Development of a Sequential Decision-Making Model for Controlling Multiple Air Pollutants Under Stochastic Uncertainty

Most of previous programming methods for air-quality management merely considered single pollutant from point sources. However, air pollution control is characterized by multiple pollutants from various sources. Meanwhile, uncertain information in the decision-making process cannot be neglected in the real-world cases. Thus, an inexact multistage stochastic programming model with joint chance constraints based on the air quality index (air-quality management model with joint chance constraints (AQM-JCC)) is developed for controlling multiple pollutants deriving from point and mobile sources and applied to a regional air-quality management system. In the model, integrated air quality associated with the joint probability existing in terms of environmental constraints is evaluated; uncertainties expressed as probability distributions and interval values are addressed; risks of violating the overall air-quality target under joint chance constraints are examined; and dynamics of system uncertainties and decision processes under a complete set of scenarios within a multistage context are reflected. The results indicate that useful solutions for air quality management practices in sequential stochastic decision environments have been generated, which can help decision makers to identify cost-effective control strategies for overall air quality improvement under uncertainties.     

SIFNP: Simulation-Based Interval-Fuzzy Nonlinear Programming for Seasonal Planning of Stream Water Quality Management

A simulation-based interval-fuzzy nonlinear programming (SIFNP) approach was developed for seasonal planning of stream water quality management. The techniques of inexact modeling, nonlinear programming, and interval-fuzzy optimization were incorporated within a general framework. Based on a multi-segment stream water quality simulation model, dynamic waste assimilative capacity of a river system within a multi-season context was considered in the optimization process. The method could not only address complexities of various system uncertainties but also tackle nonlinear environmental?Ceconomic interrelationships in water quality management problems. In addition, interval-fuzzy numbers were introduced to reflect the dual uncertainties, i.e., imprecision associated with fixing the lower and upper bounds of membership functions. The proposed method was applied to a case of water quality management in the Guoyang section of the Guo River in China. Interval solutions reflecting the inherent uncertainties were generated, and a spectrum of cost-effective schemes for seasonal water quality management could thus be obtained by adjusting different combinations of the decision variables within their solution intervals. The results indicated that SIFNP could effectively communicate dual uncertainties into the optimization process and help decision makers to identify desired options under various complexities of system components.     

随机鲁棒区间-生物质电厂选址风险分析模型

优化生物质发电厂厂址对于中国生物质发电行业意义重大。然而,生物质发电厂选址系统中包含多重不确定性和复杂性。忽略这些,将给生物质发电厂带来风险。因此,充分考虑生物质发电厂系统中的多重不确定性,将鲁棒随机规划（robust optimization,RO）与区间规划（interval parameter programming,IPP）融于两阶段规划（two-stage programming,TSP）框架中,建立基于随机鲁棒区间风险分析模型（stochastic robust interval model, SRIM）的生物质发电厂选址模型。该模型可以处理表现为离散区间和随机性的不确定变量。并且对于随机过程产生的风险进行追索,增强生物质发电系统的安全性。通过调节不同风险等级,可以对系统进行风险分析,利于决策者对系统安全性和经济性做出衡量。该文以装机容量为15 MW的生物质发电厂为案例。模型结果显示：该规划区域拟建设生物质发电厂数量为1,优化厂址介于（245,242）km至（250,247）km;各个燃料收储站优化配送方案;以及不同鲁棒等级下的系统风险和系统成本。通过模型得出的结果合理可行,可以为生物质电长选址提供科学的依据及决策支持。     

First-best, second-best, and heuristic solutions in conservation reserve site selection

Previous studies which dealt with the conservation reserve site selection problem used either optimization methods, specifically linear integer programming (IP), or heuristic algorithms. The trade-off between computational efficiency versus optimality has been discussed in some articles and conflicting messages were signaled. Although the problem of suboptimality was acknowledged, some authors argued that heuristics may be preferable to exact optimization because IP models are computationally complex and may not be solvable when too many reserve sites are involved. On the other hand, some studies reported that fairly large problems could be solved easily. This paper shows that although the computational complexity argument can be valid for large reserve selection problems, by properly guiding the solver and exploiting the problem structure, formal optimization can deliver second-best (near-optimal) solutions that dominate the greedy heuristic solutions.     

Two paths to a suboptimal solution - once more about optimality in reserve selection

Several studies have compared the performances of exact algorithms (integer programming) and heuristic methods in the solution of conservation resource allocation problems, with the conclusion that exact methods are always preferable. Here, I summarize a potentially major deficiency in how the relationship between exact and heuristic methods has been presented: the above comparisons have all been done using relatively simple (linear) maximum coverage or minimum set models that are by definition solvable using integer programming. In contrast, heuristic or meta-heuristic algorithms can be applied to less simplified nonlinear and/or stochastic problems. The focus of this study is two kinds of suboptimality, first-stage suboptimality caused by model simplification and second-stage suboptimality caused by inexact solution. Evidence from comparisons between integer programming and heuristic solution methods suggests a suboptimality level of around 3%-10% for well-chosen heuristics, much depending on the problem and data. There is also largely anecdotal evidence from a few studies that have evaluated results from simplified conservation resource allocation problems using more complicated (nonlinear) models. These studies have found that dropping components such as habitat loss rates or connectivity effects from the model can lead to suboptimality from 5% to 50%. Consequently, I suggest that more attention should be given to two topics, first, how the performance of a conservation plan should be evaluated, and second, what are the consequences of simplifying the ideal conservation resource allocation model? Factors that may lead to relatively complicated problem formulations include connectivity and evaluation of long-term persistence, stochastic habitat loss and availability, species interactions, and distributions that shift due to climate change.     

A gaussian-box modeling approach for urban air quality management in a Northern Chinese City—II. pollutant emission abatement

A modeling framework by linking air quality simulation with system optimization was presented in this paper to develop cost-effective urban air quality management strategies in Fengnan district of China. The relation between the total allowable emission and wind speed as well as the relation between the total allowable emission and air-quality-guideline satisfaction were quantified based on the simulation results of the Gaussian-box modeling system. The area-source emission reduction objective in each functional zone of the study city during the heating and non-heating seasons was calculated based on such relations. A linear programming model was then developed to optimize the emission abatement which was subject to a number of dust and SO₂ control measures. The economic objective of the air quality management strategy was to minimize the total emission control system cost while the environmental objective can still be satisfied. The environmental objective was reflected by the emission reduction objective of TSP, PM₁₀ and SO₂ corresponding to an air-quality-guideline satisfaction percentage of 80%. Consequently, the modeling system comprehensively took into account the information of emission reduction objectives, emission abatement alternatives, emission reduction cost, and related resources constraints. An optimal emission abatement strategy and the related cost were obtained for various pollution control measures. The results would provide sound bases for decision makers in terms of effective urban air quality management and ensuring healthy economic development in the study city.     

A gaussian-box modeling approach for urban air quality management in a northern chinese city—I. model development

A Gaussian-box modeling approach was presented in this paper to examine the urban air quality due to multiple point- and area-source emissions in the northern Chinese city of Fengnan, which is associated with a deteriorated air quality as a consequence of industrialization and rapid urban growth. A 3-D multi-box (3DMB) air quality model was developed to predict air quality due to area-source emissions. It improved upon the conventional box models by allowing consideration of more details in spatial variations of emission sources and meteorological conditions. The modeling domain was divided into various layers within the mixing height, while each layer was associated with a number of sub-boxes. A multi-source and multi-grid Gaussian modeling approach was then applied to predict the air quality in different sub-boxes that are associated with multiple point-source emissions. Thus the Gaussian-box modeling approach could effectively simulate impacts of both area- and point-source emissions but also reflect more details of the spatial variations in source distributions and meteorological conditions. This modeling approach was employed to predict daily average SO₂, TSP and PM₁₀ concentrations for each sub-box during the heating and non-heating seasons, respectively. The analysis of the mean normalized error of the modeling results demonstrates the feasibility and applicability of the developed method, and the presented method could provide more useful and scientific bases for developing effective urban air quality control and management strategies.     

基于模糊整数规划的水质浮标光伏/蓄电池动力源配置优化

孤岛型可再生能源供电,是解决水质监测用浮标动力来源的有效途径之一。根据水质浮标系统的特点,综合系统的经济性与环境条件,协同浮标本体成本及浮力、容积、天气因素等约束条件,构建了水质浮标动力源用光伏/蓄电池优化模型。针对制造过程中约束条件与预期值可能出现偏差这一实际问题,提出采用模糊整数规划算法进行求解,实现优化配置光伏/蓄电池动力源的目的。最后,提出对最大连续阴雨天数进行灵敏度分析,考察天气因素对所设计系统稳定性的影响。算例结果表明所提优化配置方法的有效性与可靠性,该方法不仅能够给出综合最优的动力源配置方案,还有利于结合安置地的环境条件,尽量避免主观设定关键参数,辅助水质监测用设备的高效研究与应用。     

An ARPS-CMAQ Modeling Approach for Assessing the Atmospheric Assimilative Capacity of the Beijing Metropolitan Region

A coupled ARPS–CMAQ modeling system was applied to investigate the atmospheric assimilative capacity (AAC) of PM₁₀ in the Beijing metropolitan region of China. The AAC was defined as the maximum allowable pollutant emission that can be discharged to the atmosphere without violating the desired air quality objective in the planning region. The coupled modeling system was firstly evaluated through comparing the simulation results of PM₁₀ concentration with the corresponding observations during four representative months in 2002, which showed an acceptable modeling performance. By using a trial-and-error approach, the validated modeling system was run through gradually reducing the emission strength within the modeling domain from a GIS-based pollutant emission database until a desired air quality objective was achieved. The air quality objective was characterized by the air quality guideline-satisfaction ratio (AQGSR) which was defined as the ratio of days when the daily PM₁₀ concentrations are below the air quality standard during a certain time period. The PM₁₀ emission within the modeling domain after such trial-and-error reduction under the condition of satisfying a certain AQGSR was considered its corresponding assimilative capacity. Accordingly, the atmospheric assimilative capacities of PM₁₀ in the study region were identified corresponding to the AQGSR of 60, 65, 70, 75 and 80%, respectively. The relation between such capacity and guideline-satisfaction ratio was then established through regression analysis. The results could provide sound basis for decision makers in terms of effective air quality management by understanding the allowable pollutant discharge under different desired air quality objectives.     

Optimizing wetland restoration and management for avian communities using a mixed integer programming approach

Conservation planning and management decisions often present trade-offs among habitats and species, generating uncertainty about the composition and configuration of habitat that will best meet management goals. The public acquisition of 5471 ha of salt ponds in San Francisco Bay for tidal-marsh restoration presents just such a challenge. Because the existing ponds support large numbers of waterbirds, restoring the entire area to tidal marsh could cause undesirable local declines for many species. To identify management strategies that simultaneously maximize abundances of marsh- and pond-associated species, we applied an integer programming approach to maximize avian abundance, comparing across two objectives, two models, and five species weightings (20 runs total). For each pond, we asked: should it be restored to a tidal marsh or kept as a managed pond, and with what salinity and depth? We used habitat relationship models as inputs to non-linear integer programs to find optimal or near-optimal solutions. We found that a simple linear objective, based on maximizing a weighted sum of standardized species’ abundance, led to homogeneous solutions (all-pond or all-marsh). Maximizing a log-linear objective yielded more heterogeneous configurations that benefit more species. Including landscape terms in the models resulted in slightly greater habitat aggregation, but generally favored pond-associated species. It also led to the placement of certain habitats near the bay’s edge. Using the log-linear objective, optimal restoration configurations ranged from 9% to 60% tidal marsh, depending on the species weighting, highlighting the importance of thoughtful a priori consideration of priority species.     

考虑生态环境的灌区双水源区间两阶段模糊可信性约束模型

对水资源进行合理的优化配置是缓解水资源短缺问题和遏制生态环境持续恶化的重要途径之一。为了实现水资源的可持续利用和生态环境的良性循环,该研究针对灌区水资源配置系统中存在的复杂性和不确定性,以黑龙江省建三江垦区为研究区,将水质污染治理成本（生态补偿费用）与系统经济收益相结合作为目标函数,引入离散区间、概率分布函数及模糊数表示系统中的多重不确定性,通过设置不同的可信性置信水平解决带有违规风险概率的模糊风险问题,进而建立考虑生态环境的区间两阶段模糊可信性约束规划模型。通过对模型求解,实现地表地下水资源在典型作物间合理的优化配置,得到不同来水情境下生态补偿费用、不同水源不同作物的最优配水方案及不同可信性置信水平下系统最大经济效益,结果表明：模型能够反映系统中的不确定性,有效平衡系统收益与违规风险,并通过满足生态需水和农业排污约束以促进系统经济效益和生态环境的协调发展;当来水水平分别为低、中、高3种流量时,研究区地表水生态补偿费用和地下水生态补偿费用均呈现下降的趋势;当可信性置信水平为1.0时,系统经济收益为29.88×108～52.18×108元,当可信性置信水平为0.5时,系统经济收益为31....     

基于区间多阶段随机规划模型的灌区多水源优化配置

灌区多水源灌溉系统中存在许多不确定性因素,随着系统环境的变化及不确定性因素的影响,导致其配水过程具有动态特征。针对灌区多水源灌溉系统的配水特点,该文建立基于区间多阶段随机规划的灌区多水源优化配置模型。同时,考虑灌溉水对作物产量的影响,引入水分敏感指数权重系数,并以黑龙江省和平灌区水稻不同生育阶段灌溉水资源优化配置进行实例研究。结果表明,在不同来水情境下,管理者可根据各个生育阶段水分敏感指数权重系数,判断作物不同生育阶段的需水敏感程度,当来水情境的来水量多时,会产生余水量,可调配给下一生育阶段;当来水情境的来水量少时,管理者可在减少灌溉水量与增加外调水之间进行权衡,并根据需水关键期与需水非关键期做出决策,使水资源在作物生育阶段间及作物生育阶段内进行分配,实现灌区多水源灌溉系统的动态配水。该模型的应用在确保作物产量的同时,使灌溉水资源在作物各个生育阶段进行合理配置,有效地避免了水资源浪费,对提高灌溉水利用效率、保证水资源的可持续利用具有重要意义。     

考虑分布式电源的农村配电网网络重构

分布式电源接入农村配电网改变了农网的网络结构和运行方式,辐射结构是农电网运行、保护和调度的基础.该文建立了考虑分布式电源的配电网重构非线性整数优化数学模型,引入离散的二进制决策变量表示配电网的支路状态,建立了严格的数学模型保证配电网的辐射运行,提出分支定界法结合原始对偶内点法精确求解农村配电网网络重构,通过对分支节点检测,去掉不可行节点;通过增加安全因子,避免混合非线性整数规划陷入局部最优.结果表明该方法是一种可行的配电网重构方法,可为农村配电网最优运行提供参考.     

考虑风险价值的不确定性水资源优化配置

保障区域农业用水的可持续发展,对水资源进行优化配置至关重要。由于粮食主产区水资源配置过程中存在许多不确定性因素,在追求最小用水成本时,也存在着较大的风险,因此该文以三江平原涵盖的七台河、佳木斯、双鸭山、鹤岗和鸡西5个重要粮食主产区为研究区域,以区间两阶段随机规划模型为基础,引入风险偏好,构建地表水和地下水优化配置模型。结果表明,双鸭山和鸡西的配水过程中地表水缺水量很大,主要开采利用地下水;佳木斯作为粮食生产面积较大的行政区,需要外来水进行补给;七台河和鹤岗的种植面积较小,综合考虑引水成本和粮食收益,引用较少的外来水来降低成本;最后得出不同来水水平下,各种风险偏好下水资源优化配置的最小成本的变化规律,即在低来水水平下,用水成本从344.2×108~355.4×108元增加到411.5×108~430.7×108元;在高来水水平下,总用水成本从422.5×108~435.3×108元降低到351.7×108~365.3×108元;在中来水水平时,总用水成本则呈现出先增加后减少的规律。该模型兼有区间两阶段和风险价值模型的特点,综合衡量成本和风险,可有效节约用水成本,并能增强水资源系统规避风险的能力,并以佳木斯2014年实际用水为例,计算得到相对误差在15%以内,较为真实地反映水资源优化配置过程中的不确定性和风险性,为提高水资源利用效率和区域水资源规划管理提供依据。     

A Two-Phase Optimization Model Based on Inexact Air Dispersion Simulation for Regional Air Quality Control

This study proposes a two-phase optimization model for regional air pollution control. To predict the pollutant concentrations at receptor zones, an interval Gaussian plume model is advanced to facilitate the generation of optimal pollution control policies. Results from the case study indicate satisfactory performance of the proposed model in handling uncertainties in parameters expressed as intervals and in stipulations associated with pollutant emission and ambient air quality. Compared with conventional models, it has advantages of generating compromised management strategies according to decision makers’ preference. This would be useful when the guarantee of satisfying all constraints is inapplicable or too costly. The proposed model is capable of identifying key factors and/or input conditions that may intensely affect system outputs and thus facilitating decision makers in adjusting current system status to benefit future management. The results also reveal a significantly enhanced satisfactory level would be obtained compared with conventional “single-phase”-based optimization models.     

Accounting for habitat loss rates in sequential reserve selection: Simple methods for large problems

We develop reserve selection methods for maximizing either species retention in the landscape or species representation in reserve areas. These methods are developed in the context of sequential reserve selection, where site acquisition is done over a number of years, yearly budgets are limited and habitat loss may cause some sites to become unavailable during the planning period. The main methodological development of this study is what we call a site-ordering algorithm, which maximizes representation within selected sites at the end of the planning period, while accounting for habitat loss rates in optimization. Like stochastic dynamic programming, which is an approach that guarantees a globally optimal solution, the ordering algorithm generates a sequence in which sites are ideally acquired. As a distinction from stochastic dynamic programming, the ordering is generated via a relatively fast approximate process, which involves hierarchic application of the principle of maximization of marginal gain. In our comparisons, the ordering algorithm emerges a clear winner, it does well in terms of retention and is superior to simple heuristics in terms of representation within reserves. Unlike stochastic dynamic programming, the ordering algorithm is applicable to relatively large problem sizes, with reasonable computation times expected for problems involving thousands of sites.     

基于农产品供应链的质量安全可追溯系统

为加强农产品质量安全管理,保证消费者身体健康,从农产品供应链的角度出发,提出基于农产品供应链的质量安全可追溯系统的设计方案。在危害分析和关键控制点(hazard analysis and critical control point,HACCP)管理体系的指导下,利用二维码技术、数据库技术、网络信息技术进行系统的构建和开发,实现了农产品在整个供应链上从种植、采收、加工到销售的全程跟踪和溯源,有效地加强了对农产品质量安全的监管,保证消费者最终知情权。目前,该系统在江苏江阴地区实际测试效果良好,验证了方案的可行性和有效性。     

考虑水库来用水过程关联性的多维随机动态规划算法

水库来水和以农业灌溉用水为典型代表的用水过程均受到气候条件的显著影响.气候过程的随机性会导致水库入库径流(供给侧)与需求过程(需求侧)的随机性;同时,流域气候条件的空间相似性使得二者之间存在一定程度的关联性.为有效解决水库随机优化调度中来用水过程的随机性与关联性问题,该研究采用Copula函数描述多维变量的随机性与关联...     

Artificial neural network for estimating monthly reference evapotransiration under arid and semi arid environments

The objective of this study is to investigate the potential of artificial neural networks (ANNs) for estimating reference monthly evapotranspiration under arid and semi-arid environments. A simple leave one out data analysis was carried out; one neural network solution on six inputs and another six network solutions on five inputs for each monitoring station were done. Comparison of the results showed that the accuracy of ANNs is decreased when relative humidity, wind speed and solar or extraterrestrial radiation are excluded as input variables. The results also showed that monthly evapotranspiration could be computed with relatively good accuracy compared with local calibrated Hargreaves equation based on air temperature using trained ANNs at another location. We conclude, based on our overall results, that temperature-based method ANNs can be used with relatively good accuracy for water resource management, irrigation scheduling and management, and environmental assessment when data are not enough using trained ANNs from another location.     

农产品近红外光谱品质检测软件系统的设计与实现

在中国加入WTO后，利用近红外光谱技术进行快速、准确的农产品品质检测技术与方法研究非常迫切，为满足对样品组分定量预测软件的需求，设计并实现了农产品近红外光谱品质检测软件系统。采用了面向对象编程技术，向量、矩阵运算的封装技术，结构化存储与复合文档等软件实现技术。该系统由光谱文件管理，光谱显示，光谱信号处理(预处理)，光谱校正模型的建立与管理，未知样品的组分浓度预测等五大功能模块构成。具有多种光谱仪器光谱格式(SPC、TXT、JWS、MAT、ASF等)兼容性，光谱分析功能全面性，算法(PCR、PLS、GA、ANN、Wavelet、Wavelet Package等)多样性，软件系统易维护性，用户使用操作方便性等特点。实例分析表明，无论采用本系统的PLS-NIR模型，还是采用本系统的PCA-MBP-NIR模型，组分浓度预测精度均很高。本系统为农产品品质检测近红外光谱建模和预测提供了很好的技术支持，具有重要的应用价值。