| 基于化学反应动力学的饮用水铝形态分布模型研究 |
| 摘要点击 2727 全文点击 3064 投稿时间：2009-06-15 修订日期：2009-07-30 |
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| 中文关键词 反应动力学 饮用水 铝形态 人工神经网络 模型 |
| 英文关键词 reaction kinetics drinking water aluminum species artificial neural network model |
| DOI 10.13227/j.hjkx.20100421 |
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| 中文摘要 |
| 目前饮用水中的总铝超标现象十分严重，其危害与铝的存在形态密切相关.本研究利用三层前反馈式的人工神经网络技术，建立了基于化学反应动力学的铝形态预测模型.结果表明，无机单核铝和溶解铝的浓度变化速率与反应时间及水温、pH、总铝、氟离子、磷酸根和硅酸根等水质参数密切相关，二者的反应级数均为三级.通过人工神经网络可有效地进行饮用水中无机单核铝和溶解铝反应动力学参数的预测；反应速率常数K和初始浓度项1/c02的计算值和模型预测值的相关系数R均大于0.999.由M市管网水铝形态的预测结果可知：当总铝浓度<0.05 mg·L-1时，模型对无机单核铝浓度的预测误差较大；而当总铝浓度>0.05 mg·L-1时，模型有较好的预测能力，无机单核铝和溶解铝的相对预测误差分别为±15%和±10%. |
| 英文摘要 |
| The effects of excess aluminum on water distribution system and human health were mainly attributable to the presences of some aluminum species in drinking water. A prediction model for the concentrations of aluminum species was developed using three-layer front feedback artificial neural network method. Results showed that the reaction rates of both inorganic monomeric aluminum and soluble aluminum varied with reaction time and water quality parameters, such as water temperature, pH, total aluminum, fluoride, phosphate and silicate. Their reaction orders were both three. The reaction kinetic parameters of inorganic monomeric aluminum and soluble aluminum could be predicted effectively applying artificial neural network; the correlation coefficients of K and 1/c02 between calculated value and predicted value were both greater than 0.999. Aluminum species prediction results in the drinking water of City M showed that when the concentration of total aluminum was less than 0.05 mg·L-1, the relative prediction error was large for inorganic monomeric aluminum. When the concentration of total aluminum was above 0.05 mg·L-1, the model could predict inorganic monomeric aluminum and soluble aluminum concentrations effectively, with relative prediction errors of ±15% and ±10% respectively. |
