摘要
以两相厌氧工艺中产甲烷相(UASBAF反应器)处理中药废水为原型,采用带动量、自适应学习率的快速BP收敛算法,建立人工神经网络模型,并利用分离相关权值法对关键性调控因子(pH、进水COD、碱度、HRT)对反应器运行效果的影响大小进行排序,发现pH是决定系统运行效果的限制性因子,在高运行负荷阶段其限制性尤为明显;在所建模型的基础上,通过分步固定调控因子的方法实现双因子联合作用三维谱图,直观、定性地分析各调控因子对系统运行效果的影响过程,并得到一系列的运行调控对策来优化反应器的运行条件,突破了传统人工神经网络担任预测工具的角色,为其在反应器的运行调控中优势的进一步发挥提供有效途径.
Based on the prototype experiment of UASBAF as the methanogenic phase of two - phase anaerobic treating herb wastewater, adopting a fast convergence backpropagation algorithm with momentum and adaptive learning rate, an artificial neural network （ ANN ） model was established. Besides, comparison was conducted using method of partitioning connection weights to investigate influence of key factors（ pH, COD, ALK, HRT） to the performance of the reactor. As a result, pH value is found to be the most important controlling parameter to maintain the system performance especially under high loading conditions. On the ground of model built, by fractionally fixing affecting factors tridimensional figures with two conjoint affecting factors were plotted to analyze ways of input parameters affecting the reactor visually and qualitatively. And ultimately a series of strategies were proposed to optimize the working condition of the system, which broke through the traditional predicting role of ANN and provided an effective way of exploiting advantage of ANN in controlling reactors.
出处
《哈尔滨工业大学学报》
EI
CAS
CSCD
北大核心
2007年第10期1564-1568,共5页
Journal of Harbin Institute of Technology
基金
国家高技术研究发展计划资助项目(863-2002AA601310)
国家自然科学基金重大国际合作项目(50521140075)
