• 关键词：ANFIS  ANN  蠕虫反应器  污泥减量  模型

• 基金项目：国家自然科学基金(No.50821002);国家创新研究群体项目

• 田禹
• 1. 哈尔滨工业大学城市水资源与水环境国家重点实验室, 哈尔滨 150090;
  2. 哈尔滨工业大学市政环境工程学院, 哈尔滨 150090

• 张帅
• 哈尔滨工业大学市政环境工程学院, 哈尔滨 150090

• 陈琳
• 哈尔滨工业大学市政环境工程学院, 哈尔滨 150090

• 张赛
• 辽宁大学环境学院, 沈阳 110036

• 摘要：利用水生蠕虫的捕食作用可以有效地实现污泥减量.为了研究环境条件波动对蠕虫捕食污泥减量效率的影响,应用自适应神经模糊推理系统(ANFIS)和人工神经网络(ANN)模型分别预测蠕虫反应器的污泥减量速率.结果表明,溶解氧浓度(DO)、温度(T)、蠕虫密度和污泥负荷是蠕虫捕食过程的主要影响因素,通过性能比较得出ANFIS模型预测值与实验测定值间具有更好的一致性,其相关系数(r)为0.82,绝对平均误差百分比(MAPE)为71.5%,均方根误差(RMSE)为16.7.根据ANFIS模型的预测结果,得出蠕虫反应器的最适运行条件为:DO 1.8~3.1 mg·L^-1,温度18.4~21.7℃,蠕虫密度低于1.7 g·cm^-2(以湿重计),污泥负荷563~734 mg·g^-1(以TSS计),在此操作条件下获得的污泥减量速率均高于100 mg·g^-1·d^-1.

• Abstract：Aquatic worms' predation is an effective process for ecological sludge reduction, but the sludge reduction efficiency can be affected by the fluctuation of operational conditions. In this study, dissolved oxygen (DO) concentration, temperature, worm density and sludge load were identified to be the main parameters in sludge consumption process. With these parameters, the adaptive neurofuzzy inference system (ANFIS) and artificial neural network (ANN) were employed to predict the sludge consumption rate in the worm reactor. Compared with the ANN model, the ANFIS model was proved to be more effective with the correlation coefficient of 0.82, the mean absolute percentage error of 71.5% and the root mean square error of 16.7. Based on the ANFIS model, the optimal operation conditions were found to be DO of 1.8~3.1 mg·L^-1, temperature of 18.4~21.7℃, worm density below 1.7 g·cm^-2 and sludge load of 563~734 mg·g^-1. Under these conditions, the sludge consumption rate can be maintained above 100 mg·g^-1·d^-1.

• Key words：adaptive neurofuzzy inference system  artificial neural network  worm reactor  sludge reduction  modeling

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