神经网络法预测分子总表面积的研究

本文用误差反向传播人工神经网络（ＡＮＮ）预测有机分子的总表面积，提出一组分子描述码（描述分子的分支性、不饱和性和成环性）作为输入的特征参数，预测结果：对于烷烃、烯烃、炔烃、脂环烃、芳烃、卤代烃、醇、醛、酮、酸、酯、胺、硫醇、杂环等１４类有机物平均误差为２．５８％，对于多环芬烃平均误差为２．８８％。根据ＡＮＮ法提供的信息，用统计分析的方法，得到分子总表面积与分子描述码之间的关联式，用它估算分子总表面     

多环芳烃的分子表面积与双区理论

本文提出了一种利用基团贡献法原理计算分子表面积(TSA)的新方法,应用该方法计算了多环芳烃(PAHs)的TSA,并以双区理论为基础,研究PAHs的分子表面积与致癌活性的关系,将TSA参数引入双区理论方程,通过本文提出的理论方程对PAHs的致癌活性进行了计算,所得结果与实验值吻合得很好,对双区理论进行了分子表面积的补充。     

Numerical parametric study on CO2 capture by indirect thermal swing adsorption

Post-combustion CO₂ capture remains one of the most-challenging issue to lower CO₂ emissions of existing power plants or heavy industry installations because of strong economy and energy efficiency aspects. The major issue comes from CO₂ dilution (4% for NGCC and 14% for PC) and the high flow rates to be treated. Furthermore, CO₂ purity has to be higher than 95% with recovery at 90%, to match the transportation/injection requirements.The MEA absorption process remains the reference today but its energy consumption (about 3 MJ/kg_CO2) and the amine consumption are still challenging drawbacks.The interest of CO₂ capture by indirect TSA (Temperature Swing Adsorption) was demonstrated experimentally in a previous work. The aim of this paper is to present the results of a numerical parametric study. Two main parameters are explored: the desorption temperature (100–200 °C) and the purge flow rate (0.1–0.5 Ndm³ min⁻¹). Four performance indicators are evaluated: CO₂ purity, recovery, productivity and specific energy consumption.Results show that purity above 95% can be achieved. Keeping the 95% target, it is possible to achieve recovery at 81% with productivity at 57.7 g_CO2/kg_ads h and a specific energy consumption of 3.23 MJ/kg_CO2, which is less than for the reference MEA process.Comparison with other adsorption processes exhibits that this process has good potential especially since some improvements are still expected from further research.