提出了一种紫外多波长与BP神经网络相结合的有机废水COD检测技术.由单片机(single chip microcmputer,SCM)控制GSM(global system for mobile communication)系统,采集的数据以串行通信方式发送到PC机上.由BP算法对超出单波长法检测区间...提出了一种紫外多波长与BP神经网络相结合的有机废水COD检测技术.由单片机(single chip microcmputer,SCM)控制GSM(global system for mobile communication)系统,采集的数据以串行通信方式发送到PC机上.由BP算法对超出单波长法检测区间的COD值进行预测.分别从15条光谱扫描曲线中提取8组不同波长对应的吸光度数据作为算法的输入,调整算法的有关参数,并进行一定次数的训练.对最终预测结果进行了误差分析,数据显示相对误差控制在5%以内,预测结果稳定.展开更多
The analysis of seven aliphatic carboxylic acids(formic,acetic,propionic,iso-butyric,n-butyric,iso-valeric and n-valeric acid) in anaerobic digestion process waters for biogas production was examined by ion-exclusion ...The analysis of seven aliphatic carboxylic acids(formic,acetic,propionic,iso-butyric,n-butyric,iso-valeric and n-valeric acid) in anaerobic digestion process waters for biogas production was examined by ion-exclusion chromatography with dilute acidic eluents(benzoic acid,perfluorobutyric acid(PFBA) and sulfuric acid) and non-suppressed conductivity/ultraviolet(UV) detection.The columns used were a styrene/divinylbenzene-based strongly acidic cation-exchange resin column(TSKgel SCX) and a polymethacrylate-based weakly acidic cation-exchange resin column(TSKgel Super IC-A/C).Good separation was performed on the TSKgel SCX in shorter retention times.For the TSKgel Super IC-A/C,peak shape of the acids was sharp and symmetrical in spite of longer retention times.In addition,the mutual separation of the acids was good except for iso-and n-butyric acids.The better separation and good detection was achieved by using the two columns(TSKgel SCX and TSKgel Super IC-A/C connected in series),lower concentrations of PFBA and sulfuric acid as eluents,non-suppressed conductivity detection and UV detection at 210 nm.This analysis was applied to anaerobic digestion process waters.The chromatograms with conductivity detection were relatively simpler compared with those of UV detection.The use of two columns with different selectivities for the aliphatic carboxylic acids and the two detection modes was effective for the determination and identification of the analytes in anaerobic digestion process waters containing complex matrices.展开更多
文摘提出了一种紫外多波长与BP神经网络相结合的有机废水COD检测技术.由单片机(single chip microcmputer,SCM)控制GSM(global system for mobile communication)系统,采集的数据以串行通信方式发送到PC机上.由BP算法对超出单波长法检测区间的COD值进行预测.分别从15条光谱扫描曲线中提取8组不同波长对应的吸光度数据作为算法的输入,调整算法的有关参数,并进行一定次数的训练.对最终预测结果进行了误差分析,数据显示相对误差控制在5%以内,预测结果稳定.
文摘The analysis of seven aliphatic carboxylic acids(formic,acetic,propionic,iso-butyric,n-butyric,iso-valeric and n-valeric acid) in anaerobic digestion process waters for biogas production was examined by ion-exclusion chromatography with dilute acidic eluents(benzoic acid,perfluorobutyric acid(PFBA) and sulfuric acid) and non-suppressed conductivity/ultraviolet(UV) detection.The columns used were a styrene/divinylbenzene-based strongly acidic cation-exchange resin column(TSKgel SCX) and a polymethacrylate-based weakly acidic cation-exchange resin column(TSKgel Super IC-A/C).Good separation was performed on the TSKgel SCX in shorter retention times.For the TSKgel Super IC-A/C,peak shape of the acids was sharp and symmetrical in spite of longer retention times.In addition,the mutual separation of the acids was good except for iso-and n-butyric acids.The better separation and good detection was achieved by using the two columns(TSKgel SCX and TSKgel Super IC-A/C connected in series),lower concentrations of PFBA and sulfuric acid as eluents,non-suppressed conductivity detection and UV detection at 210 nm.This analysis was applied to anaerobic digestion process waters.The chromatograms with conductivity detection were relatively simpler compared with those of UV detection.The use of two columns with different selectivities for the aliphatic carboxylic acids and the two detection modes was effective for the determination and identification of the analytes in anaerobic digestion process waters containing complex matrices.