Biofiltration of Hexane Vapor: Experimental and Neural Model Analysis

Biofiltration is a commonly practiced biological technique to remove volatile compounds from waste gas streams. From an industrial view‐point, biofilter (BF) operation should be flexible to handle temperatures and inlet load (IL) variations. A compost BF was operated at different temperatures (30–45°C) and at various inlet loading rates (ILR; 8–598 g m^?3 h^?1) under intermittent loading conditions. Complete removal of n‐hexane was observed at 30 and 35°C at ILRs up to 330 g m^?3 h^?1. Besides, 20–75% of the pollutant was removed at 40°C, corresponding to the different ILs applied to the BF. Increasing the temperature to 45°C decreased the removal efficiency (RE) significantly. A feed forward neural network was used to predict the RE of BF with temperature and ILR as the input variables. The experimental data was divided into training (2/3) and test datasets (1/3). The best structure of neural network was obtained by trial and error on the basis of the least differences between predicted and experimental values, as ascertained from their coefficient of regression (R²) values. The modeling results showed that a multilayer network with the topology 2?10?1 was able to predict BF performance effectively with R²‐value of 0.995 for the test data. The results from this study showed the predicting capability of ANNs which can be considered as an alternative for conventional knowledge‐based models.     

温度、盐度和pH对生物过滤器去除氨氮效率的影响

采用正交设计法对温度，盐度和pH值对生物膜转氨氮能力的影响进行考察，考虑至生物过滤器的一般工作环境，3个因子各取三水平，为了充分考虑交互作用，选用正交表L27（3^13).结果表明，取值范围盐pH值对硝化细菌代谢的影响不明显，温度对硝化细菌的代诱明显的影响，而且25℃组的氨氮支作率最高。