Statistical approach to determination of overhaul and maintenance cost of loading equipment in surface mining

The purpose of this research was to develop a new approach in determination of overhaul and maintenance cost of loading equipment in surface mining. Two statistical models including univariate exponential regression (UER) and multivariate linear regression (MLR) were used in this study. Loading equipment parameters such as bucket capacity, machine weight, engine power, boom length, digging depth, and dumping height were considered as variables. The results obtained by models and mean absolute error rate indicate that these models can be applied as the useful tool in determination of overhaul and maintenance cost of loading equipment. The results of this study can be used by the decision-makers for the specific surface mining operations.

Characteristic Period Value of the Seismic Design Response Spectrum of UHV Electrical Equipment

Characteristic period is an important parameter of the seismic design response spectrum. There is important theoretical significance and engineering application value to the study of the characteristic period of seismic design response spectrum of ultra high voltage （UHV） electrical equipment. In this paper, 1448 horizontal earthquake records within the world scope including the United States and Japan for Site Class m were analyzed. Results show that both magnitude and epicentral distance have great influence on the characteristic period. About 80 % of characteristic periods of strong earthquake records are about 0. 9s. Statistical analysis was conducted on the seismic hazard assessment results of 312 projects of China in recent years, and it is found that about 70 % of characteristic periods are about 0. 9s. Combined with the related code comparison and analysis, it is suggested that the characteristic period of the seismic design response spectrmn of UHV electrical equipment should select 0. 9s in order to effectively guarantee the seismic safety of UHV electrical equipment.

Predicting stability of integrated circuit test equipment using upper side boundary values of normal distribution

In response to the growing complexity and performance of integrated circuit(IC),there is an urgent need to enhance the testing and stability of IC test equipment.A method was proposed to predict equipment stability using the upper side boundary value of normal distribution.Initially,the K-means clustering algorithm classifies and analyzes sample data.The accuracy of this boundary value is compared under two common confidence levels to select the optimal threshold.A range is then defined to categorize unqualified test data.Through experimental verification,the method achieves the purpose of measuring the stability of qualitative IC equipment through a deterministic threshold value and judging the stability of the equipment by comparing the number of unqualified data with the threshold value,which realizes the goal of long-term operation monitoring and stability analysis of IC test equipment.

Prediction Model for Cooling of an Electrical Unit with Time-Dependent Heat Generation

The satisfactory performance of electrical equipments depends on their operating temperature. In order to maintain these devices within the safe temperature limits, an effective cooling is needed. High heat transfer rate of compact in size and reliable operation are the challenges of a thermal design engineer of electronic equipment. Then, it has been simulated the transient a three-dimensional model to study the heating phenomenon with two assumption values of heat generation. To control for the working of this equipment, cooling process was modeled by choosing one from different cooling technique. Constant low speed fan at one direction of air flow was used for cooling to predict the reducing of heating temperature through working of this equipment. Numerical Solution of finite difference time domain method （FDTD） has been utilized to simulate the temporal and spatial temperature profiles through two processes, which would minimize the solution errors.

Improvement potential of today＇s WEEE recycling performance： The case of LCD TVs in Belgium

Waste of electrical and electronic equipment （WEEE） constitutes one of the most relevant waste streams because of the quantity and presence of valuable materials. However, there is limited knowledge on the resource potential of urban mining WEEE, as data on material composition, and the efficiency of current recycling treatments are still scarce. In this article, an evaluationof the recycling performance at a national level for one of the fastest growing e-waste streams： LCD TVs is carried out through the following four steps. Firstly, material characterisation is performed by means of sampling of the waste stream. Secondly, a material flow analysis is conducted by evaluating the separation performance of a recycling plant in Belgium..Thirdly, the recovered economic value and avoided environmental impact （EI） of the analysed recycling system is assessed. Finally, the potential of urban mining for Belgium is forecasted. The analysis shows that while recycling performance for ferrous metals and aluminium are relatively high; there is substantial room to better close the material loops for precious metals （PM） and plastics. PMs and plastics account for 66 % of the economic value in LCD TVs and 57% of the El. With the current, commonly applied recycling technology only one-third of the PM and housing plastics are recycled; meaning that for＇these material＇s, at a national level for Belgium, there is a potential for improvement that represents 3.3 million euros in 2016 and 6.8 million euros in 2025.