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。     

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。     

活性污泥系统优化设计对不确定性参数的灵敏度研究

在活性污泥系统优化设计模型参数不确定性分析的基础上,考察了进水负荷、生物动力学参数、曝气设备性能参数和二沉池浓缩常数变化对优化设计的影响。灵敏度分析结果表明,对优化设计影响较大的参数为：进水水量水质、与氧转移效率相关的校正参数以及二沉池的浓缩常数等。研究结果对于低灵敏度系统的设计,提高优化设计结果的工程实用性具有重要的意义。     

低能耗脉冲喷吹袋式除尘器的三维数值模拟及工程应用

以袋式除尘器装置为研究对象,考虑射流偏移,建立了脉冲喷吹清洁的三维CFD数值模型,并进行实验验证;对喷嘴与文丘里管优化设计,修改数值模型,研究了低能耗下的脉冲喷吹清灰效果;在将改进的喷嘴与文丘里管进行工程应用的过程中,研究了其对大气粉尘排放的影响。结果表明,建立的三维CFD模型展现出了高瞬态行为和可压缩效应,即在射流中表现出涡流环与冲击单元现象。与孔喷嘴相比,改进后的喷嘴设计对射流偏移进行了调整,并且使滤袋内脉冲压力增加了5.1%～13.3%,提升了清灰效果。对比喉部直径为85 mm的文丘里管,无文丘里管的设计使得射流不易进入滤袋中,导致滤袋内脉冲压力降低了41.4%～46.3%,引起清灰效果的下降;减小文丘里管喉部直径,可以减少回流,提升滤袋内脉冲压力,改善清灰效果。对比原始装置,安装了改进喷嘴与文丘里管的袋式除尘器能明显降低大气粉尘排放,以上研究结果可为脉冲喷吹清灰除尘器的优化设计提供参考。     

紫外消毒器辐射剂量模拟与设备设计

基于拉格朗日方法建立了耦合流体流动、紫外光强辐射与粒子辐射剂量的数值计算模型。利用该模型对工业尺寸的紫外消毒设备进行了模拟计算,获得了消毒设备内的场量信息;展示了利用CFD模拟优化设计紫外消毒反应器的方法;讨论了设置挡板和改变灯管布置2种方式对粒子辐射剂量的影响,计算结果表明,调整辐射场对紫外消毒设备性能的改善程度明显高于改变流动状态。     

催化型微粒捕集器的再生与压降数值研究

采用新的再生机理对催化型微粒捕集器（CPF）深床捕集微粒的氧化再生过程进行了建模，并将微粒捕集器中的压降分成5个部分分别计算，对其压降特性进行了数值研究。结果表明，新模型对深床捕集微粒的氧化再生计算与实验值吻合良好，比原模型更能反映微粒沉积的实际过程，深床捕集微粒的压降计算值也更精确。在排气流量大、温度高的工况下，深床捕集微粒的压降先迅速增大再减小的现象明显。研究结果能为工程应用提供验证手段，为CPF的优化设计提供依据。     

过硫酸盐缓释材料释放过程模型构建

为预测评估过硫酸盐缓释材料的释放性能,对释放过程模型的构建以及模型的验证进行了研究。借助微积分思想,从材料体的概化分割、初始条件设定、每个小单元的状态标定、各个小单元中过硫酸钾的迁移变化量以及材料最外层释放过硫酸盐的量5个方面构建过硫酸盐缓释材料释放模型,利用Excel-VBA编程实现其释放过程模拟。采用欧盟标准NEN7375测试过硫酸盐缓释材料释放性能并获得模型参数。通过输入相关模型参数得到过硫酸盐动态迁移过程及其释放特征曲线,并利用实测数据与模拟数据进行拟合校验。结果表明,模型模拟值与实测值拟合较好,平均误差为1.88%,表明该模型设计合理,能够准确模拟过硫酸盐缓释材料释放过程,可作为缓释材料优化设计工具。     

内循环厌氧反应器多相流流场的三维数值分析简

利用数值模拟的方法，引入欧拉双流体多相流模型及标准k-ε紊流模型，模拟计算内循环厌氧反应器的三相流三维流场，并通过改变污泥颗粒密度及进水流量，针对固相流速及固含率的变化情况，分析条件的改变对流场的影响。研究结果表明，应用数值模拟方法可以获得内循环厌氧反应器内的流场特征；污泥颗粒密度及进水流量的改变对于反应器内污泥颗粒的流速及分布的均匀性有较为明显的影响。模拟结果对反应器的应用及优化设计具有一定的参考价值。     

旋风除尘器流场及浓度场实验与模拟

在研究旋风除尘器内气固两相的运动状况及分离机理方面,计算机模拟替代部分实验的方法能够优化设计旋风除尘器结构参数,提高其对微细颗粒的捕集效率,减少运行压力损失。本研究采用RSM模型和随机轨道模型对旋风除尘器内流场及浓度场进行模拟及实验。研究表明,旋风除尘器压力损失模拟结果与实验结果吻合较好,对于大于5μm的颗粒其捕集效率模拟结果与实验结果基本吻合;旋风除尘器外壁的颗粒浓度呈螺旋带状分布;如将排气管管径减少至原直径0.8倍,可使其对2μm颗粒捕集效率提高6.6%,但压力损失提高36.5%;颗粒的凝并作用有利于提高旋风除尘器微细颗粒的捕集效率。     

不确定性水质模型及其研究进展

水环境是一个充满不确定性因素的复杂巨系统,研究这些不确定性的变化有利于提高人们对水环境分析的认识和做出更切合实际的决策.详细、清晰地评述了不确定性水质模型研究的必要性、研究的方法和国内外研究进展等,并针对此类模型研究与应用过程中普遍存在的一些问题、主要表现和成因进行了分析与论述,最后结合目前的研究热点指出了今后不确定性水质模型发展的3个重要方向.     

Optimizing emission inventory for chemical transport models by using genetic algorithm

Air pollutant emission inventory is an important input parameter for chemical transport models (CTMs). Since great uncertainties exist in the emission inventory, further improvements and refinements are required. In this paper, genetic algorithm (GA), a global search and optimization method, was applied to optimize the emission inventory for the Models-3/Community Multiscale Air Quality (CMAQ) model. An emission optimizing system based on GA was developed and embedded to the CMAQ through the design of several core modules, which implemented the basic functions such as emission adjusting, GA population initializing, CMAQ results evaluating and GA operating. Hypothetical and real-data experiments were respectively performed to examine the validity of GA for emission calibrating. GA showed good performance in both experiments and was always able to find the global minimum. The emission optimizing system was then used to calibrate seasonal PM₁₀ emission inventories of Beijing. Results revealed that PM₁₀ emission in Beijing was underestimated in 2002, an average of 62.74% higher adjustment factor should be imposed on the original emission in target months of different seasons. With the calibrated emission inventories, CMAQ model errors were decreased by 6.46% on average in different seasons. It was concluded that GA was a promising search technique in calibrating emission inputs for CTMs.     

The importance of outlier detection and training set selection for reliable environmental QSAR predictions

Empirical QSAR models are only valid in the domain they were trained and validated. Application of the model to substances outside the domain of the model can lead to grossly erroneous predictions. Partial least squares (PLS) regression provides tools for prediction diagnostics that can be used to decide whether or not a substance is within the model domain, i.e. if the model prediction can be trusted. QSAR models for four different environmental end-points are used to demonstrate the importance of appropriate training set selection and how the reliability of QSAR predictions can be increased by outlier diagnostics. All models showed consistent results; test set prediction errors were very similar in magnitude to training set estimation errors when prediction outlier diagnostics were used to detect and remove outliers in the prediction data. Test set prediction errors for substances classified as outliers were much larger. The difference in the number of outliers between models with a randomly and systematically selected training illustrates well the need of representative training data.     

基于膨胀特性的厌氧流化床反应器的设计和运行控制

生物流化床的床层膨胀行为直接与生化反应区的水动力学和效率相关 ,床层膨胀特性是进行合理设计和运行的重要依据。目前流化床设计一般没有考虑生物膜的影响 ,但生物颗粒的流化行为有别于光滑载体 ,因此需要对曳力系数Cd 和膨胀特性方程中的ui 进行必要的修正。在综合分析基础上 ,探讨了优化两相流化床设计和运行的程序和方法     

Estimating first-order reaction rate coefficient for transport with nonequilibrium linear mass transfer in heterogeneous media

A travel-time based approach is developed for estimating first-order reaction rate coefficients for transport with nonequilibrium linear mass transfer in heterogeneous media. Tracer transport in the mobile domain is characterized by a travel-time distribution, and mass transfer rates are described by a convolution product of concentrations in the mobile domain and a memory function rather than predefining the mass transfer model. A constant first-order reaction is assumed to occur only in the mobile domain. Analytical solutions in Laplace domain can be derived for both conservative and reactive breakthrough curves (BTCs). Temporal-moment analyses are presented by using the first and second moments of conservative and reactive BTCs and the mass consumption of the reactant for an inverse Gaussian travel-time distribution. In terms of moment matching, there is no need for one to specify the mass transfer model. With the same capacity ratio and the mean retention time, all mass transfer models will lead to the same moment-derived reaction rate coefficients. In addition, the consideration of mass transfer generally yields larger estimations of the reaction rate coefficient than models ignoring mass transfer. Furthermore, the capacity ratio and the mean retention time have opposite influences on the estimation of the reaction rate coefficient: the first-order reaction rate coefficient is positively linearly proportional to the capacity ratio, but negatively linearly proportional to the mean retention time.     

Cytotoxicity estimation of ionic liquids based on their effective structural features

Cytotoxicity of a diverse set of 227 ionic liquids (taken from UFT/Merck Ionic Liquids Biological Effects Database) containing 94 imidazolium, 53 pyridinium, 23 pyrrolidinium, 22 ammonium, 15 piperidinium, 10 morpholinium, 5 phosphanium, and 5 quinolinium cations in combination with 25 different types of anions to Leukemia Rat Cell Line (IPC-81) was estimated from their structural parameters using quantitative structure - toxicity relationship “QSTR” methodology. Linear and nonlinear models were developed using genetic algorithm (GA), multiple linear regressions (MLR) and multilayer perceptron neural network (MLP NN) approaches. Robustness and reliability of the constructed models were evaluated through internal and external validation methods. Furthermore, chemical applicability domain was determined via leverage approach. In this work, it was revealed that the cationic moieties make the major contribution to cytotoxicity and the anionic parts play a secondary role in cytotoxicity of the ionic liquids studied here. Structural information represented in this work, can be used for a rational design of safer ILs.     

Sustainable Development by Design: Review of Life Cycle Design and Related Approaches

The environmental profile of goods and services that satisfy our individual and societal needs is shaped by design activities. Substantial evidence suggests that current patterns of human activity on a global scale are not following a sustainable path. Necessary changes to achieve a more sustainable system will require that environmental issues be more effectively addressed in design. But at present much confusion surrounds the incorporation of environmental objectives into the design process. Although not yet fully embraced by industry, the product life cycle system is becoming widely recognized as a useful design framework for understanding the links between societal needs, economic systems and their environmental consequences. The product life cycle encompasses all activities from raw material extraction, manufacturing, and use to final disposal of all residuals.

Life cycle design (LCD), Design for Environment (DFE), and related initiatives based on this product life cycle are emerging as systematic approaches for integrating environmental issues into design. This review presents the life cycle design framework developed forthe U.S. Environmental Protection Agency as a structure for discussing the environmental design literature. Specifying environmental requirements and evaluation metrics are essential elements of designing for sustainable development. A major challenge for successful design is choosing appropriate strategies that satisfy cost, performance, cultural, and legal criteria while also optimizing environmental objectives. Various methods for specifying requirements, strategies for reducing environmental burden, and environmental evaluation tools are explored and critiqued. Currently, many organizational and operational factors limit the applicability of life cycle design and other design approaches to sustainable development. For example, lack of environmental data and simple, effective evaluation tools are major barriers. Despite these problems, companies are beginning to pursue aspects of life cycle design. The future of life cycle design and sustainable development depends on education, government policy and regulations, and industry leadership but fundamental changes in societal values and behavior will ultimately determine the fate of the planet’s life support system.     

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.     

Conjunctive Use of Models to Design Cost-Effective Ozone Control Strategies

Abstract

The management of tropospheric ozone (O₃) is particularly difficult. The formulation of emission control strategies requires considerable information including: (1) emission inventories, (2) available control technologies, (3) meteorological data for critical design episodes, and (4) computer models that simulate atmospheric transport and chemistry. The simultaneous consideration of this information during control strategy design can be exceedingly difficult for a decision-maker. Traditional management approaches do not explicitly address cost minimization. This study presents a new approach for designing air quality management strategies; a simple air quality model is used conjunctively with a complex air quality model to obtain low-cost management strategies. A simple air quality model is used to identify potentially good solutions, and two heuristic methods are used to identify cost-effective control strategies using only a small number of simple air quality model simulations. Subsequently, the resulting strategies are verified and refined using a complex air quality model. The use of this approach may greatly reduce the number of complex air quality model runs that are required. An important component of this heuristic design framework is the use of the simple air quality model as a screening and exploratory tool. To achieve similar results with the simple and complex air quality models, it may be necessary to “tweak” or calibrate the simple model. A genetic algorithm-based optimization procedure is used to automate this tweaking process. These methods are demonstrated to be computationally practical using two realistic case studies, which are based on data from a metropolitan region in the United States.     

An adaptive reduction algorithm for efficient chemical calculations in global atmospheric chemistry models

We present a computationally efficient adaptive method for calculating the time evolution of the concentrations of chemical species in global 3-D models of atmospheric chemistry. Our strategy consists of partitioning the computational domain into fast and slow regions for each chemical species at every time step. In each grid box, we group the fast species and solve for their concentration in a coupled fashion. Concentrations of the slow species are calculated using a simple semi-implicit formula. Separation of species between fast and slow is done on the fly based on their local production and loss rates. This allows for example to exclude short-lived volatile organic compounds (VOCs) and their oxidation products from chemical calculations in the remote troposphere where their concentrations are negligible, letting the simulation determine the exclusion domain and allowing species to drop out individually from the coupled chemical calculation as their production/loss rates decline. We applied our method to a 1-year simulation of global tropospheric ozone-NO_x-VOC-aerosol chemistry using the GEOS-Chem model. Results show a 50% improvement in computational performance for the chemical solver, with no significant added error.