首页|一种预测Mn/Fe复合氧化物对燃煤烟气中砷、硒吸附特性的新方法

一种预测Mn/Fe复合氧化物对燃煤烟气中砷、硒吸附特性的新方法

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Mn/Fe复合氧化物因对燃煤烟气中的砷、硒具备良好的吸附潜力而备受关注,但其吸附性能会受到吸附剂制备过程和吸附过程的影响,且实验测试成本高、周期长。根据吸附剂物化特性与吸附量的关系,该文建立新的预测模型,并对比不同摩尔比吸附剂的表面结构差异以及计算不同温度下砷、硒的转化率、活化能等参数。然后,分别将二者的影响程度嵌入数学模型中,使得该模型可以用于预测不同条件下吸附剂的吸附效率。结果表明:当 Mn/Fe 摩尔比为 1:1时,砷、硒的吸附量达到最大;二者的最佳吸附温度分别为750 和 600℃。砷、硒的吸附流量系数随温度升高均呈现先升高后下降的趋势,吸附流量系数随不同温度下吸附剂的形貌结构呈正相关。将预测值与实验值进行对比分析后证明了该方法的准确性,可为寻找高效重金属吸附剂提供一种实验手段之外的新思路。
A New Method for Predicting the Adsorption Properties of Mn/Fe Binary Oxides for Arsenic and Selenium in Coal Flue Gas
Mn/Fe binary oxides have gained significant attention due to their excellent adsorption potential for arsenic and selenium in coal-fired flue gas.However,their adsorption performance is influenced by both the preparation process of the adsorbent and the adsorption process itself,and experimental testing is characterized by high costs and lengthy cycles.A new predictive model is established based on the physicochemical properties of the adsorbent and its adsorption capacity.This model compares the surface structure differences of adsorbents with different molar ratios and calculates various parameters such as the conversion rate and activation energy of arsenic and selenium at different temperatures.Subsequently,the respective impact magnitudes of these factors are incorporated into the mathematical model,allowing the model to predict the adsorption efficiency of the adsorbent under different conditions.The results of the model calculations indicate that the maximum adsorption capacity for arsenic and selenium is achieved when the Mn/Fe molar ratio is 1:1.The optimal adsorption temperatures for arsenic and selenium are 750 and 600℃,respectively.The adsorption flux coefficients of arsenic and selenium show an initial increase followed by a decrease with increasing temperature.The adsorption flux coefficients are positively correlated with the morphological structure of the adsorbent at different temperatures.A comparative analysis between predicted values and experimental values confirms the accuracy of this method.This research provides a novel approach beyond experimental means for identifying efficient heavy metal adsorbents.

coal flue gasadsorbentarsenicseleniumprediction model

王震、邢佳颖、袁潇、王春波

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河北省低碳高效发电技术重点实验室(华北电力大学动力工程系),河北省 保定市 071003

燃煤烟气 吸附剂 预测模型

2024

中国电机工程学报
中国电机工程学会

中国电机工程学报

CSTPCD北大核心
影响因子:2.712
ISSN:0258-8013
年,卷(期):2024.44(24)