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污水生物-生态协同处理系统的协同优化
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Collaborative Optimization of Biological and Ecological Combined Process for Municipal Sewage Treatment
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    摘要:

    为降低生物-生态协同污水处理系统的能耗,提出的生物-生态协同优化方法包括两方面内容:一是针对不同季节,通过动态分配生物、生态单元的处理负荷,将生物单元出水浓度控制为生态单元的允许进水浓度,从而降低生物单元的处理负荷、降低系统能耗;二是针对生物处理单元,采用最优化方法,通过引入氧开关函数,定量反应DO对最大比降解速率的影响,建立了以DO为控制变量、出水达标排放为约束条件、生物单元曝气能耗最小为目标函数的最优化方程.并以某污水处理厂春季某天为例,通过求解最优化方程,得到维持生物单元DO值4.58 mg/L、连续曝气2.3 h,耗能31.7 kWh,即可去除污染物,与单独运行生物单元相比,可缩短曝气时间1.7 h,节电28.3 kWh,节能效果显著.最后,用某污水厂春季30天运行实测数据验证了该最优化方法的有效性.

    Abstract:

    In order to decrease the energy consumption in municipal sewage treatment process, a collaborative optimization method of biological and ecological combined process was proposed. Collaborative optimization included two main aspects. On the one hand, to reduce the treatment load and energy consumption, the effluent concentration of biological unit was controlled as the permitted influent concentration of ecological unit by distributing the pollutant load dynamically between biological and ecological treatment units in different seasons. On the other hand, in the biological unit, an optimal equation was established with DO concentration, up-to-standard discharge and minimum energy consumption of aeration as the control variable, constraint condition and objective function, respectively. The equation adopted optimization method, and oxygen switch function was introduced to reflect the influence of DO concentration on the maximum specific rate of pollutant degradation quantitatively. Furthermore, by solving the optimal equation in a sewage treatment plant, the pollutants could be removed after 2.3 hours' aeration in the condition of keeping DO value at 4.58 mg/L, and the energy consumption was 31.7 kWh. It shortened the aeration time of 1.7 h and saved energy of 28.3 kWh compared with operating biological unit only, which suggests remarkable energy-saving effect. Finally, the effectiveness of the optimization method was verified by one month's data of a sewage treatment plant.

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卿晓霞,郭庆辉,周健,王诚,史孟彬.污水生物-生态协同处理系统的协同优化[J].湖南大学学报:自然科学版,2014,41(12):96~101

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  • 在线发布日期: 2015-01-05
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