Inexact multistage stochastic integer programming for water resources management under uncertainty

In this study, an inexact multistage stochastic integer programming (IMSIP) method is developed for water resources management under uncertainty. This method incorporates techniques of inexact optimization and multistage stochastic programming within an integer programming framework. It can deal with uncertainties expressed as both probabilities and discrete intervals, and reflect the dynamics in terms of decisions for water allocation through transactions at discrete points of a complete scenario set over a multistage context. Moreover, the IMSIP can facilitate analyses of the multiple policy scenarios that are associated with economic penalties when the promised targets are violated as well as the economies-of-scale in the costs for surplus water diversion. A case study is provided for demonstrating the applicability of the developed methodology. The results indicate that reasonable solutions have been generated for both binary and continuous variables. For all scenarios under consideration, corrective actions can be undertaken dynamically under various pre-regulated policies and can thus help minimize the penalties and costs. The IMSIP can help water resources managers to identify desired system designs against water shortage and for flood control with maximized economic benefit and minimized system-failure risk.     

有限运力条件下的地震救灾物资调度模型及方法

救灾物资的调度是地震救灾工作中的一个重要环节。如何将地震救灾物资以时间最短、安全性最高及经济性最好的方式运送到物资需求点是救灾物资调度的关键。本文从时效性、安全性及经济性方面考虑了地震救灾物资的调度问题。建立了运力不足的条件下,时间最短、出救点数目最少、安全性最高的地震救灾物资调度数学优化模型;将问题转化为不受运力约束的情况,求解出可行方案的集合,再利用模糊规划对每个可行方案求最优解;通过比较最优解,求出"时间最短,出救点数目最少,安全性最高"的方案。最后,通过一个仿真算例阐述了本文方法的使用。     

Two-stage planning for sustainable water-quality management under uncertainty

In water-quality management problems, uncertainties may exist in a number of impact factors and pollution-related processes (e.g., the volume and strength of industrial wastewater and their variations can be presented as random events through identifying a statistical distribution for each source); moreover, nonlinear relationships may exist among many system components (e.g., cost parameters may be functions of wastewater-discharge levels). In this study, an inexact two-stage stochastic quadratic programming (ITQP) method is developed for water-quality management under uncertainty. It is a hybrid of inexact quadratic programming (IQP) and two-stage stochastic programming (TSP) methods. The developed ITQP can handle not only uncertainties expressed as probability distributions and interval values but also nonlinearities in the objective function. It can be used for analyzing various scenarios that are associated with different levels of economic penalties or opportunity losses caused by improper policies. The ITQP is applied to a case of water-quality management to deal with uncertainties presented in terms of probabilities and intervals and to reflect dynamic interactions between pollutant loading and water quality. Interactive and derivative algorithms are employed for solving the ITQP model. The solutions are presented as combinations of deterministic, interval and distributional information, and can thus facilitate communications for different forms of uncertainties. They are helpful for managers in not only making decisions regarding wastewater discharge but also gaining insight into the tradeoff between the system benefit and the environmental requirement.     

Modelling the economic consequences of the EU Water Framework Directive for Dutch agriculture

The EU Water Framework Directive (WFD) requires member states to take measures to ensure that bodies of water will be in good chemical and ecological condition by 2015. Important measures to achieve this goal include reducing emissions of nitrogen (N) and phosphate (P₂O₅) from manure and mineral fertilizers into the environment. In regions with a high livestock density, this measure is expected to affect agricultural production and income. To quantify these effects, an environmental economic model is required that can assess alternatives capable of reducing N and P₂O₅ potential emissions to water. In this paper, we develop a model that is capable of analysing changes in potential emissions to water of N and P₂O₅ and apply it to the Netherlands, a country with large nutrient emissions. Compared to a 2015 reference scenario based on current efforts to reduce nutrient emissions, we found that the WFD measures will increase regional transport and export of manure and reduce the number of animals in the Netherlands. Fodder adjustments (defined as lower N and P₂O₅ input in purchased fodder) to decrease nutrient excretion in manure were a less attractive option than amongst others export, transportation of manure to another region, land use changes or reduction of the number of livestock. Compared to the reference scenario in 2015, total agricultural income will decrease by about €81.5 million per year (about €49/ha per year), although the effects will differ among parts of the Netherlands and agricultural sectors. The average predicted decrease in N emissions from agricultural sites, vulnerable to leaching into bodies of water will be almost 20% or approximately 14.7 kg N/ha per year. The reduction in N emissions to air from animal sheds, manure storage systems, application of animal manure and mineral fertilisers to the crops and grazing animals equals 6.5% or 5 kg ammonia (NH₃) per hectare.     

农村景观规划与生态建设

介绍了农村景观的发展与变化 ,论述了农村景观生态规划的内容与原则 ,并介绍了 5种典型的景观生态建设模式。     

Optimizing the Monitoring Strategy of Wastewater Treatment Plants by Multiobjective Neural Networks Approach

This paper applies artificial neural network (ANN) to model the observed effluent quality data. The ANN’s structure, involving the number of hidden layer and node and their connection, is determined endogenously by resorting to the compromise of data cost minimization and prediction accuracy maximization. To obtain the best compromise possible, the model introduces an aspiration variable (μ) that represents the level of aspiration achieved in one objective and the conjugate of μ, (1 − μ), represents level of aspiration achieved in the other objective. Because a massive amount of calculation is required, the model applies genetic algorithm (GA) for its computational flexibility and capability to ensure global solution. Feasibility and practicality of the model is tested by a case study with a set of 150 daily observations on 17 operational variables and quality parameters at an industrial wastewater treatment plant (WTP) located in southern Taiwan. Of these 17 variables open to selection, only 6 variables, wastewater flow rate (Q), CN⁻, SS, MLSS, pH and COD are selected by the model to achieve the maximum accuracy of prediction, 0.94, with a total cost of 5,950 NT$. By constraining budget availability, the variables included in the model are reduced in number, causing a concomitant reduction in prediction accuracy, that is, by varying μ (aspiration level of accuracy), a trajectory of cost and accuracy is generated. The calculation results a cost of 3,650 NT$ and 0.54 accuracy for the case with variables including flow rate, SCN⁻ and SS in equalization basin; aeration tank hydraulic retention time (HRT) and percentage of returned sludge (R%) are selected for building the prediction model when the importance of required budget is equal to the accuracy of prediction model. In addition, when required cost for building ANN model is between 3,650 NT$ and 3,900 NT$, the marginal return of budget input is highest in the entire range of calculation.     

A two-stage inexact joint-probabilistic programming method for air quality management under uncertainty

A two-stage inexact joint-probabilistic programming (TIJP) method is developed for planning a regional air quality management system with multiple pollutants and multiple sources. The TIJP method incorporates the techniques of two-stage stochastic programming, joint-probabilistic constraint programming and interval mathematical programming, where uncertainties expressed as probability distributions and interval values can be addressed. Moreover, it can not only examine the risk of violating joint-probability constraints, but also account for economic penalties as corrective measures against any infeasibility. The developed TIJP method is applied to a case study of a regional air pollution control problem, where the air quality index (AQI) is introduced for evaluation of the integrated air quality management system associated with multiple pollutants. The joint-probability exists in the environmental constraints for AQI, such that individual probabilistic constraints for each pollutant can be efficiently incorporated within the TIJP model. The results indicate that useful solutions for air quality management practices have been generated; they can help decision makers to identify desired pollution abatement strategies with minimized system cost and maximized environmental efficiency.     

Emergy-based fuzzy optimization approach for water reuse in an eco-industrial park

The establishment of an eco-industrial park (EIP) provides opportunity for individual plants to cooperate with each other in order to utilize resources efficiently and thus reduce waste. The goal of an EIP is to “close the loop” through recycling and reuse of material and energy streams. Studies show with current freshwater consumption trends there would be water stress aggravated by global warming in the near future. This paper presents a model to design an EIP water reuse network that considers overall system sustainability as measured with emergy, as well as cost saving desired by individual plants. Case studies from literature are then solved to illustrate the advantage of this method in decision making. The illustrative examples show how the model achieves a compromise among the potentially conflicting fuzzy goals of the various EIP stakeholders.     

大面积停电下配电网重要用户的应急供电策略研究

为研究电网较长时间停电环境下应急发电车与发电燃油供应协同优化调度问题,首先在电力公司管理现状基础上,通过对重要用户识别分析,按照是否自行配备发电机将重要用户进行分类,针对已经配备发电机的用户,重点考虑周期性供应燃油,对未配备发电机的用户,电力公司需紧急调度有限移动应急发电车,据此构建2阶段的移动应急发电车和油料供应模型以保证重要用户的紧急供电不会中断（第1阶段为电力公司应急发电车指派,第2阶段为加油站油料补充分配）。然后,将该调度问题转化为移动应急发电车指派问题和运油车路径问题的非线性混合模型,针对非线性目标函数和约束条件,通过引入大M法和变量替换进行线性化,得到混合整数线性规划,最小化负荷损失和成本为目标。最后,以某市电网重要用户为例,验证上述模型有效性,并通过原模型与聚类模型对比强调有限移动应急发电车的有效利用具备一定重要性。研究结果对电网公司保障应急供电具有重要指导意义。