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首页> 外文期刊>Journal of analytical & applied pyrolysis >Comprehensive study of the kinetics of combustion and pyrolysis of petrochemical sludge: Experimentation and application of artificial neural network
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Comprehensive study of the kinetics of combustion and pyrolysis of petrochemical sludge: Experimentation and application of artificial neural network

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? 2023 The AuthorsEnergy can be obtained from the pyrolysis and combustion of purified terephthalic acid (PTA) sludge which is produced during the treatment of PTA wastewater. In this study, the combustion and pyrolysis behaviors and kinetics of the sludge produced from the thermochemical treatment of PTA wastewater were analyzed under non-isothermal heating. It was found that reaction orders were in the range of 1–3 for oxidation/combustion. The reactions were exothermic, as indicated by the negative change in enthalpy. The phenomenon of auto gasification was observed during pyrolysis for three sludges. The analysis of the change of activation energy with the degree of conversion showed that several parallel reactions occurred at the beginning of the sludge decomposition during air combustion. The value of activation energy for CuSO4, FeCl3 and FeCl3 + CPAA sludge was 99.5 kJ/mol, 109 kJ/mol and 118.7 kJ/mol, respectively, for pyrolysis and 116.3 kJ/mol, 68.8 kJ/mol, and 127.9 kJ/mol, respectively, for combustion. These sludges are promising for energy recovery because they have a high net calorific value (NCV) and a high gross calorific value (GCV). The GCV was 22.4 MJ/kg, 19.8 MJ/kg, and 28.4 MJ/kg for CuSO4, FeCl3, and FeCl3 with CPAA, respectively and the NCV was 22.3 MJ/kg, 18.6 MJ/kg, and 27.3 MJ/kg for CuSO4, FeCl3, and FeCl3 with CPAA, respectively. The artificial neural network was used to validate the experimental results. The correlation coefficient between the expected and experimental results were 0.9945, 0.9980 and 0.9980 for CuSO4, FeCl3 and FeCl3 +CPAA sludge for pyrolysis, respectively and 0.9989, 0.9963 and 0.9968 for CuSO4, FeCl3 and FeCl3 +CPAA sludge for combustion, respectively. The developed ANN model successfully predicts the combustion and pyrolytic process for petrochemical sludge under specified conditions.

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