Empirical mathematical models and artificial neural networks for the determination of alum doses for treatment of southern Australian surface waters
Empirical mathematical models and artificial neural networks for the determination of alum doses for treatment of southern Australian surface waters
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用于确定处理澳大利亚南部地表水的明矾剂量的经验数学模型和人工神经网络
DOI：
10.1046/j.1365-2087.1999.00135.x
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发表时间：
1999
影响因子：
4.3
通讯作者：
M. Drikas
中科院分区：
文献类型：
--
作者：
J. Leeuwen;C. Chow;D. Bursill;M. Drikas
研究了基于原水的物理化学参数预测南澳大利亚地表水明矾剂量的可能性。这些参数包括溶解有机碳、在2 54 nm处的吸光度、浊度和碱度。用于评估明矾剂量的可预测性的程序是经验数学模型和人工神经网络。
通过烧杯试验确定的明矾剂量是根据沉淀和过滤后的浊度、颜色和残留铝的目标值来选择的。
回归方程综合了DOC值、紫外光吸光度(2 5 4 nm/cm)、浊度、碱度和pH等参数，相关系数均大于0.9。这些方程在实际剂量的±10 mg/L明矾内有较高的预测频率。同样，人工神经网络预测的氧化铝剂量中，86%的剂量与实际剂量相差不超过10 mg/L。虽然已经实现了对混凝剂投加量的良好预测，但生成的模型很可能是针对所研究的水的类型和明矾剂投加量的选择标准而特定的。
The potential for predicting alum doses for surface waters from southern Australia based on physico-chemical parameters of the raw waters was studied. These parameters included dissolved organic carbon (DOC), absorbance at 254 nm, turbidity and alkalinity. Procedures used for assessing the predictability of alum dosing were empirical mathematical models and artificial neural networks.
Alum doses determined by jar tests were selected on the basis of target values for settled and filtered turbidities, colour and residual aluminium.
Regression equations which incorporated the parameters of DOC, UV absorbance (254 nm/cm), turbidity, alkalinity and pH gave correlation coefficients of greater than 0.9. These equations gave a high frequency of prediction within ±10 mg/L alum of actual doses. Similarly, 86% of alum doses predicted by artificial neural networks were within 10 mg/L of the actual doses. Although a good prediction of coagulant dosing was achieved, it is likely that the models generated are specific for the types of waters studied and the criteria for alum dose selection.