Cai Jing, Zheng Ping, Qaisar Mahmood, Luo Tao
College of Environmental Science and Engineering, Zhejiang Gongshang University, Hangzhou, 310012, China,
Environ Sci Pollut Res Int. 2015 Jun;22(11):8272-9. doi: 10.1007/s11356-014-3976-3. Epub 2014 Dec 20.
The present investigation deals with the prediction of the performance of simultaneous anaerobic sulfide and nitrate removal in an upflow anaerobic sludge bed (UASB) reactor through an artificial neural network (ANN). Influent sulfide concentration, influent nitrate concentration, S/N mole ratio, pH, and hydraulic retention time (HRT) for 144 days' steady-state condition were the inputs of the model; whereas output parameters were sulfide removal percentage, nitrate removal percentage, sulfate production percentage, and nitrogen production percentage. The prediction performance was evaluated by calculating root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE), and determination coefficient (R (2)) values. Generally, the ANN model exhibited good prediction of the simultaneous sulfide and nitrate removal process. The effect of five input parameters to the performance of the reactor was quantified and compared using the connection weights method, Garson's algorithm method, and partial derivatives (PaD) method. The results showed that HRT markedly affects the performance of the reactor.
本研究通过人工神经网络（ANN）对升流式厌氧污泥床（UASB）反应器中同时去除厌氧硫化物和硝酸盐的性能进行预测。144天稳态条件下的进水硫化物浓度、进水硝酸盐浓度、S/N摩尔比、pH值和水力停留时间（HRT）为模型输入；而输出参数为硫化物去除率、硝酸盐去除率、硫酸盐产率和氮产率。通过计算均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对相对误差（MARE）和决定系数（R²）值来评估预测性能。总体而言，ANN模型对同时去除硫化物和硝酸盐的过程表现出良好的预测效果。使用连接权重法、加森算法法和偏导数（PaD）法对五个输入参数对反应器性能的影响进行了量化和比较。结果表明，HRT对反应器性能有显著影响。
