C. The calcium conversion degree of the sample calcined from CaCO3 was 0.59 in the absence of CO2, and the conversion degree with the CO2 concentration of 45% reduced by about 31%. The sulfation kinetics of two samples were appropriately described by the shrinking unreacted core model. The sample calcined from CaCO3 had a better sulfation activity than the commercial CaO. [ABSTRACT FROM AUTHOR]

Lignite and sewage sludge powder were pretreated through microwave heating at a power level of 119 W. The non-isothermal combustion characteristics of lignite and sewage sludge powder through the microwave pretreatment were examined based on a thermo-gravimetric technique at three heating rates (10, 20 and 30 °C min−1). The effect of the microwave pretreatment on the combustion performance index and kinetic parameters was addressed. The ignition temperature region of the lignite samples was in between 357 and 376 °C, which was higher than that of the sewage sludge samples ranged from 219 to 235 °C. The comprehensive performance index of the treated lignite and sewage sludge increased by 22.5% and 2.3%, respectively. The combustion kinetic parameters of the samples were evaluated based on KAS method coupled with Criado method. The average activation energies of the untreated lignite and sewage sludge were 213.83 and 176.92 kJ mol−1, while that for the treated samples were 232.64 and 203.01 kJ mol−1. [ABSTRACT FROM AUTHOR]

The artificial intelligence technique is utilized to improve evaluation of thermally induced solid-state reaction kinetics. A general regression neural network (GRNN) model was applied to directly determine the kinetic triplets, i.e., activation energy, pre-exponential factor, and mechanism model. The effect of number of heating rate on prediction performance of the GRNN model was assessed based on the estimation indictors. The obtained kinetic triplets based on the triple heating rates were considered to be accepted. The prediction ability of the GRNN model was very robust at more than three heating rates. The relative errors for kinetic parameters derived from five heating rates were within ± 4%, and the cognition rates for mechanism models were up to 99.6%. The developed GRNN model was successfully applied in the high-temperature synthesis of Li4Ti5O12/C composites. It is expected that the model also could be extended to estimate the kinetics of other solid-state reactions. [ABSTRACT FROM AUTHOR]