A laboratory scale up-flow biological activated carbon(BAC) reactor was constructed for the advanced treatment of synthetic flotation wastewater. Biodegradation of a common collector(i.e., ethyl xanthate) for non-ferr...A laboratory scale up-flow biological activated carbon(BAC) reactor was constructed for the advanced treatment of synthetic flotation wastewater. Biodegradation of a common collector(i.e., ethyl xanthate) for non-ferrous metallic ore flotation was evaluated. The results show that the two stages of domestication can improve microbial degradation ability. The BAC reactor obtains a chemical oxygen demand(COD) reduction rate of 82.5% for ethyl xanthate and its effluent COD concentration lowers to below 20 mg/L. The kinetics equation of the BAC reactor proves that the activated carbon layers at the height of 0 mm to 70 mm play a key role in the removal of flotation reagents. Ultraviolet spectral analysis indicates that most of the ethyl xanthate are degraded by microorganisms after advanced treatment by the BAC reactor.展开更多
基金Project(201209013)supported by Special Fund for Environmental Scientific Research in the Public Interest,China
文摘A laboratory scale up-flow biological activated carbon(BAC) reactor was constructed for the advanced treatment of synthetic flotation wastewater. Biodegradation of a common collector(i.e., ethyl xanthate) for non-ferrous metallic ore flotation was evaluated. The results show that the two stages of domestication can improve microbial degradation ability. The BAC reactor obtains a chemical oxygen demand(COD) reduction rate of 82.5% for ethyl xanthate and its effluent COD concentration lowers to below 20 mg/L. The kinetics equation of the BAC reactor proves that the activated carbon layers at the height of 0 mm to 70 mm play a key role in the removal of flotation reagents. Ultraviolet spectral analysis indicates that most of the ethyl xanthate are degraded by microorganisms after advanced treatment by the BAC reactor.
文摘针对污水处理复杂系统中关键水质参数生化需氧量(biochemical oxygen demand,BOD)难以准确实时预测的问题,在分析污水处理过程相关影响因素的基础上,提出一种基于敏感度分析法的自组织随机权神经网络(selforganizing neural network with random weights,SONNRW)软测量方法.该方法首先通过机理分析选取原始辅助变量,经过数据预处理,之后采用主元分析法对辅助变量进行精选,作为SONNRW的输入变量进行污水处理关键水质参数BOD的预测.SONNRW算法利用隐含层节点输出及其权值向量计算该隐含层节点对于残差的敏感度,根据敏感度大小对网络隐含层节点进行排序,删除敏感度较低的隐含层节点即冗余点.仿真结果表明:该软测量方法对水质参数BOD的预测精度高、实时性好、模型结构稳定,能够用于污水水质的在线预测.
