以木本中药渣为原料,采用浸渍法负载不同含量K2CO3催化剂;通过热重实验,分析中药渣催化热解特性和热解特征参数,并采用Starink法进行动力学分析,计算催化热解反应的表观活化能;使用固定床热解炉,优化催化热解反应条件,考察不同K2CO3负载量对热解产物分布的影响规律。热重结果表明,K2CO3能显著降低中药渣的初始热解温度和最大热解温度,从而降低热解快速失重段的反应活化能;且K2CO3负载量越大,催化热解效果越好。热解实验证实：K2CO3含量为中药渣催化热解反应的最主要影响因素,它可加速生物基大分子的低温解聚和热解中间产物的催化裂解,既可降低热解油产率,又能大幅提升H2、CO和C2H6等小分子低碳烃气体的产率,且有利于提高热解气的H2/CO比例。
Abstract
Woody herb residues were used as the raw materials, and K2CO3 catalysts with different contents were simply loaded by the impregnation method. The catalytic pyrolysis characteristics and kinetic parameters of herb residues were analyzed by thermogravimetric experiments （TG）, and Starink method was used for kinetic analysis to calculate the apparent activation energy of catalytic pyrolysis reaction. A fixed-bed reactor was used to optimize the catalytic pyrolysis conditions, and the effect of K2CO3 loadings on the product distribution of the herb residue pyrolysis was investigated as well. The TG/DTG results of thermogravimetric analysis show that K2CO3 could significantly reduce the initial pyrolytic temperature （Ti） and maximum pyrolytic temperature （Tmax）, and greatly reduce the activation energy of the herb catalytic pyrolysis during the rapid weight loss. The greater the K2CO3 loading is, the higher efficient the herb catalytic pyrolysis is. The fixed-bed pyrolysis experiments show that K2CO3 loading is the most important factor for the catalytic pyrolysis of the herb residues based on the orthogonal analysis. It is proved that K2CO3 accelerates the low-temperature depolymerization of biological macromolecules and the catalytic cracking of pyrolysis intermediates. Potassium carbonate not only reduces the yield of pyrolysis oil, but also obviously increases the yield of syngas （H2, CO） and small molecule low-carbon hydrocarbon gases such as C2H6 and C3H8, which is conducive to increasing the H2/CO molar ratio of pyrolysis gas.
关键词
生物质 /
热解 /
动力学参数 /
碳酸钾 /
中药渣
Key words
biomass /
pyrolysis /
kinetic parameters /
potassium carbonate /
herb residues
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基金
国家重点研发计划（2019YFB1503905）; 国家自然科学基金（51676192）; 广东省科技计划（2019A050510031）
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