三效催化转化器老化过程预测及数值仿真

基于三效催化转化器的老化机理,提出了老化过程的预测方法。然后将老化机理与化学反应机理相结合,建立包含老化过程的三效催化转化器反应模型,并进行数值仿真,得到三效催化转化器载体温度及氧浓度沿载体轴向的分布特性,最后计算得出预测三效催化转化器老化过程的主要参数以及老化前后转化效率的变化,并与实验值进行比较。     

车用三效催化转化器发展概况

从载体及三效催化剂角度论述了车用三效催化转化器的发展；指出了影响车用三效催化转化器性能的因素，这些因素的作用是协同的；为提高催化转化器的性能，必须采用系统的方法对其进行优化设计。     

三效催化转化器研究进展

综述了三效催化转化器,即汽车尾气催化转化器的研究进展,详细介绍了载体和三效催化剂的研究状况,对其存在的问题进行了探讨。     

三效催化转化器研究进展

综述了三效催化转化器,即汽车尾气催化转化器的研究现状,详细介绍了载体和三效催化剂的研究状况,对其存在的问题进行了探讨.     

三效催化转化器研究进展

综述了三效催化转化器,即汽车尾气催化转化器的研究现状,详细介绍了载体和三效催化剂的研究状况,对其存在的问题进行了探讨.     

车用催化转化器的数值模拟与实验研究

采用数值模拟和实验相结合的方法比较了常规结构和一种特殊结构双载体三效催化转化器的性能。数值模拟的结果表明,安装环形片的催化转化器流动均匀性优于常规结构的催化转化器。实验结果表明,安装环形片可以提高催化器的转化效率。     

车用催化转化器的数值模拟与实验研究

采用数值模拟和实验相结合的方法比较了常规结构和一种特殊结构双载体三效催化转化器的性能。数值模拟的结果表明，安装环形片的催化转化器流动均匀性优于常规结构的催化转化器。实验结果表明，安装环形片可以提高催化器的转化效率。     

三效催化转化器催化剂硫中毒机理

在一种详细的表面反应机理中加入了包含贵金属催化剂与助催化剂的硫中毒基元反应,建立了新的三效催化转化器催化反应机理。利用该机理进行了稳态工况数值模拟,得到了各排气污染物起燃温度与转化效率随尾气中SO2浓度的变化情况。采用灰色关联分析了SO2浓度与各排气组分转化效率的关联度,结果表明,SO2浓度对C3H6与NOx转化效率影响最大。对三效催化转化器的催化剂活性设计具有重要的参考价值,并对其工况设计及优化提供了指导。     

汽车尾气催化转化器模型研究进展

本文主要介绍汽车尾气催化转化器模型的研究进展 ,包括反应器内气体流动特性、单通道反应器内的传递现象和反应动力学、通道间的热传递和反应行为的模拟 ;并指出认识反应器内的各种特性和规律对设计汽车尾气催化转化器的必要性和意义     

汽车尾气催化转化器模型研究进展

本文主要介绍汽车尾气催化转化器模型的研究进展，包括反应器内气体流动特性，单通道反应器内的传递现象和反应动力学，通道间的热传递和反应行为的模拟，并指出认识反应器内的各种特性和规律对设计汽车尾气催化转化器的必要性和意义。     

基于BP模型的磷酸铵镁法除磷模拟研究

以实际试验数据为依据,通过网络结构优化建立了一个可用于磷酸铵镁法除磷模拟的BP神经网络模型。二维和三维的数值模拟表明,所建立的BP模型能够获得多因素条件下磷酸铵镁法除磷的变化规律和趋势,可为进一步的MAP法除磷及磷回收试验研究提供重要参考依据。     

Fuzzy neural network for flow estimation in sewer systems during wet weather.

Estimation of the water flow from rainfall intensity during storm events is important in hydrology, sewer system control, and environmental protection. The runoff-producing behavior of a sewer system changes from one storm event to another because rainfall loss depends not only on rainfall intensities, but also on the state of the soil and vegetation, the general condition of the climate, and so on. As such, it would be difficult to obtain a precise flowrate estimation without sufficient a priori knowledge of these factors. To establish a model for flow estimation, one can also use statistical methods, such as the neural network STORMNET, software developed at Lyonnaise des Eaux, France, analyzing the relation between rainfall intensity and flowrate data of the known storm events registered in the past for a given sewer system. In this study, the authors propose a fuzzy neural network to estimate the flowrate from rainfall intensity. The fuzzy neural network combines four STORMNETs and fuzzy deduction to better estimate the flowrates. This study's system for flow estimation can be calibrated automatically by using known storm events; no data regarding the physical characteristics of the drainage basins are required. Compared with the neural network STORMNET, this method reduces the mean square error of the flow estimates by approximately 20%. Experimental results are reported herein.     

大气环境质量的人工神经网络决策模型

为了对环境质量进行综合评价，运用误差反向传播算法的人工神经网络方法建立了环境质量评价的Ｂ－Ｐ决策模型。用此模型研究实例的大气环境质量，结果表明Ｂ－Ｐ网络用于环境质量评价具有客观性和实用性。     

基于改进型灰色神经网络组合模型的空气质量预测

基于空气质量数据不足及波动较大的情况,将灰色GM（1,1）模型与人工神经网络模型组合并改进,建立改进型灰色神经网络组合模型。利用天津市2001—2008年PM10、SO2和NO2年均值作为原始数据预测2009—2010年PM10、SO2和NO2的浓度以进行模型精度检验,最后利用该模型预测2011—2015年天津市空气质量状况。结果表明,与灰色GM（1,1）模型、传统灰色神经网络组合模型相比,所建立的改进型灰色神经网络组合模型相对模拟误差小,预测结果更为可靠,可以用于空气质量预测。     

Forecasting Daily Source Air Quality Using Multivariate Statistical Analysis and Radial Basis Function Networks

Abstract

It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.     

Forecasts using neural network versus Box-Jenkins methodology for ambient air quality monitoring data

This study explores ambient air quality forecasts using the conventional time-series approach and a neural network. Sulfur dioxide and ozone monitoring data collected from two background stations and an industrial station are used. Various learning methods and varied numbers of hidden layer processing units of the neural network model are tested. Results obtained from the time-series and neural network models are discussed and compared on the basis of their performance for 1-step-ahead and 24-step-ahead forecasts. Although both models perform well for 1-step-ahead prediction, some neural network results reveal a slightly better forecast without manually adjusting model parameters, according to the results. For a 24-step-ahead forecast, most neural network results are as good as or superior to those of the time-series model. With the advantages of self-learning, self-adaptation, and parallel processing, the neural network approach is a promising technique for developing an automated short-term ambient air quality forecast system.     

基于因子分析法优选河流水体中氮浓度预测方法的研究

定量的河流水体中氮浓度预测方法有很多种,如何优选出预测精度较高的方法一直是学术界多年来致力于研究的重点。本研究采用因子分析法对预测方法的精度评价指标进行分析,并建立了预测方法精度的评价模型,对回归分析法、神经网络法、灰色系统法和增长率统计法4种水体氮浓度预测方法进行综合评估,优选出精度较高的河流水体氮浓度预测模型——BP神经网络预测模型。结果表明,此评估模型对类似研究具有一定的参考价值,能为选择出合适的河流水体氮浓度预测方法提供依据。     

神经网络在次级河流回水区叶绿素a浓度预测中的应用

以长江次级河流之一的临江河为研究对象,探讨神经网络应用于次级河流回水区叶绿素a浓度短期预测的可行性。利用主成分分析法（PCA）选取对叶绿素a浓度影响较大的指标,在这些指标数据的基础上建立RBF神经网络模型。网络训练和测试的结果表明,模型模拟精度较高,说明RBF神经网络模型可以用于次级河流回水区叶绿素a浓度的短期预测。通过对临江河回水区叶绿素a影响因子的分析,表明控制水体中磷含量应是临江河回水区富营养化防治的重点。     

ANFIS在混凝投药前馈控制器中应用的仿真研究

在水处理中混凝投药前馈控制器的应用效果好坏关键在于控制器是否对混凝投药过程具有良好的模型辨识能力,传统的控制器效果都不太理想,而且存在沉淀池出水浊度波动大,药剂浪费严重等问题。为了解决该问题,介绍了一种用多层前馈神经网络优化模糊逻辑系统的自适应模糊推理系统——ANFIS。它具有良好的非线性函数逼近能力,在ANFIS投药前馈控制器的设计中,运用减法聚类对样本数据进行空间划分,获取初始模糊隶属函数和模糊规则,得到ANFIS模型的初始结构。用烧杯试验历史数据进行了仿真验证,并与传统的回归模型前馈投药控制仿真比较,结果表明ANFIS投药前馈控制模型明显优于回归模型,它能够根据原水水质适时有效预测混凝投药量。     

基于人工蜂群算法与BP神经网络的水质评价模型

针对BP网络水质评价模型的不足,引入人工蜂群(ABC)算法,将求解BP神经网络各层权值、阀值的过程转化为蜜蜂寻找最佳蜜源的过程,提出了一种新的结合人工蜂群算法的BP网络水质评价方法(ABC-BP)。并以2000—2006年渭河监测断面的10组实测数据作为测试样本对其水质进行了评价,实验结果表明该方法得到的水质评价结果准确,并具有很强的稳定性和鲁棒性。