基于神经网络的活性污泥系统建模及其控制

针对松江污水厂污水处理活性污泥系统,采用神经网络技术进行建模试验研究,在对实际运行数据剔除异常数据后,将样本数据随机分成训练样本、检验样本和测试样本.用试凑法确定合理的神经网络隐层节点数,用检验样本实时监控训练过程从而避免"过训练"现象,用多次改变网络初始连接权值求得全局极小点,从而建立了泛化能力较好的基于神经网络的活性污泥系统数学模型.利用建立的神经网络模型,对活性污泥系统运行情况的仿真与控制进行了分析研究.示例研究表明:神经网络技术能较好地应用于活性污泥系统的建模与控制,有很好的理论与实践意义.     

活性污泥系统神经网络建模与仿真研究

采用神经网络技术对松江污水厂污水处理活性污泥系统进行建模试验研究，在对实际运行数据按机理准则和范围准则剔除异常数据后，将样本数据随机分成训练样本、检验样本和测试样本。用试凑法确定合理的神经网络隐层节点数，以避免采用过大或过小的网络结构，在训练过程中用检验样本实时监控从而避免“过训练”现象的影响，较好地解决神经网络方法建模的两大难题，从而建立可靠、有效的活性污泥系统神经网络模型。并应用建立的网络模型对活性污泥系统的运行情况进行了仿真研究。建模研究表明，神经网络技术能较好地应用于活性污泥系统的建模，模型具有较好的泛化能力，有很好的实用价值。     

基于BP和RBF神经网络的外加碳源量模型研究

碳源作为反硝化过程的电子供体,是影响生物脱氮过程的重要因素,低碳氮比污水需外加碳源以保证反硝化反应的顺利进行。为了优化控制碳源投加量,对实验室搭建的CAST工艺污水处理装置的进水条件和外加碳源量的非线性关系分别进行了基于BP和RBF神经网络的模型研究,并对外加碳源量进行了预测。结果表明,两种网络模型均能有效预测外加碳源量,RBF神经网络模型在训练速度和逼近能力方面优于BP神经网络模型,但在预测性能方面BP神经网络模型则有更高的预测精度。     

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。     

基于BP网络的水质综合评价模型及其应用

讨论了BP网络模型存在的不足及建模条件,提出了建立合理的BP网络模型的基本原则和步骤.针对水质评价问题,通过在各类水质污染指标浓度区间内生成随机分布样本的方法,组成足够多用于BP网络训练、检验和测试用的样本,建立了辽河水质综合评价的BP网络模型;给出了区分不同类别水质的模型分界值样本和模型输出分界值.     

基于IGA-BP网络的水质预测方法

针对水质预测中传统BP神经网络模型收敛速度慢,对隐层结点选取缺乏有效的手段等问题,引入了遗传算法优化BP网络的结构和隐层神经元阈值和连接权值,通过设计灵活的实数编码方案和新型交叉算子等,对实数编码遗传算法进行改进,在此基础上,提出了一种基于改进的实数编码遗传算法优化BP神经网络(IGA-BP)的水质预测新模型,并以安徽蚌埠蚌埠闸逐周水质监测的PH值数据为例,进行水质预测,通过与传统的GA-BP神经网络以及BP神经网络的水质预测模型对比,结果表明,这种预测方法训练的BP神经网络收敛速度快,样本逼近精度高且泛化能力强。     

基于神经网络的活性污泥系统建模及其控制

针对松江污水厂污水处理活性污泥系统，采用神经网络技术进行建模试验研究，在对实际运行数据剔除异常数据后，将样本数据随机分成训练样本、检验样本和测试样本。用试凑法确定合理的神经网络隐层节点数，用检验样本实时监控训练过程从而避免“过训练”现象，用多次改变网络初始连接权值求得全局极小点，从而建立了泛化能力较好的基于神经网络的活性污泥系统数学模型。利用建立的神经网络模型，对活性污泥系统运行情况的仿真与控制进行了分析研究。示例研究表明：神经网络技术能较好地应用于活性污泥系统的建模与控制，有很好的理论与实践意义。     

深圳市区空气污染的人工神经网络预测

利用深圳市2006至2013年的大气污染物监测浓度数据和气象资料,分析深圳市空气质量的逐月分布变化特征。采用Pearson相关分析,选择显著相关因子,分别以BP神经网络和RBF神经网络构建空气质量预测模型,对该市2013年SO2、NO2、PM103种空气污染物的月均值进行预测。实验结果表明,通过Pearson相关分析建立的预测模型有更高的预报精度。BP和RBF 2种网络预测效果都比较理想,对不同污染物的预测精度各有高低。但BP网络的构建和参数优化过程较为复杂且网络训练结果不稳定,而RBF网络构建和训练简单,时间短而结果稳定。在综合性能上,RBF网络用于环境空气污染物浓度的预测具有更强的适用性。     

遗传神经网络对肼类化合物的定量构效研究

应用遗传算法和反向传播（BP）神经网络相结合的方法，研究了胼类化合物的定量构效（QSAR）关系，构建了遗传神经网络QSAR模型。对30种肼类化合物的6个量子化学参数进行相关性和主成分分析，利用遗传神经网络QSAR模型对肼类化合物的毒性参数进行预测。结果表明，与常规BP神经网络建立的模型相比较，遗传神经网络QSAR模型有效解决了常规BP神经网络模型存在的过训练和过拟合问题，并且具有很好的预测效果。     

基于BP网络的水质综合评价模型及其应用

讨论了BP网络模型存在的不足及建模条件，提出了建立合理的BP网络模型的基本原则和步骤。针对水质评价问题，通过在各类水质污染指标浓度区间内生成随机分布样本的方法，组成足够多用于BP网络训练、检验和测试用的样本，建立了辽河水质综合评价的BP网络模型；给出了区分不同类别水质的模型分界值样本和模型输出分界值。     

An extended self-organizing map network for modeling and control of pulse jet fabric filters

Pulse jet fabric filters (PJFFs) have become an attractive option of particulate collection utilities, because they can meet stringent particulate emission limits regardless of variation in operating conditions. Despite their wide applications, the present control algorithm for PJFFs can best be described as rudimentary. In this paper, a modeling and control strategy based on the local model network (LMN) is proposed. An extended self-organizing map (ESOM) network is developed to construct the LMN model of the filtration process using the filter's input-output data. Subsequently, these ESOM local models are incorporated into the design of local generalized predictive controllers (GPC), and the proposed controller design is obtained as the weighted sum of these local controllers. Simulation results show that the proposed controller design yields a better performance than both conventional GPC and proportional plus integral (PI) controllers yield.     

Actors and arenas in hybrid networks: implications for environmental policymaking in the Baltic Sea region

Policymaking within and among states is under pressure for change. One feature of this change is empirically observed as an activation of different network structures in the Baltic Sea Region, especially since the collapse of the Iron Curtain, the initiation of the Rio process, and the enlargement of the European Union. The contemporary theoretical debates about governance highlight the changing conditions for policymaking and implementation on all societal levels. This process of change, especially evident concerning environmental policies, includes new types of networks crossing state borders both at the supranational and the subnational levels. This article illuminates this process of change with empirical data from the project "Governing a Common Sea" (GOVCOM) within the Baltic Sea Research Program (BIREME).     

Revised Air Quality Surveillance Requirements for State Implementation Plans

The National Ambient Air Quality Monitoring Program is carried out by state and local air pollution control agencies in support of their State Implementation Plans (SIP’s). The current EPA regulations which specify the characteristics of these state monitoring programs are undergoing change as a result of a comprehensive review by an independent work group. These revised regulations, which are described in the paper, are intended to improve the quality, timeliness, and usability of the data generated by the states for all data users. In addition, the revised regulations seek to bring about; (a) national consistency in monitoring site locations through standardized siting procedures; (b) improved network operations by means of a minimum quality assurance program; (c) reduced network inflexibility through an annual network review process; and (d) reduced data reporting through changed data reporting procedures.     

An Extended Self-Organizing Map Network for Modeling and Control of Pulse Jet Fabric Filters

ABSTRACT

Pulse jet fabric filters (PJFFs) have become an attractive option of particulate collection utilities, because they can meet stringent particulate emission limits regardless of variation in operating conditions. Despite their wide applications, the present control algorithm for PJFFs can best be described as rudimentary. In this paper, a modeling and control strategy based on the local model network (LMN) is proposed. An extended self-organizing map (ESOM) network is developed to construct the LMN model of the filtration process using the filter's input-output data. Subsequently, these ESOM local models are incorporated into the design of local generalized predictive controllers (GPC), and the proposed controller design is obtained as the weighted sum of these local controllers. Simulation results show that the proposed controller design yields a better performance than both conventional GPC and proportional plus integral (PI) controllers yield.     

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.     

Identification and Control of NOx Emissions Using Neural Networks

ABSTRACT

We investigate the application of two classes of artificial neural networks for the identification and control of discrete-time nonlinear dynamical systems. A fully connected recurrent network is used for process identification, and a multilayer feedforward network is used for process control. The two neural networks are arranged in series for closed-loop control of oxides of nitrogen (NO_x) emissions of a simplified representation of a dynamical system. Plant data from one of Commonwealth Edison's coal-fired power plants are used for testing the approach, with initial results indicating that the method is feasible. However, further work is required to determine whether the method remains feasible as the number of state variables and control variables are increased.     

Assessing spatial variability of SO2 field as detected by an air quality network using Self-Organizing Maps,cluster, and Principal Component Analysis

In Bilbao (Spain), an air quality network measures sulphur dioxide levels at 4 locations. The objective of this paper is to develop a practical methodology to identify redundant sensors and evaluate a network's capability to correctly follow and represent SO₂ fields in Bilbao, in the frame of a continuous network optimization process.The methodology is developed and tested at this particular location, but it is general enough to be useable at other places as well, since it is not tied neither to the particular geographical characteristics of the place nor to the phenomenology of the air quality over the area.To assess the spatial variability of SO₂ measured at 4 locations in the area, three different techniques have been used: Self-Organizing Maps (SOMs), cluster analysis (CA) and Principal Component Analysis (PCA). The results show that the three techniques yield the same results, but the information obtained via PCA can be helpful not only for that purpose but also to throw light on the major mechanisms involved. This might be used in future network optimization stages. The main advantage of cluster analysis and SOMs is that they provide readily interpretable results. All the calculations have been carried out using the freely available software R.     

Assessment and rationalization of water quality monitoring network: a multivariate statistical approach to the Kabbini River (India)

The establishment of an efficient surface water quality monitoring (WQM) network is a critical component in the assessment, restoration and protection of river water quality. A periodic evaluation of monitoring network is mandatory to ensure effective data collection and possible redesigning of existing network in a river catchment. In this study, the efficacy and appropriateness of existing water quality monitoring network in the Kabbini River basin of Kerala, India is presented. Significant multivariate statistical techniques like principal component analysis (PCA) and principal factor analysis (PFA) have been employed to evaluate the efficiency of the surface water quality monitoring network with monitoring stations as the evaluated variables for the interpretation of complex data matrix of the river basin. The main objective is to identify significant monitoring stations that must essentially be included in assessing annual and seasonal variations of river water quality. Moreover, the significance of seasonal redesign of the monitoring network was also investigated to capture valuable information on water quality from the network. Results identified few monitoring stations as insignificant in explaining the annual variance of the dataset. Moreover, the seasonal redesign of the monitoring network through a multivariate statistical framework was found to capture valuable information from the system, thus making the network more efficient. Cluster analysis (CA) classified the sampling sites into different groups based on similarity in water quality characteristics. The PCA/PFA identified significant latent factors standing for different pollution sources such as organic pollution, industrial pollution, diffuse pollution and faecal contamination. Thus, the present study illustrates that various multivariate statistical techniques can be effectively employed in sustainable management of water resources. Highlights ? The effectiveness of existing river water quality monitoring network is assessed ? Significance of seasonal redesign of the monitoring network is demonstrated ? Rationalization of water quality parameters is performed in a statistical framework     

Sampling frequency guidance for ambient air toxics monitoring

The U.S. Environmental Protection Agency (EPA) is in the process of designing a national network to monitor hazardous air pollutants (HAPs), also known as air toxics. The purposes of the expanded monitoring are to (1) characterize ambient concentrations in representative areas; (2) provide data to support and evaluate dispersion and receptor models; and (3) establish trends and evaluate the effectiveness of HAP emission reduction strategies. Existing air toxics data, in the form of an archive compiled by EPA's Office of Air Quality Planning and Standards (OAQPS), are used in this paper to examine the relationship between estimated annual average (AA) HAP concentrations and their associated variability. The goal is to assess the accuracy, or bias and precision, with which the AA can be estimated as a function of ambient concentration levels and sampling frequency. The results suggest that, for several air toxics, a sampling schedule of 1 in 3 days (1:3) or 1:6 days maybe appropriate for meeting some of the general objectives of the national network, with the more intense sampling rate being recommended for areas expected to exhibit relatively high ambient levels.