Modelling of the location of vehicle recycling facilities: A case study in Poland

The technical progress causes that increasing number of used devices presents a threat for environment, particularly in the rural areas. It can be prevented by organizing a proper system of waste disposal. Currently, the most important problem to solve is recycling of vehicles. The key element for the improvement of the functioning of the recycling network in Poland is to redesign the system so that it will allow for a reduction of the total cost related to the vehicle recycling. This paper presents a modelling approach that could be used to establish one important part of the reverse logistics (RL) network for end-of-life vehicles (ELVs) by defining the optimum locations for dismantling facilities. The proposed modelling approach is illustrated using Mazovia province in Poland as an example. The optimization criteria for the location of the elements of the recycling network are the components of the total cost of the ELV's recycling. Due to high complexity of the model a genetic algorithm has been adapted for solving the model and getting a good solution in a reasonable run time. The criteria of optimization was cost of the following processes: transportation, storage, and dismantling of ELVs. The results of simulation proved that the transportation costs of parts and materials may amount to about 70%, and that the cost of dismantling may exceed 25% of the total cost of recycling. The obtained results confirmed that genetic algorithm method can be used effectively to location the ELV's dismantling facilities. The effect of changing the location of processing facilities on the location of dismantling stations was also studied. The developed model is universal and may be used to determine the locations of different kinds of facilities organized in a reverse recycling network.     

Application of an evolutionary algorithm to the inverse parameter estimation of an individual-based model

Inverse parameter estimation of individual-based models (IBMs) is a research area which is still in its infancy, in a context where conventional statistical methods are not well suited to confront this type of models with data. In this paper, we propose an original evolutionary algorithm which is designed for the calibration of complex IBMs, i.e. characterized by high stochasticity, parameter uncertainty and numerous non-linear interactions between parameters and model output. Our algorithm corresponds to a variant of the population-based incremental learning (PBIL) genetic algorithm, with a specific “optimal individual” operator. The method is presented in detail and applied to the individual-based model OSMOSE. The performance of the algorithm is evaluated and estimated parameters are compared with an independent manual calibration. The results show that automated and convergent methods for inverse parameter estimation are a significant improvement to existing ad hoc methods for the calibration of IBMs.     

基于多模式逆向水质模型的程海水位调控-水质响应预测研究

以云南省程海为例,基于CE-QUAL-W2计算平台开发了水动力-水质模型.由于程海现有的数据不足以支撑开发可靠的流域水文与污染物负荷模型,为弥补由此引起的水质模型边界条件缺失的限制,本研究提出了基于逆向模拟与遗传算法的多模式负荷-参数识别方法来实现对水质模型的校正和鲁棒校验.应用校验后的模型对程海不同水位调控情景的水质...     

Cost‐Effective Placement of Best Management Practices in a Watershed: Lessons Learned from Conservation Effects Assessment Project

This article reviews the key, cross‐cutting findings concerning watershed‐scale cost‐effective placement of best management practices (BMPs) emerging from the National Institute of Food and Agriculture Conservation Effects Assessment Project (CEAP) competitive grants watershed studies. The synthesis focuses on two fundamental aspects of the cost‐effectiveness problem: (1) how to assess the location‐ and farmer‐specific costs of BMP implementation, and (2) how to decide on which BMPs need to be implemented and where within a given watershed. Major lessons learned are that (1) data availability remains a significant limiting factor in capturing within‐watershed BMP cost variability; (2) strong watershed community connections help overcome the cost estimation challenges; (3) detailing cost components facilitates the transferability of estimates to alternative locations and/or economic conditions; and (4) implicit costs vary significantly across space and farmers. Furthermore, CEAP studies showed that (5) evolutionary algorithms provide workable ways to identify cost‐effective BMP placements; (6) tradeoffs between total conservation costs and watershed‐scale cost‐effective water quality improvements are commonly large; (7) quality baseline information is essential to solving cost‐effectiveness problem; and (8) systemic and modeling uncertainties alter cost‐effective BMP placements considerably.     

Depressional runoff cascade networks of the Des Moines Lobe of Iowa

In this study, we develop a general mathematical framework and algorithm for routing cumulative precipitation excess through depressional fill–spill cascade networks in a landscape using only information about depression morphology, local contributing areas, and potential overland flow pathways. The framework also allows for the classification of depressions according to their landscape position within a network, and calculation of precipitation- and non-precipitation-dependent network properties, including measures of network complexity and runoff connectivity. To demonstrate its use, we applied our framework to the 167,287 drained depressions of the Des Moines Lobe of Iowa, a sub-region of the Prairie Pothole Region of North America, over a large range of historically observed precipitation amounts for scenarios both neglecting and incorporating infiltration in runoff generation. Our results show that 85.3% of depressions in this region form 18,851 unique depressional runoff cascade networks, with the remainder being disjunct features. Most of the properties of the region's networks appear to conform to either a truncated power-law or lognormal distribution. For a given rainfall amount, surface runoff connectivity between depressions within networks, and between networks and off-network areas, is controlled primarily by available aggregate depressional volumetric storage and contributing area, and to a lesser degree, network complexity.     

应用数据挖掘算法预测台风条件下PM_(2.5)质量浓度

利用6种数据挖掘算法对2012年台风和污染物浓度等数据建模,建立台风条件下的气象因素和环境空气中PM2.5的关系模型。通过对2013年数据的测试,表明6种数据挖掘方法模型的预测值与实测值具有很好的一致性。6种算法均能实现实时预测,其中Bagging算法预测结果的相关性整体最好,而对于测试样本,M5Rules算法最好,SMOreg算法次之。以2013年的超强台风"海燕"的预测为例,讨论台风条件下PM_(2.5)质量浓度的变化过程及对预测结果的解释。     

基于GA-ANN改进的空气质量预测模型

基于人工神经网络的空气质量预测模型优于传统的逐步回归模型,但由于性能差异不明显而较少在空气质量预报中应用. 设计了将遗传算法和神经网络算法相结合的基于GA_-ANN的空气质量预测模型,并利用天津市2003—2007年气象和污染物监测资料对该模型进行验证. 对2007年全年的ρ(SO₂),ρ(NO₂)和ρ(PM₁₀₎进行预测,预测值与实测值的相关系数分别为0.899 6,0.828 3和0.600 0. 与一般的人工神经网络预测模型相比较,GA_-ANN模型将空气质量等级预报的准确率从77.57%提高到79.67%. GA_-ANN模型可结合其他方法进行日常空气质量预报.      

应用遗传算法优化氢气/空气燃烧反应系数

研究目的是优化氢气/空气简化反应机理的系数,使得简化机理在一定的误差范围内,能代替详细机理.通过计算氢气/空气详细反应机理得到的组分浓度作为遗传算法的适应度目标,对简化机理进行优化.结果显示,优化后的简化机理不仅在在计算组分浓度,而且在层流预混火焰速度和组分时空分布方面,更加接近详细机理所能达到的计算结果,比优化之前的简化反应机理计算结果更加准确.