School of Energy and Environment, Southeast University, No. 2 Sipailou Road, Nanjing, 210096, China.
ERC Taihu Lake Water Environment (Wuxi), No. 99 Linghu Avenue, Wuxi, 214135, China.
Environ Sci Pollut Res Int. 2017 Aug;24(23):19211-19222. doi: 10.1007/s11356-017-9437-z. Epub 2017 Jun 30.
A systematic calibration and validation procedure for the complex mechanistic modeling of anaerobic-anoxic/nitrifying (A2N) two-sludge system is needed. An efficient method based on phase experiments, sensitivity analysis, and genetic algorithm is proposed here for model calibration. Phase experiments (anaerobic phosphorus release, aerobic nitrification, and anoxic denitrifying phosphate accumulation) in an A2N sequencing batch reactor (SBR) were performed to reflect the process conditions accurately and improve the model calibration efficiency. The calibrated model was further validated using 30 batch experiments and 3-month dynamic continuous flow (CF) experiments for A2N-SBR and CF-A2N process, respectively. Several statistical criteria were conducted to evaluate the accuracy of model predications, including the average relative deviation (ARD), mean absolute error (MAE), root mean square error (RMSE), and Janus coefficient. Visual comparisons and statistical analyses indicated that the calibrated model could provide accurate predictions for the effluent chemical oxygen demand (COD), ammonia nitrogen (NH-N), total nitrogen (TN), and total phosphorus (TP), with only one iteration.
需要对厌氧-缺氧/硝化(A2N)双污泥系统的复杂机理模型进行系统的标定和验证。本文提出了一种基于阶段实验、敏感性分析和遗传算法的有效方法来进行模型标定。在 A2N 序批式反应器(SBR)中进行了阶段实验(厌氧磷释放、好氧硝化和缺氧反硝化磷积累),以准确反映工艺条件并提高模型标定效率。利用 30 批实验和 3 个月的动态连续流(CF)实验对 A2N-SBR 和 CF-A2N 工艺分别进行了模型验证。采用平均相对偏差(ARD)、平均绝对误差(MAE)、均方根误差(RMSE)和简纳斯系数等几个统计标准来评估模型预测的准确性。视觉比较和统计分析表明,标定后的模型仅需一次迭代即可对出水化学需氧量(COD)、氨氮(NH-N)、总氮(TN)和总磷(TP)进行准确预测。