Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E., Malaysia.
J Hazard Mater. 2011 Aug 30;192(2):568-75. doi: 10.1016/j.jhazmat.2011.05.052. Epub 2011 May 23.
A membrane sequencing batch reactor (MSBR) treating hypersaline oily wastewater was modeled by artificial neural network (ANN). The MSBR operated at different total dissolved solids (TDSs) (35,000; 50,000; 100,000; 150,000; 200,000; 250,000mg/L), various organic loading rates (OLRs) (0.281, 0.563, 1.124, 2.248, and 3.372kg COD/(m(3)day)) and cyclic time (12, 24, and 48h). A feed-forward neural network trained by batch back propagation algorithm was employed to model the MSBR. A set of 193 operational data from the wastewater treatment with the MSBR was used to train the network. The training, validating and testing procedures for the effluent COD, total organic carbon (TOC) and oil and grease (O&G) concentrations were successful and a good correlation was observed between the measured and predicted values. The results showed that at OLR of 2.44kg COD/(m(3)day), TDS of 78,000mg/L and reaction time (RT) of 40h, the average removal rate of COD was 98%. In these conditions, the average effluent COD concentration was less than 100mg/L and met the discharge limits.
采用人工神经网络（ANN）对处理高盐含油废水的膜序批式反应器（MSBR）进行建模。MSBR 在不同总溶解固体（TDS）（35000、50000、100000、150000、200000、250000mg/L）、不同有机负荷率（OLR）（0.281、0.563、1.124、2.248 和 3.372kg COD/（m³·d））和循环时间（12、24 和 48h）下运行。采用批处理反向传播算法训练的前馈神经网络用于对 MSBR 进行建模。使用 193 组来自 MSBR 处理废水的运行数据对网络进行训练。对出水 COD、总有机碳（TOC）和油和油脂（O&G）浓度的训练、验证和测试过程是成功的，并且观察到测量值和预测值之间存在良好的相关性。结果表明，在 OLR 为 2.44kg COD/（m³·d）、TDS 为 78000mg/L 和反应时间（RT）为 40h 的条件下，COD 的平均去除率为 98%。在这些条件下，出水 COD 浓度的平均值小于 100mg/L，符合排放标准。
