基于变异CPSO算法的LSSVM出水COD的软测量研究

在分析混沌粒子群优化算法(CPSO)和最小二乘支持向量机(SVM)理论基础上,以某污水处理厂的氧化沟系统为对象,采用带有末位淘汰机制的混沌粒子群优化算法优化支持向量机的参数,建立了基于变异CPSO算法的LS-SVM的氧化沟出水水质COD软测量模型,并与PSO-LSSVM,LSSVM模型比较,研究表明,ICPSO-LSSVM模型预测准确,泛化性能好,且该模型预测结果中相对误差小于10%的样本达到90%,最大相对误差仅为12.5%,均方差MSE为0.0106,模型具有较高的精度,基本可以实现出水COD浓度的在线预估。     

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。     

基于主成分和粒子群优化支持向量机的水质评价模型

水质的评价是治理水污染必不可少的工作。为了准确、快速地对水质进行评价,利用主成分分析法从水质监测常见的多个物化指标提取出主成分,然后将主成分作为支持向量分类机的输入,利用历史数据进行水质评价训练并用粒子群算法优化参数,构造出水质评价模型,将从物化指标中得出的主成分代入此模型即可得到水质类别。最后,选取水质监测点实测数据进行试验,结果表明,模型的水质评价结果准确且稳定。     

基于PSO-LSSVM算法的工业废气净化装置电源参数预测模型

为提高工业废气去除率与净化效率,针对传统净化装置中高频高压电源的输出电压幅值、频率参数不能随废气种类、流量和浓度进行在线调整而造成电能利用率降低的问题,提出一种基于PSO-LSSVM多元回归预测算法的工业废气净化装置电源参数预测模型。根据电源参数及其影响因素,将采集到的历史数据样本分为建模数据样本和实验数据样本,对废气净化装置的有关参数优化协调设置。为克服最小二乘支持向量机(LSSVM)对人为经验选择学习参数的依赖问题,采用粒子群优化算法(PSO)确定惩罚因子C和核函数参数σ~2。结果表明,基于PSO-LSSVM的电源参数预测模型具有较高的精确度,可以真实反映电源参数随废气形式的变化。     

考虑相位的屏障衍射声场工程算法研究

目前声屏障衰减的工程算法均基于能量法,不考虑声源在衍射时的相位变化,而在计算有限长屏障衍射声场或多个屏障的多重衍射声场时,往往误差较大。因此,提出了一种考虑相位、用于计算屏障声衍射的新工程计算方法,该工程算法基于Keller的几何声衍射理论,且延伸自Pierce提出的一个刚性楔形体边界的Hadden-Pierce严格解,相比严格解的积分算法,该简化算法能在计算声影边界的衍射声时避免奇异,计算效率高。使用本文提出的算法、边界元法和Wadsworth研究的实验数据对三维有限长薄屏障和单个厚屏障的双重衍射声场进行了计算,结果表明,该算法与Hadden-Pierce原始积分算法误差在0.5 d B范围以内,与边界元法误差在2 d B以内,且与Wadsworth实验数据吻合度较高;而在计算效率方面,本算法相比原始积分算法提高了500多倍,比边界元法提高了约2 400倍,说明本简化算法完全可应用于户外噪声传播中屏障衰减的准确计算。     

大气污染物负荷优化分配的一种简化方法

本文研究线性规划方法用于大气污染物负荷优化分配问题，在保证优化模型解不变的前提下，文中提出了一种减少约束方程个数的简化方法，并给出了试验结果。     

基于改进支持向量回归机的污水处理厂出水总氮预测模型

在小样本数据的情况下,采用粒子群优化算法(PSO)对传统支持向量回归机(SVR)进行改进,将其应用于北京某大型污水处理厂出水总氮浓度预测上。预测结果精度对比分析表明,PSO-SVR模型预测结果平均相对误差为1.836%,决定系数为67.76%,均方根误差为0.693 9,各评价指标均优于多元线性回归模型、BP神经网络模型。因此在小样本情况下,利用PSO-SVR模型对污水处理厂出水总氮浓度进行预测是可行有效的,为应用数据驱动模型对污水处理过程进行建模模拟提供了一种新方法尝试。     

基于支持向量机的地下水质量综合评价

提出基于支持向量机(SVM)机器学习算法的地下水质量评价模型。首先给出了训练样本生成和数据规范化处理的具体方法,然后采用支持向量机的多分类算法构建模型,并使用k折交叉核实方法对参数进行验证优化。最后通过实证分析,并与单因子指数法、模糊综合评价法和BP神经网络法的评价结果对比分析可知,该方法简便易行,评价结果客观且准确度较高,具有很强的实用性。     

基于多级神经网络模型的厌氧氨氧化系统参数预测

厌氧氨氧化菌生长条件复杂、影响因素多,其工艺系统运行控制复杂,为解决上述问题,研究构建了1个多级神经网络预测模型,以提高SBBR单级自养脱氮厌氧氨氧化系统出水总氮去除率预测精度,并确定了系统工程应用的关键控制参数。一级神经网络模型通过灰色关联度分析,对影响出水总氮去除率的关键性指标进行预测;二级神经网络模型基于一级模型增加数据维度,并通过改进粒子群算法优化网络、借鉴遗传算法变异的思想扩大搜索范围,提高了出水总氮去除率的预测精度。多级神经网络模型预测结果表明,其总氮去除率平均相对误差为0.54%,相对误差为5.76%,均方根误差为1.132 1,预测数据基本上与实际值相符;与其他预测模型相比较,该模型表现出较优的预测精度。进一步分析发现,通过控制工艺系统的曝气量调节出水亚氮浓度,是保证工艺反应的稳定和实现厌氧氨氧化工艺工程应用的有效控制方式。     

大气监测优化布点的遗传BP法

本文着重介绍一种新的优化布点的方法———遗传ＢＰ算法和构造该算法的步骤。应用实例表明：遗传ＢＰ算法用于环境监测优化布点是十分理想的     

水质模型参数识别的遗传算法

Ｏ’ｃｏｎｎｏｒ模型是一个比广泛应用的Ｓｔｒｅｅｔｅｒ－Ｐｈｉｌｉｐｓ模型更精确的水质模型,由于该模型参数率定的困难性,限制它在实际中应用。     

A Monitoring Network Design Procedure for Three-Dimensional (3D) Groundwater Contaminant Source Identification

Finding the location and concentration of contaminant sources is an important step in groundwater remediation and management. This discovery typically requires the solution of an inverse problem. This inverse problem can be formulated as an optimization problem where the objective function is the sum of the square of the errors between the observed and predicted values of contaminant concentration at the observation wells. Studies show that the source identification accuracy is dependent on the observation locations (i.e., network geometry) and frequency of sampling; thus, finding a set of optimal monitoring well locations is very important for characterizing the source. The objective of this study is to propose a sensitivity-based method for optimal placement of monitoring wells by incorporating two uncertainties: the source location and hydraulic conductivity. An optimality metric called D-optimality in combination with a distance metric, which tends to make monitoring locations as far apart from each other as possible, is developed for finding optimal monitoring well locations for source identification. To address uncertainty in hydraulic conductivity, an integration method of multiple well designs is proposed based on multiple hydraulic conductivity realizations. Genetic algorithm is used as a search technique for this discrete combinatorial optimization problem. This procedure was applied to a hypothetical problem based on the well-known Borden Site data in Canada. The results show that the criterion-based selection proposed in this paper provides improved source identification performance when compared to uniformly distributed placement of wells.     

污水处理工艺系统优化设计理论的研究与发展

综述了污水处理工艺系统优化设计理论研究的发展历史 ,分析总结了优化设计模型研究的发展规律以及需要解决的关键性问题 ;详细介绍了几个具有典型代表性的非线性系统优化模型的结构及寻优方法 ;从 6个特征方面横向比较了部分优化数学模型的研究成果 ;结合国内研究动态提出了对该领域研究前景的展望     

A hybrid method for inverse characterization of subsurface contaminant flux

The methods presented in this work provide a potential tool for characterizing contaminant source zones in terms of mass flux. The problem was conceptualized by considering contaminant transport through a vertical “flux plane” located between a source zone and a downgradient region where contaminant concentrations were measured. The goal was to develop a robust method capable of providing a statement of the magnitude and uncertainty associated with estimated contaminant mass flux values.In order to estimate the magnitude and transverse spatial distribution of mass flux through a plane, the problem was considered in an optimization framework. Two numerical optimization techniques were applied, simulated annealing (SA) and minimum relative entropy (MRE). The capabilities of the flux plane model and the numerical solution techniques were evaluated using data from a numerically generated test problem and a nonreactive tracer experiment performed in a three-dimensional aquifer model. Results demonstrate that SA is more robust and converges more quickly than MRE. However, SA is not capable of providing an estimate of the uncertainty associated with the simulated flux values. In contrast, MRE is not as robust as SA, but once in the neighborhood of the optimal solution, it is quite effective as a tool for inferring mass flux probability density functions, expected flux values, and confidence limits.A hybrid (SA-MRE) solution technique was developed in order to take advantage of the robust solution capabilities of SA and the uncertainty estimation capabilities of MRE. The coupled technique provided probability density functions and confidence intervals that would not have been available from an independent SA algorithm and they were obtained more efficiently than if provided by an independent MRE algorithm.     

A hybrid method for inverse characterization of subsurface contaminant flux

The methods presented in this work provide a potential tool for characterizing contaminant source zones in terms of mass flux. The problem was conceptualized by considering contaminant transport through a vertical "flux plane" located between a source zone and a downgradient region where contaminant concentrations were measured. The goal was to develop a robust method capable of providing a statement of the magnitude and uncertainty associated with estimated contaminant mass flux values. In order to estimate the magnitude and transverse spatial distribution of mass flux through a plane, the problem was considered in an optimization framework. Two numerical optimization techniques were applied, simulated annealing (SA) and minimum relative entropy (MRE). The capabilities of the flux plane model and the numerical solution techniques were evaluated using data from a numerically generated test problem and a nonreactive tracer experiment performed in a three-dimensional aquifer model. Results demonstrate that SA is more robust and converges more quickly than MRE. However, SA is not capable of providing an estimate of the uncertainty associated with the simulated flux values. In contrast, MRE is not as robust as SA, but once in the neighborhood of the optimal solution, it is quite effective as a tool for inferring mass flux probability density functions, expected flux values, and confidence limits. A hybrid (SA-MRE) solution technique was developed in order to take advantage of the robust solution capabilities of SA and the uncertainty estimation capabilities of MRE. The coupled technique provided probability density functions and confidence intervals that would not have been available from an independent SA algorithm and they were obtained more efficiently than if provided by an independent MRE algorithm.     

Fuzzy programming method for multi-objective optimal allocation of sediment resources and the cooperative bargaining: a case study in Weishan irrigation area,China

The optimal allocation of sediment resources needs to balance three objectives including ecological, economic, and social benefits so as to realize sustainable development of sediment resources. This study aims to apply fuzzy programming and bargaining approaches to solve the problem of optimal allocation of sediment resources. Firstly, Pareto-optimal solutions of multi-objective optimization were introduced, and the multi-objective optimal allocation model of sediment resources and fuzzy programming model was constructed. Then, from the perspective of multiplayer cooperation, the optimal allocation model of sediment resources was transformed into a game model by using Nash bargaining, and Nash bargaining solution was obtained as the optimal equilibrium strategy. Finally, the influence of different disagreement utility points and bargaining weights on the results was discussed, and the results of Nash bargaining and fuzzy programming methods were compared and analyzed. Results corroborate that Nash bargaining can achieve the cooperative optimization of multiple objectives with competitive relationship and obtain satisfactory scheme. Disagreement utility points and bargaining weights have a certain impact on the optimization results. The solution of fuzzy programming is close to that of Nash bargaining, which provides different ideas for multi-objective optimization problem.

     

Application of Unknown Groundwater Pollution Source Release History Estimation Methodology to Distributed Sources Incorporating Surface-Groundwater Interactions

Sources of contamination of groundwater are often difficult to characterize. However, it is essential for effective remediation of polluted groundwater resources. This study demonstrates an application of the linked simulation-optimization based methodology to estimate the release history from spatially distributed sources of pollution at an illustrative abandoned mine-site. In linked simulation-optimization approaches a numerical groundwater flow and transport simulation model is linked to the optimization model. In this study, topographic and geologic characteristics of the abandoned mine-site were simulated using a three-dimensional (3D) numerical groundwater flow model. Transport of contaminant in the groundwater was simulated using a 3D transient advective-dispersive contaminant transport model. Adsorption or chemical reaction of the contaminant was not considered in the contaminant transport model. Adaptive simulated annealing (ASA) was employed for solving the optimization problem. An optimization algorithm generates the candidate solutions corresponding to various unknown groundwater source characteristics. The candidate solutions are used as input in the numerical groundwater transport simulation model to generate the concentration of pollutant in the study area. This information is used to calculate the objective function value, which is utilized by the optimization algorithm to improve the candidate solution. This process continues until an optimal solution is obtained. Optimal solutions obtained in this study show that the linked simulation-optimization based methodology is potentially applicable for the characterization of spatially distributed pollutant sources, typically present at abandoned mine-sites.