生物质热解特性研究

以竹材、稻壳、木屑为原料,通过常规热解结合快速热解研究生物质热解特性。结果表明,生物质常规热解的液体得率较低,相比而言竹材最高,稻壳最低,且热解温度是影响竹材和木屑热解的主要因素,其液体得率随温度的升高呈先增后减的变化规律;快速热解方面,利用居里点裂解仪和GC—MS在线分析竹材热解的液相组成,其组成以糠醛和酚类物质为主,它们分别来源于纤维素、半纤维素和木质素的热解。     

居里点裂解色谱及其应用

本文简要介绍居里点裂解色谱的工作原理、装置、实验方法和特点,并通过实例介绍其在各方面的应用。     

水煤浆和原煤粉居里点裂解仪对比实验研究

利用居里点裂解仪对水煤浆和原煤粉进行中速热解。介绍了居里点裂解仪的工作原理,计算了2种样品在实验中的热解动力学参数和升温速率,对比分析2种样品中速热解时的反应特性。结果表明,水煤浆的活化能为16.362 kJ/mol,大于原煤粉的活化能12.691 kJ/mol,但升温速率比原煤粉小,分别为617 K/s和834 K/s。实验还发现水煤浆因为其中水分的影响,其挥发分的热解有明显的滞后,但热解曲线的斜率比原煤粉大,说明水煤浆在经过热力储备阶段达到热解的活化能后其热解速率比原煤粉要快,最终的产量也稍高。     

Techniques for studying and modeling coal pyrolysis and their relevance to biomass and wastes

Pyrolysis is the first step in the thermochemical conversion of any organic material. It plays a key role in coal conversion, where processes like combustion, gasification and hydrogenation include pyrolytic reactions as a starting step, and is the basic process in cokemaking. In the long history of industrial use of thermochemical coal conversion, various methods have been developed for feedstock characterization and for the main chemical reactions. Furthermore, approaches have been established to describe pyrolysis behavior quantitatively and to provide tools for reactor modeling. The main experimental techniques are reviewed and their relevance for the treatment of other solid feedstocks like biomass and wastes is discussed.     

Rapid assessment of the adulteration of virgin olive oils by other seed oils using pyrolysis mass spectrometry and artificial neural networks

Curie-point pyrolysis mass spectra were obtained from a variety of extra-virgin olive oils, prepared from various cultivars using several mechanical treatments. Some of the oils were adulterated (according to a double-blind protocol) with different amounts of seed oils (50–500 ml of soya, sunflower, peanut, corn or rectified olive oils per litre of mixed oil). Canonical variates analysis indicated that the major source of variation between the pyrolysis mass spectra was due to differences between the cultivars. rather than whether the oils had been adulterated. However, artificial neural networks could be trained (using the back-propagation algorithm) successfully to distinguish virgin oils from those which had been adulterated.