基于反向传播神经网络模型优化冶金废水单级脱氮试验研究  

Optimization of Single-stage Denitrification of Metallurgical Wastewater Based on Back Propagation Neural Network

作　　者：郑小发 杨丽 刁月 王秀模 ZHENG Xiaofa;YANG Li;DIAO Yue;WANG Xiumo(Chongqing University of Mechanical and Electrical Technology,Chongqing 402760,China)

机构地区：[1]重庆机电职业技术大学,重庆402760

出　　处：《湿法冶金》2021年第2期167-173,共7页Hydrometallurgy of China

基　　金：重庆市教育科学“十三五”规划项目(2018-GX-419);重庆市教委科学技术研究项目(KJQN201903702);2016年重庆市教育委员会人文社会科学研究项目(16SKGH254)。

摘　　要：研究了基于前馈反向传播人工神经网络模型,结合Box-Behnken设计方法,采用厌氧氨氧化和部分硝化(SNAP)工艺、以上流式污泥床(upflow-sludge-bed,USB)反应器进行单级脱氮处理从含氮废水中去除NH+4和总氮。通过优化网络结构参数,开发神经网络;基于拟合优度标准,用Levenberg-Marquardt算法训练3层NH+4和总氮的去除。神经网络模型偏差较小(±2.1%),测定系数和分数方差较理想,协议指数(IA)分别为0.989~0.997、0.003~0.031和0.993~0.998。计算结果表明,优化后的神经网络结构可提高神经网络模型效率;利用人工神经网络模型对复杂的生物系统进行建模,可以提高去除效率、实施过程控制策略和实现优化性能;微生物群落的16S rRNA高通量法分析结果表明,库尼尼假丝酵母属的作用最显著(13.11%),其次是亚硝基单胞菌属(6.23%)和蛋白链球菌属(3.1%),这进一步说明USB的脱氮途径主要是部分硝化/氨氧化过程。Based on the feedforward back propagation artificial neural network model,combined with Box-Behnken design,the single-stage denitrification treatment was carried out using anammox,partial nitrification(SNAP)process and upflow sludge bed(USB)reactor to remove NH+4 and total nitrogen in nitrogen-containing wastewater.Based on the goodness of fit criterion,Levenberg Marquardt algorithm is used to train the removal of NH+4 and total nitrogen in three layers.The deviation of the neural network model is small(±2.1%),and the measurement coefficient and fractional variance are ideal.The agreement index(IA)is 0.989~0.997,0.003~0.031 and 0.993~0.998,respectively.The results show that the optimized neural network structure can improve the efficiency of the neural network model;the artificial neural network model can improve the removal efficiency,implement the process control strategy and realize the optimization performance;the 16S of microbial community can improve the removal efficiency,implement the process control strategy and achieve the optimization performance The results of rRNA high-throughput analysis show that Candida cunini is the most effective(13.11%),followed by nitrosomonas(6.23%)and streptococcus albuminus(3.1%).This further indicates that the denitrification pathway of USB is mainly partial nitrification/ammoxidation.

关 键 词：人工神经网络 数学建模 部分硝化氨氧化法 单级脱氮 

分 类 号：X756[环境科学与工程—环境工程]