【摘要】 采用CSTR型产酸-硫酸盐还原反应器处理高浓度硫酸盐废水,根据原型试验,利用带动量的自适应学习速率梯度下降算法,建立BPNN模型,预测碳硫比(C/S),硫酸盐负荷率(Ns),pH值和碱度(ALK)4个关键生态因子对硫酸盐去除率(η)的影响.在此基础上,采用信息流(IF),分割连接权值(PCW)和偏导数(PaD)3种方法,定量化分析网络各层神经元的连接权值,从而明确了引发微生物群落生态演替全过程的主导因子是C/S.不同的生态因子在生态演替的3个阶段所起的主导作用各不相同.pH值是演替阶段I的主导因子,C/S是演替阶段II的主导因子,Ns是演替阶段III的主导因子.更多还原

【Abstract】 Adopting acidogenic sulfate-reducing reactor for treating high concentration sulfate wastewater, based on the prototype experiment utilizing a algorithm of gradient descent with momentum and adaptive learning rate backpropagation a back-propagation neural network (BPNN) model was established to predict the influence of four key ecological factors of COD/SO42- ratio (C/S), sulfate loading rate (Ns), pH value and alkalinity (ALK) on sulfate removal rate (η). Based on this, three methods of information flow, partitioning connection weights (PCW) and partial derivatives (PaD) were adopted to analyze quantitatively the connection weights between neurons in the different layers of network; thus, that the dominant factors initiating the entire course (stage I + stage II + stage III) of the microbial community ecological succession was C/S. while the roles of different ecological factors in these three stages were different. pH value was the dominant factors of stage I; C/S was that of stage II, and Ns was that of the stage III.更多还原