湖泊富营养化模型的研究进展

湖泊的富营养化是全球普遍关注的环境问题之一.湖泊的富营养化模型是防治、修复和治理湖泊富营养化的重要决策工具.按研究的侧重点不同,将湖泊富营养化模型分为简单回归模型、水质模型、生态模型和生态-水动力水质模型,并分别回顾了四类模型的研究进展.最后指出湖泊富营养化模型的发展趋势,强调不确定理论、3S技术、耦合模型是今后湖泊富营养化模型研究的重点,应在此基础上建立通用的模拟、预测、评价和优化模型,为湖泊富营养化管理提供科学依据.     

公路交通噪声预测模型FHWA与RLS90的比较

为了了解我国长期以来使用的两种公路交通噪声预测模型,即美国联邦公路局(FHWA)模型和德国RLS90模型的预测精确性,通过理论分析,比较了RLS90分段模型、RLS90长直线模型、FHWA模型的差异,并通过实际测量值对3种模型的计算结果进行了验证,表明RLS90模型在高速公路交通噪声预测中略优于FHWA模型。     

模拟降解去除人工湿地营养物

人工湿地能有效去除水中的营养物,作为净化水质的方法之一,正得到广泛的应用.同时,尝试建立数学模型模拟去污过程,预测处理效果,并为湿地的运行管理提供理论支持.综合比较了目前常用的4种模型:回归方程模型、一级动力学模型、Monod模型和生态动力学模型,从分析各模型的原理出发,提出了各类模型的优缺点及适用条件,对湿地模型的建立具有指导作用.     

水质模型、生态模型及计算机模型软件

本文系统地介绍了有关水环境主要的形态模型、表面络合模型、点源和非点源污染模型及其计算机模型软件,并着重阐述了生态模型的发展及应用等     

干法脱硫非均匀初始孔隙率数学模型

分析了晶粒模型的优缺点,并针对晶粒模型的缺陷提出了一种晶粒模型的改进模型。改进后的模型考虑到颗粒内部初始孔隙率的非均匀性,建立了一个初始孔隙率沿半径变化的单颗粒脱硫数学模型。     

膜生物反应器污水处理数学模型研究及其应用现状

膜生物反应器(MBR)具有与传统工艺无法比拟的优势而成为污水处理中很有前景的工艺。由于MBR工艺本身固有的复杂性和不确定性,发展一种能够为该工艺提供整体性理解的模型是相当必要的。模型的建立可为MBR污水处理的优化设计和运行控制提供理论依据。给出了3类用于模拟MBR工艺的模型:生物动力学模型、膜污染模型和对MBR完整描述的综合模型。在此基础上,阐述了每个模型各自的机制和功能,分析讨论了模型研究与应用中存在的问题,并提出了MBR相关模型的发展前景。     

非点源污染模型参数数字化及不确定性研究进展

非点源污染模型是非点源污染量化研究的重要内容.目前,非点源污染模型数量繁多,集总模型不考虑时空变异性,适用流域面积小;分布式模型利用网格划分流域,可以模拟时空变异性,但参数繁多、率定困难、精度达不到要求、难以收集与管理.而全球定位系统、地理信息系统和遥感(合称3S)技术的应用,可以解决参数的选择问题,减少模型中的不确定成分.因此,在未来的非点源污染模型研究中,应重点关注利用3S技术解决参数的选择问题,以及模型参数的敏感性分析和不确定性分析.概述了非点源污染模型的研究进展,重点介绍了3S技术在非点源污染模型中的应用和非点源污染模型中的不确定性分析.     

酸化模型及其发展趋势评述

本文将酸化模型分为指标评价模型、经验酸化模型和以湖泊－流域为基础的机理模型三大类。概述了几个典型的酸化模型的结构、对参数和过程的处理方法、存在的问题和应用范围并介绍了它们的一些具体应用。还以ＭＡＧＩＣ模型为例说明了酸化模型的发展及其趋势。作为模型的一个有机组成部分,本文还阐述了酸化模型的不确定性分析。     

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。     

基于人工神经网络的半连续式混合厌氧消化产气量预测

研究采用BP神经网络和模糊神经网络(FNN)模型对逐步提高有机负荷的半连续式餐厨垃圾和猪粪混合厌氧消化试验进行日产气量预测.结果表明,BP神经网络模型的预测准确率为77.63％,FNN模型为82.33％,2种模型均可用于产气预测,但FNN模型在传统神经网络模型基础上加入了模糊控制,可提高其准确率,更适用于混合厌氧消化产气量预测.     

基于智能优化算法的河流水质模型参数的优化

为探索河流水质模型参数新的求解方法,根据有限的实测数据,分别应用免疫进化优化算法和免疫进化优选的捕食搜索算法,对河流水质模型计算公式中的多参数进行优化。将优化得到的计算公式用于国内外若干河流的河段中DO浓度值的拟合,并与实测结果进行了比较。结果表明,将免疫进化优化算法或免疫进化优选的捕食搜索算法优化得到的水质模型参数精度不仅较高,而且相对稳定,从而为河流水质模型参数的优化提供了一种新方法。     

基于智能优化算法的河流水质模型参数的优化简

为探索河流水质模型参数新的求解方法,根据有限的实测数据,分别应用免疫进化优化算法和免疫进化优选的捕食搜索算法,对河流水质模型计算公式中的多参数进行优化。将优化得到的计算公式用于国内外若干河流的河段中DO浓度值的拟合,并与实测结果进行了比较。结果表明,将免疫进化优化算法或免疫进化优选的捕食搜索算法优化得到的水质模型参数精度不仅较高,而且相对稳定,从而为河流水质模型参数的优化提供了一种新方法。     

免疫粒子群算法优化的环境空气质量评价方法

为了提高免疫算法的收敛速度,将粒子群优化思想引入到免疫算法中,设计了一种免疫粒子群优化算法。采用该算法对大气污染损害公式的参数进行寻优,得到了适用于臭氧、PM2．5等6种大气污染物的环境空气质量评价的污染损害指数公式及环境空气质量评价模型。为了使评价结果更准确,采用了国家环保部最新发布的空气质量标准中给出的大气污染物种类、数目及各级浓度限值。将该评价方法应用于大气质量评价领域,实验结果表明,该方法评价结果准确,具有较好的灵活性、实用性和应用前景。     

地下水水质评价的逻辑斯谛曲线模型

为检验已订的地下水水质评价标准的合理性，解决各单项水质指标的评估结果的不相容性问题，提高水质等级模型的分辨率，提出了一种新的水质评价模型－逻辑斯谛曲线模型，它的水质等级是连续的实数值。根据LOG模型的参数值可以分析各水质指标值对水质等级的影响程度，据此检验原订水质等级标准的合理性。并给出了基于实码遗传算法的LOG建模的实施方案。实例研究的结果说明，该方案是可行、有效且通用的，在环境质量综合评价中具有广泛的应用价值。     

Support vector machine―an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river?

Water quality forecasting in agricultural drainage river basins is difficult because of the complicated nonpoint source (NPS) pollution transport processes and river self-purification processes involved in highly nonlinear problems. Artificial neural network (ANN) and support vector model (SVM) were developed to predict total nitrogen (TN) and total phosphorus (TP) concentrations for any location of the river polluted by agricultural NPS pollution in eastern China. River flow, water temperature, flow travel time, rainfall, dissolved oxygen, and upstream TN or TP concentrations were selected as initial inputs of the two models. Monthly, bimonthly, and trimonthly datasets were selected to train the two models, respectively, and the same monthly dataset which had not been used for training was chosen to test the models in order to compare their generalization performance. Trial and error analysis and genetic algorisms (GA) were employed to optimize the parameters of ANN and SVM models, respectively. The results indicated that the proposed SVM models performed better generalization ability due to avoiding the occurrence of overtraining and optimizing fewer parameters based on structural risk minimization (SRM) principle. Furthermore, both TN and TP SVM models trained by trimonthly datasets achieved greater forecasting accuracy than corresponding ANN models. Thus, SVM models will be a powerful alternative method because it is an efficient and economic tool to accurately predict water quality with low risk. The sensitivity analyses of two models indicated that decreasing upstream input concentrations during the dry season and NPS emission along the reach during average or flood season should be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data and even trimonthly data are available, the SVM methodology developed here can easily be applied to other NPS-polluted rivers.     

基于GA-BP算法的含油污泥处理 工艺参数优化

在含油污泥进行资源化处理过程中,针对处理目标受多个因素影响的实际,为了解决工艺之间的耦合问题,采用正交实验的方法来解决,并把主要参数作为优化对象,把含油污泥的脱水率作为评价目标,通过采用GA-BP算法对含油污泥耦合工艺正交实验参数进行了线性与非线性分析.在采用遗传算法优化神经网络的权值和阈值的基础上,用优化后的权值和阈值对测试样本和训练样本进行了预测.预测结果表明,预测误差都有明显减小,分别由0.34211减少到0.031549和0.15476减少到0.040682,可见耦合参数趋向于非线性优化.     

Cost-effective sampling network design for contaminant plume monitoring under general hydrogeological conditions

A new simulation-optimization methodology is developed for cost-effective sampling network design associated with long-term monitoring of large-scale contaminant plumes. The new methodology is similar in concept to the one presented by Reed et al. (Reed, P.M., Minsker, B.S., Valocchi, A.J., 2000a. Cost-effective long-term groundwater monitoring design using a genetic algorithm and global mass interpolation. Water Resour. Res. 36 (12), 3731-3741) in that an optimization model based on a genetic algorithm is coupled with a flow and transport simulator and a global mass estimator to search for optimal sampling strategies. However, this study introduces the first and second moments of a three-dimensional contaminant plume as new constraints in the optimization formulation, and demonstrates the proposed methodology through a real-world application. The new moment constraints significantly increase the accuracy of the plume interpolated from the sampled data relative to the plume simulated by the transport model. The plume interpolation approaches employed in this study are ordinary kriging (OK) and inverse distance weighting (IDW). The proposed methodology is applied to the monitoring of plume evolution during a pump-and-treat operation at a large field site. It is shown that potential cost savings up to 65.6% may be achieved without any significant loss of accuracy in mass and moment estimations. The IDW-based interpolation method is computationally more efficient than the OK-based method and results in more potential cost savings. However, the OK-based method leads to more accurate mass and moment estimations. A comparison of the sampling designs obtained with and without the moment constraints points to their importance in ensuring a robust long-term monitoring design that is both cost-effective and accurate in mass and moment estimations. Additional analysis demonstrates the sensitivity of the optimal sampling design to the various coefficients included in the objective function of the optimization model.     

Application of genetic algorithms for the design of ozone control strategies

Designing air quality management strategies is complicated by the difficulty in simultaneously considering large amounts of relevant data, sophisticated air quality models, competing design objectives, and unquantifiable issues. For many problems, mathematical optimization can be used to simplify the design process by identifying cost-effective solutions. Optimization applications for controlling nonlinearly reactive pollutants such as tropospheric ozone, however, have been lacking because of the difficulty in representing nonlinear chemistry in mathematical programming models. We discuss the use of genetic algorithms (GAs) as an alternative optimization approach for developing ozone control strategies. A GA formulation is described and demonstrated for an urban-scale ozone control problem in which controls are considered for thousands of pollutant sources simultaneously. A simple air quality model is integrated into the GA to represent ozone transport and chemistry. Variations of the GA formulation for multiobjective and chance-constrained optimization are also presented. The paper concludes with a discussion of the practically of using more sophisticated, regulatory-scale air quality models with the GA. We anticipate that such an approach will be practical in the near term for supporting regulatory decision-making.     

Application of Genetic Algorithms for the Design of Ozone Control Strategies

ABSTRACT

Designing air quality management strategies is complicated by the difficulty in simultaneously considering large amounts of relevant data, sophisticated air quality models, competing design objectives, and unquantifiable issues. For many problems, mathematical optimization can be used to simplify the design process by identifying cost-effective solutions. Optimization applications for controlling nonlinearly reactive pollutants such as tropospheric ozone, however, have been lacking because of the difficulty in representing nonlinear chemistry in mathematical programming models.

We discuss the use of genetic algorithms (GAs) as an alternative optimization approach for developing ozone control strategies. A GA formulation is described and demonstrated for an urban-scale ozone control problem in which controls are considered for thousands of pollutant sources simultaneously. A simple air quality model is integrated into the GA to represent ozone transport and chemistry. Variations of the GA formulation for multiobjective and chance-constrained optimization are also presented. The paper concludes with a discussion of the practicality of using more sophisticated, regulatory-scale air quality models with the GA. We anticipate that such an approach will be practical in the near term for supporting regulatory decision-making.     

Design of optimized PI controller for 7-level inverter: a new control strategy

Photovoltaic (PV) system produces electricity that differs from variations in environmental parameters such as temperature and solar radiation. The PV network will operate at maximum power point (MPP) and deal with an ever-increasing energy demand, that changes from both load and weather conditions.” Moreover, energy storage devices could be a potential solution for improving the efficiency and performance of renewable energy sources (RES). This paper intends to establish a control design by an optimization-assisted PI controller for a 7-level inverter. Accordingly, the gains of PI controller are adjusted dynamically by FireFly Integrated-Sea Lion Optimization algorithm (FFI-SLnO) that integrates the concepts of both Sea Lion Optimization (SLnO) and FireFly algorithm (FF). The gains should be tuned such that the error among the reference signal and fault signal should be low and hence better dynamic performance can be obtained by the presented optimized PI controller. Finally, the performance of the proposed method is compared over other traditional models with respect to certain measures and its superiority is proved.