Environmental and economic impact assessment of construction and demolition waste disposal using system dynamics

Construction and demolition wastes (CDW) have increasingly serious problems in environmental, social, and economic realms. There is no coherent framework for utilization of these wastes which are disposed both legally and illegally. This harms the environment, contributes to the increase of energy consumption, and depletes finite landfills resources. The aim of this paper is to evaluate the impacts of two alternatives for the management of CDW, recycling and disposing. The evaluation is carried out through developing a dynamic model with aid STELLA software by conducting the following steps: (1) quantifying the total cost incurred to mitigate the impacts of CDW landfills and uncollected waste on the environment and human health; (2) quantifying the total avoided emissions and saved energy by recycling waste; (3) estimating total external cost saved by recycling waste and; (4) providing a decision support tool that helps in re-thinking about waste disposal. The proposed evaluation methodology allows activating the stringent regulations that restrict waste disposal and developing incentives to encourage constructors to recycle their wastes. The research findings show that recycling CDW leads to significant reductions in emissions, energy use, global warming potential (GWP), and conserves landfills space when compared to disposal of wastes in landfills. Furthermore, the cost of mitigating the impact of disposal is extremely high. Therefore, it is necessary to recycle construction and demolition wastes.     

Determining the socially optimal recycling rate

What municipal recycling rate is socially optimal? One credible answer would consider the recycling rate that minimizes the overall social costs of managing municipal waste. Such social costs are comprised of all budgetary costs and revenues associated with operating municipal waste and recycling programs, all costs to recycling households associated with preparing and storing recyclable materials for collection, all external disposal costs associated with waste disposed at landfills or incinerators, and all external benefits associated with the provision of recycled materials that foster environmentally efficient production processes. This paper discusses how to estimate these four components of social cost to then estimate the optimal recycling rate.     

Applying a mathematical approach to improve the tire retreading process

Problem of waste treatment is very important for environment and the share of tires in this scope is high. The aim of this paper is to analyse retreading as one very interesting way of tires’ treatment. In practice, firms involved with commercial vehicles exploitation have to decide whether to retread used tires or not, depending on the number of retreadings of used tires and travelled distances after each retreading. An approach based on Bayesian networks is proposed as a tool for decision making support in tire retreading process. Analysis is performed on database of tires’ exploitation from a company of public passenger transportation and the statistical results are used as inputs to the proposed model. The results obtained according to the proposed model provide a good basis when it comes to making a decision whether to retread or not a used tire.     

京津冀电子废弃物回收利用潜力预测及环境效益评估

电子废弃物是京津冀区域重要的"城市矿山"之一,全面系统地评估该地区电子废弃物产生量及其回收利用的资源环境效益,可为该区域通过电子废弃物协同精准管理来解决资源环境问题提供科学依据和方法支撑.基于改进的保有量系数法、物料系数法、市场价值法及污染系数法,通过估算废弃电视、电冰箱、空调、洗衣机和计算机5类主要产品（"四机一脑" ）的产生量、资源可回收量、资源回收价值及资源环境效益等,对2010~2025年京津冀电子废弃物的回收利用潜力及环境效益进行预测和评估.结果表明,2010~2025年京津冀废弃"四机一脑"产生量不断增加,2010~2018年期间年均增长率为7.75%,此后预计将以年均3.07%的速度递增,2025年废弃"四机一脑"总量将达到1861.74万台,可回收普通金属27.19万t、塑料12.75万t和贵金属19.45 t.2019~2025年期间,废弃空调和计算机的资源回收经济价值较高,二者之和占年回收总价值的77.22%;铜和金的回收经济价值贡献比分别为43.37%和19.82%,显著高于其他资源.预计2025年资源回收总价值达50.24亿元,是2010年的3.13倍,其中普通金属、贵金属和塑料的回收价值分别为28.68、13.27和8.29亿元.若2025年产生的电子废弃物全部被规范回收利用,能够减少原生矿石开采913.23万t,减少标准煤利用122.71万t,减少废水、废气、废渣及重金属排放量分别为1231.19、473.89、785.10和6.08万t,减少温室气体排放32.92万t （以CO₂-eq计）.废弃空调的回收利用潜力最大,但资质处理企业废弃空调回收处理能力亟待提高.为高效回收利用京津冀电子废弃物,未来应尽快完善基金补贴制度,加强对非正规拆解部门的监督与管理,通过建立区域信息共享平台,实现电子废弃物全生命周期区域协同管理.     

种植绿肥对作物产量和细菌群落稳定性的影响

为深入研究青海高原地区长期种植绿肥,减施化肥条件下小麦/油菜产量及土壤理化性状及微生物群落的变化,通过2011年建立的定位试验,设GF0（毛苕子作为绿肥,不施用化肥）、GF60（毛叶苕子配施60%化肥）、GF70（毛叶苕子配施70%化肥）、GF80（毛叶苕子配施80%化肥）、GF90（毛叶苕子配施90%化肥）、GF100（毛叶苕子配施100%化肥）、F0（休耕并且不施化肥）、F100（休耕并且施用100%化肥）共8个处理,利用高通量测序等技术,研究了种植利用绿肥减施化肥条件下小麦和油菜的产量、土壤性质和微生物群落结构的变化特征.结果表明,在保证小麦、油菜不减产的基础上,种植绿肥后茬作物减施化肥达到30%左右.绿肥配施减量化肥对土壤肥力有显著的提升作用,特别是土壤有机碳和全氮含量有明显的提高,分别提高1.34%~7.46%、2.16%~7.48%.化肥与绿肥配施增强了土壤微生物群落多样性,其中酸杆菌门和变形菌门是本研究土壤中的优势菌群.共生网络分析表明,绿肥处理提高了细菌群落的稳定性和抗干扰能力,同时绿肥应用增加了细菌群落中的关键物种.本研究表明,不同土壤微生物丰度受种植模式、施肥量影响很大,长期种植绿肥、减施化肥提高了土壤微生物丰度和多样性,增强微生物群落的稳定性.种植绿肥条件下化肥减肥量30%左右保证作物稳产可为当地的施肥管理提供参考.     

Parametric study along with selection of optimal solutions in dry wire cut machining of cemented tungsten carbide (WC-Co)

This work deals with parametric study of dry wire EDM (WEDM) process of cemented tungsten carbide. Experiments have been conducted using air as dielectric medium to investigate effects of pulse on time, pulse off time, gap set voltage, discharge current and wire tension on cutting velocity (CV) surface roughness (SR) and oversize (OS). Firstly, a series of exploratory experiments were carried out to identify appropriate gas and its pressure. Afterward, preliminary experiments were conducted to investigate effects of process parameters on dry WEDM characteristics and find appropriate ranges for each factor. Then a central composite rotatable method was employed to design experiments based on response surface methodology (RSM). Empirical models were developed to create relationships between process factors and responses by considering to analysis of variances (ANOVA). To increase the predictability of the process, intelligent models have been developed based on back-propagation neural network (BPNN) and accuracy of these models was compared with mathematical models based on root mean square error (RMSE) and prediction error percent (PEP). In order to select optimal solutions in the cases of single-objective and multi-objectives optimization problems, optimization includes two main approaches. First approach was based on mathematical model and desirability function. Also second approach was designed based on neural network and particle swarm optimization. These approaches were applied in both cases of single-objective and multi-objectives optimization problems and their results were compared with together. Results indicated that selection of air at inlet pressure of 1.5 bar is really appropriate for conducting experiments of next stages. Also, the BPNN creates more accurate prediction rather than mathematical model. Moreover, the BPNN-PSO approach was more efficient in optimization of process rather than mathematical model-desirability function in respect with validation tests.     

Combining state and transition models with dynamic Bayesian networks

Bashari et al. (2009) propose combining state and transition models (STMs) with Bayesian networks for decision support tools where the focus is on modelling the system dynamics. There is already an extension of Bayesian networks - so-called dynamic Bayesian networks (DBNs) - for explicitly modelling systems that change over time, that has also been applied in ecological modelling. In this paper we propose a combination of STMs and DBNs that overcome some of the limitations of Bashari et al.’s approach including providing an explicit representation of the next state, while retaining its advantages, such an the explicit representation of transitions. We then show that the new model can be applied iteratively to predict into the future consistently with different time frames. We use Bashari et al.’s rangeland management problem as an illustrative case study. We present a comparative complexity analysis of the different approaches, based on the structure inherent in the problem being modelled. This analysis showed that any models that explicitly represent all the transitions only remain tractable when there are natural constraints in the domain. Thus we recommend modellers should analyse these aspects of their problem before deciding whether to use the framework.     

Assessing biodegradation benefits from dispersal networks

The performance of biodegradation of organic pollutants in soil often depends on abiotic conditions and the bioavailability of these pollutants to degrading bacteria. In this context, bacterial dispersal is an essential aspect. Recent studies on the potential promotion of bacterial dispersal by fungal hyphae raised the idea of specifically applying fungal networks to accelerate bacterial degradation processes in situ. Our objective is to investigate these processes and their performance via simulation modelling and address the following questions: (1) Under what abiotic conditions can dispersal networks significantly improve bacterial degradation? and (2) To what extent does the spatial configuration of the networks influence the degradation performance? To answer these questions, we developed a spatially explicit bacterial colony model, which is applied to controlled laboratory experiments with Pseudomonas putida G7 organisms as a case study. Using this model, we analyzed degradation performance in response to different environmental scenarios and showed that conditions of limited bacterial dispersal also limit degradation performance. Under such conditions, dispersal networks have the highest potential for improving the bioavailability of pollutants to bacteria. We also found that degradation performance significantly varies with the spatial configuration of the dispersal networks applied and the time horizon over which performance is assessed. Regarding future practical applications, our results suggest that (1) fungal networks may dramatically improve initially adverse conditions for biodegradation of pollutants in soil, and (2) the network's spatial structure and accessibility are decisive for the success of such tasks.     

Determining factors that influence the dispersal of a pelagic species: A comparison between artificial neural networks and evolutionary algorithms

Because of increasing transport and trade there is a growing threat of marine invasive species being introduced into regions where they do not presently occur. So that the impacts of such species can be mitigated, it is important to predict how individuals, particularly passive dispersers are transported and dispersed in the ocean as well as in coastal regions so that new incursions of potential invasive species are rapidly detected and origins identified. Such predictions also support strategic monitoring, containment and/or eradication programs. To determine factors influencing a passive disperser, around coastal New Zealand, data from the genus Physalia (Cnidaria: Siphonophora) were used. Oceanographic data on wave height and wind direction and records of occurrences of Physalia on swimming beaches throughout the summer season were used to create models using artificial neural networks (ANNs) and Na?ve Bayesian Classifier (NBC). First, however, redundant and irrelevant data were removed using feature selection of a subset of variables. Two methods for feature selection were compared, one based on the multilayer perceptron and another based on an evolutionary algorithm. The models indicated that New Zealand appears to have two independent systems driven by currents and oceanographic variables that are responsible for the redistribution of Physalia from north of New Zealand and from the Tasman Sea to their subsequent presence in coastal waters. One system is centred in the east coast of northern New Zealand and the other involves a dynamic system that encompasses four other regions on both coasts of the country. Interestingly, the models confirm, molecular data obtained from Physalia in a previous study that identified a similar distribution of systems around New Zealand coastal waters. Additionally, this study demonstrates that the modelling methods used could generate valid hypotheses from noisy and complicated data in a system about which there is little previous knowledge.     

基于模糊神经网络的煤炭企业循环经济评价模型

针对煤炭企业循环经济水平具有模糊性的特点,利用模糊神经网络具有模糊化和良好泛化的能力,在探讨煤炭企业发展循环经济影响因素的基础上,从经济发展、资源利用、节能减排、环境状况、循环特征五个方面建立了煤炭企业循环经济评价指标体系,构建基于模糊神经网络的煤炭企业循环经济评价模型,并按照模糊神经网络结构的建立、输入数据的模糊化、输出数据的反模糊化、BP神经网络训练进行模型求解和运算,算例验证了该模型具有较好的学习能力.利用该模型评价煤炭企业循环经济具有有效性和准确性.