Elemental analysis of lightweight metal scraps recovered by an automatic sorting technique combining a weight meter and a laser 3D shape-detection system

To verify the effectiveness of a new automatic sorting technique that combines a weight meter and a laser 3D shape-detection system, elemental analysis of lightweight metal scraps generated in end-of-life vehicle (ELV) shredder facilities was conducted using a handheld XRF analyzer. According to their 3D shape and chemical composition, aluminum scraps were classified into cast alloy (Alc) and wrought alloy (Alw) fragments, and magnesium scraps were classified into irregularly shaped and rod-like fragments. The average chemical composition of a group of fragments was estimated before and after the separation test using the developed automatic sorting technique. The results show that the production of wrought aluminum alloy from the mixture of Alc and Alw fragments is not realistic because the contents of some alloying elements greatly exceed the standard values, although these alloying elements greatly decrease after the Alc fragments are separated out. For the magnesium scraps, after the rod-like magnesium fragments originating from the steering column were separated from the irregularly shaped fragments, the average chemical compositions of the rod-like products and the irregularly shaped products clearly approached the standard compositions of AM60B and AZ91D alloys, respectively. Thus, it was confirmed that the developed automatic sorting technique contributes to recycling of lightweight metal scrap in the automobile industry.     

Recycling opportunities in the UK for aluminium-bodied motor cars

The purpose of this paper is to review, and draw attention to, issues raised by the recycling of wrought aluminium from motor cars, even though the time horizon for significant arisings of such aluminium scrap is in the order of 20 years from now. Recycling of specific grades of wrought aluminium will be viable only when a means of positively identifying different types of scrap is available. A solution must be reliable, rapid, and low-cost; probably used in conjunction with a vehicle shredder. Such a system of identification will eliminate the need for costly hand-dismantling and segregation. Simple segregation of cast and wrought alloy will, however, be essential when wrought aluminium from car bodies dominates the scrap arisings. Such segregation will produce two high-value scrap products. The first of which will be similar to the A380 casting alloy specification, maintaining the current supply of this scrap, and the second will be a composite of wrought alloys. These issues are relevant to the aluminium scrap industry, which will have to accommodate future changes in the composition of the scrap it receives, and the motor industry, which may adopt in-house recycling of wrought alloy in order to offset the high purchase cost of aluminium.     

Scrap tires to crumb rubber: feasibility analysis for processing facilities

Crumb rubber can be produced from scrap tires in a wide range of particle sizes and quality levels. Ideally, the revenue stream includes tipping fees paid to receive the raw materials; sales of variously-sized crumb products to different end-user markets; and potential sales of scrap metal and fiber contained within the tires. General demand has been increasing, and submarkets for crumb products are growing in size and variety. However, the optimistic expectations of potential investors and government agencies contrast sharply with the experiences of many current and former producers. Production planning and operation is complex, real-dollar crumb prices have fallen, and many producers recount difficulties finding stable markets and competing against newer, state-subsidized competitors. This paper examines the engineering economics of crumb rubber facilities. Following a literature review and interviews with producers, a financial model of a nominal processing operation was created to aid the analysis of different market, crumb size, and production scenarios. The profitability of a crumb facility appears to be particularly sensitive to crumb rubber prices, operating costs, and raw material availability. Better analysis of market and production impacts on financial viability for proposed processing facilities would aid overall market efficiency.     

Optimization of thermal performance of the parabolic trough solar collector systems based on GA-BP neural network model

The aim of this paper is to optimize the thermal performance (system output energy, thermal efficiency, and heat loss of cavity absorber) of parabolic trough solar collector (PTC) systems in order to improve its thermal performance, based on the genetic algorithm-back propagation (GA-BP) neural network model. There are a number of undefined problems, fuzzy or incomplete information and a complex thermal performance of the PTC systems. Therefore, the thermal performance prediction of the PTC systems based on GA-BP neural network model was developed. Subsequently, the metrics performances have been adopted to comprehensively understand the algorithm and evaluate the prediction accuracy. Results revealed that the GA-BP neural network model can be successfully used to predict the complex nonlinear relationship between the input variables and thermal performance of the PTC systems. The cosine effect has a great influence on the thermal performance; thereby the geometrical structure of the PTC systems was optimized. It was found that the optimized geometrical structure was beneficial to improve the thermal performance of the PTC system. In conclusion, the GA-BP neural network model has higher prediction accuracy than the other algorithm and it can be feasible and reliable.     

基于Adaboost集成BP网络原煤生产成本预测研究

为了提高传统BP神经网络预测模型精度,避免BP网络容易陷入局部极值、收敛速度慢等问题,将BP神经网络与Ada-boost算法相结合,提出了一种Adaboost集成BP神经网络模型.结合磁县观台煤矿原煤生产成本相关数据,建立了原煤生产成本预测的Adaboost集成BP神经网络模型,将该模型用于实际的原煤成本预测.结果表明：该模型预测精度高于传统的BP神经网络,收敛速度快,具有较强的鲁棒性,预测精度能满足实际预测需要,为原煤生产成本预测提供了一种新的途径,也为原煤生产成本控制提供了重要依据.     

WATER QUALITY OF STORMWATER RUNOFF FROM TEN INDUSTRIAL SITES1

ABSTRACT: This study was to designed to determine concentrations of selected metals, organic compounds, pesticides, and conventional pollutants in stormwater runoff from two North Carolina businesses in each of the following five general industrial groups: auto salvage, metal fabrication, scrap and recycling, vehicle maintenance, and wood preserving facilities. The sampling procedure involved collecting a first flush sample of runoff from a single storm event and both first flush and composite samples of runoff from three consecutive storm events. Analysis of samples collected during the first flush indicated that zinc and copper were the most common of the 13 metals included in the analysis. Additional analysis documented that several volatile organic, semi-volatile organic, or pesticide compounds including acrolein, methylene chloride, xylenes, toluene, tetrachloroethylene, trichloroethylene, pentachlorophenol, and aldrin were also found in the first flush samples. Concentrations of aggregate organics, nitrogen, phosphorus, and sediment were determined in both first flush and three-hour composite samples. Concentrations of pollutants in first flush samples were similar to those in corresponding composite samples.     

Price and volatility transmission between primary and scrap metal markets

The relationship between primary and scrap prices has been hypothesized for the most part as unidirectional, characterized by spillovers from primary to scrap prices. The purpose of this study is to evaluate empirically the dynamic interactions between primary and scrap metal prices through multivariate time series methods. In addition, the study expands the investigation at the level of volatility transmission, which has not been previously examined. The metal prices utilized are for copper, lead, and zinc for the period 1984–2001. The paper demonstrates differing long run and short run links. Scrap prices do not improve the long run interpretation of primary prices, but information flows from the scrap to the primary markets exist in the short run. Additionally, the copper and lead markets exhibit bidirectional information flows in terms of volatility transmission. The analysis provides valuable insight into the interactions of the primary and scrap metal sectors which can be used to improve forecasting and planning.     

Price and volatility transmission between primary and scrap metal markets

The relationship between primary and scrap prices has been hypothesized for the most part as unidirectional, characterized by spillovers from primary to scrap prices. The purpose of this study is to evaluate empirically the dynamic interactions between primary and scrap metal prices through multivariate time series methods. In addition, the study expands the investigation at the level of volatility transmission, which has not been previously examined. The metal prices utilized are for copper, lead, and zinc for the period 1984–2001. The paper demonstrates differing long run and short run links. Scrap prices do not improve the long run interpretation of primary prices, but information flows from the scrap to the primary markets exist in the short run. Additionally, the copper and lead markets exhibit bidirectional information flows in terms of volatility transmission. The analysis provides valuable insight into the interactions of the primary and scrap metal sectors which can be used to improve forecasting and planning.     

UK metal reclamation : Prospects for improvement

Ferrous and non-ferrous metal-bearing scrap and waste arisings in the UK are discussed. Information is given on the quantities arising, their locations, the amounts recycled and the quantities of metal lost to the economy. Methods of increasing the recovery of metal values are discussed, with an indication of current R & D and possible future requirements.     

Assessing the potential of yield improvements, through process scrap reduction, for energy and CO2 abatement in the steel and aluminium sectors

Targets to cut 2050 CO₂ emissions in the steel and aluminium sectors by 50%, whilst demand is expected to double, cannot be met by energy efficiency measures alone, so options that reduce total demand for liquid metal production must also be considered. Such reductions could occur through reduced demand for final goods (for instance by life extension), reduced demand for material use in each product (for instance by lightweight design) or reduced demand for material to make existing products. The last option, improving the yield of manufacturing processes from liquid metal to final product, is attractive in being invisible to the final customer, but has had little attention to date. Accordingly this paper aims to provide an estimate of the potential to make existing products with less liquid metal production.Yield ratios have been measured for five case study products, through a series of detailed factory visits, along each supply chain. The results of these studies, presented on graphs of cumulative energy against yield, demonstrate how the embodied energy in final products may be up to 15 times greater than the energy required to make liquid metal, due to yield losses. A top-down evaluation of the global flows of steel and aluminium showed that 26% of liquid steel and 41% of liquid aluminium produced does not make it into final products, but is diverted as process scrap and recycled. Reducing scrap substitutes production by recycling and could reduce total energy use by 17% and 6% and total CO₂ emissions by 16% and 7% for the steel and aluminium industries respectively, using forming and fabrication energy values from the case studies. The abatement potential of process scrap elimination is similar in magnitude to worldwide implementation of best available standards of energy efficiency and demonstrates how decreasing the recycled content may sometimes result in emission reductions.Evidence from the case studies suggests that whilst most companies are aware of their own yield ratios, few, if any, are fully aware of cumulative losses along their whole supply chain. Addressing yield losses requires this awareness to motivate collaborative approaches to improvement.     

我国废有色金属拆解利用再生产业发展现状

阐述了我国废金属拆解利用产业发展现状及存在的主要问题,提出了应对废金属拆解再生产业加强政府引导、加强回收体系建设、研发推广先进技术和改善税收政策等对策建议。     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

修正的BP神经网络森林火灾成灾面积预测研究

以1997-2012年《中国林业统计年鉴》的全国森林火灾成灾面积为依据,应用BP神经网络模型对成灾面积进行了预测,对预测方法进行了检验.在此基础上,利用残差提出了修正的BP神经网络模型,并对预测方法进行了改进.研究结果表明,修正的BP神经网络预测精度高于BP神经网络,预测相对误差平均为7.2％,可应用于森林火灾成灾面积的预测.     

Modelling price fluctuations of ferrous scrap

This paper presents a model that analyses the interrelationships between markets of light non-flat steel products (such as concrete reinforcing bars) and iron and steel scrap, both of which are characterized by wide price fluctuations. The model, which was developed as a linear complementarity programming problem, seems to be robust in analysing unstable and simulataneous price movements of those markets. The model may be applied to other metal and scrap markets without substantial changes.     

组合优化的能源消费量预测模型

针对非等间距灰色系统预测中存在误差较大的问题，结合序列本身的特点，利用世界能源消费的历史数据，采用3种灰色预测模型与神经网络进行组合优化，建立了灰色神经网络的能源消费量组合预测模型。实证分析结果表明，提高了模型的拟合和预测精度，拓宽了应用范围。该模型可对能源的消费趋势进行预测，为科学分析能源结构提供依据。     

Dynamic material flow analysis of steel resources in Korea

Material flow analysis (MFA) is an evaluation technique that systematically identifies the flows and stocks of materials within predefined spatial and temporal boundaries. In this paper, the steel resources in Korea are investigated using dynamic MFA. Iron ore and steel scrap are added as raw material components during the production processes of steel, which is then used in a variety of product groups such as construction products, transportation equipment, machinery/metal products, electrical/electronic devices, and other products through fabrication and manufacturing processes. When such product groups are discarded, they are either recycled or landfilled. With consideration for the lifetimes of various product groups in conjunction with steel resource flows in Korea, dynamic MFA is conducted on the flows of steel stock change and annual scrap generation. By 2020, these two flows are expected to increase by as much as 40% and 30%, respectively, compared to 2008, with transportation equipment, in particular, envisaged to experience high growth. At the current recycling rate, however, it will be hard to meet future scrap demand. According to the scenario analysis, 100% of this future scrap demand can be supplied domestically if the recycling rate is increased to over 70% for all product groups, except construction products and transportation equipment, which already have high recycling rates. By 2020, the reduction in scrap importation costs is projected to offer a financial gain of 2.3 billion dollars.     

神经网络模型在北海市空气日报污染指数计算中的应用

根据空气质量日报的实际需要,引入了Excel软件神经网络技术建立空气质量日报污染指数计算模型,采用LM算法提高了计算精度,并将模型应用于北海市空气日报。结果表明:此法较之实际公式计算法更加快捷方便,并且计算结果相当吻合。     

Predicting Carbon Monoxide Concentrations in the Air of Pardis City,Iran, Using an Artificial Neural Network

To date, several methods have been proposed to explain the complex process of air pollution prediction. One of these methods uses neural networks. Artificial neural networks (ANN) are a branch of artificial intelligence, and because of their nonlinear mathematical structures and ability to provide acceptable forecasts, they have gained popularity among researchers. The goal of our study as documented in this article was to compare the abilities of two different ANNs, the multilayer perceptron (MLP) and radial basis function (RBF) neural networks, to predict carbon monoxide (CO) concentrations in the air of Pardis City, Iran. For the study, we used data collected hourly on temperature, wind speed, and humidity as inputs to train the networks. The MLP neural network had two hidden layers that contained 13 neurons in the first layer and 25 neurons in the second layer and reached a mean bias error (MBE) of 0.06. The coefficient of determination (R²), index of agreement (IA), and the Nash–Scutcliffe efficiency (E) between the observed and predicted data using the MLP neural network were 0.96, 0.9057, and 0.957, respectively. The RBF neural network with a hidden layer containing 130 neurons reached an MBE of 0.04. The R², IA, and E between the observed and predicted data using the RBF neural network were 0.981, 0.954, and 0.979, respectively. The results provided by the RBF neural network had greater acceptable accuracy than was the case with the MLP neural network. Finally, the results of a sensitivity analysis using the MLP neural network indicated that temperature is the primary factor in the prediction of CO concentrations and that wind speed and humidity are factors of second and third importance when forecasting CO levels.     

Design of unsurfaced roads constructed with large-size shredded rubber tires: a case study

Stockpiles of scrap tires are serious fire hazard, public health hazard, and an environmental burden. The construction of road embankments, using tire shreds as a lightweight fill, can consume large quantities of scrap tires and has certain engineering benefits. All the previous research focused on small size tire shreds (3–6 in. size) in terms of its use in civil engineering applications and determination of the engineering/environmental properties. This research specifically focuses on large size tire shreds (12 in. size) and its direct comparison with the other sizes of the tire shreds to develop mechanistic-empirical practical design model and so that the use of tire shreds in road bases could be used on regular basis with enhanced reliability instead of on an empirical basis. The research also examines the potential environmental implications of the use of shredded rubber tires and the comparison of the short-term results with other long-term monitoring studies.     

Tracking effective measures for closed-loop recycling of automobile steel in China

Closed-loop recycling of steel in automobiles is particularly difficult because of the low tolerance for impurities and the use of composites of various types of steel products. Technologies that reduce impurities or increase impurity tolerance must be developed and introduced to the steel recycling system at the appropriate time. This study evaluated the feasibility of closed-loop recycling in the automobile industry in China. Material pinch analysis combined with dynamic modeling of the life cycle of steel sheets used in the manufacture of automobiles was employed to estimate the amount of steel sheet scrap available for closed-loop recycling and the amount of copper contamination in the scrap. The results indicate that by 2050, more than half of the old steel sheet scrap generated annually will have to be down-cycled because of its high copper contamination. However, scenario analyses of three types of technologies for mitigating the problem of copper contamination showed the potential for increasing the amount of old scrap used in closed-loop recycling. In particular, improving copper tolerance in the steel production process could be effective both now and in 2050.