炼钢转炉氧枪预设开度对转炉供氧的影响

氧枪是转炉炼钢的关键设备。氧枪的主要作用是向熔池供氧和传氧,氧枪开氧供应时的预设开度直接影响转炉开吹时的供氧强度,而转炉冶炼尤其是开吹时的供氧强度将会直接决定能否在熔池内产生碳氧反映。因此,转炉氧枪开氧供应时的预设开度是否合理,将直接影响转炉的正常冶炼。

涡轴五燃气轮机燃用半水煤气的试验研究

介绍了涡轴五燃气轮机燃用半水煤气 (热值为 890 0 k J/ Nm3 )的燃气发生器的整机运转性能试验。分析了燃气轮机燃用低热值的气体燃料后对起动点火、起动加速、燃气温度场分布和整机性能的影响。试验结果表明 ,燃气轮机燃用半水煤气后 ,它的点火可靠性、燃烧稳定性及燃气温度场分布品质均能适应燃气轮机的要求 ,与原燃油燃机相比较 。

Research on software development of air temperature prediction in coal face

With ever-increasing depth of coal mine and the continuous improvement of mechanization, heat damage has become one of the major disasters in coal mine exploitation. Established the temperature prediction models suitable for different kinds of tunnels through analysis of the heat of shafts, roadways and working faces. The average annual air temperature prediction equation from the inlets of shafts to the working faces was derived. The formula was deduced using combine method of iteration and direct calculation. The method can improve the precision of air temperature prediction, so we could establish the whole pathway air temperature prediction model with high precision. Emphasizing on the effects of leakage air to air temperature of working face and using the ideology of the finite difference method and considering the differential equation of inlet and outlet at different stages, this method can significantly improve the accuracy of temperature prediction. Program development uses Visual Basic 6.0 Language, and the Origin software was used to fit the relevant data. The predicted results shows that the air temperature generally tends to rapidly increase in the air inlet, then changes slowly on working face, and finally increases sharply in air outlet in the condition of goaf air leakage. The condition is in general consistent with the air temperature change tendency of working face with U-type ventilation system. The software can provide reliable scientific basis for reasonable ventilation, cooling measures and management of coal mine thermal hazards.

Combined ANN prediction model for failure depth of coal seam floors

Failure depth of coal seam floors is one of the important considerations that must be kept in mind when mining is carried out above a confined aquifer. In order to study the factors that affect the failure depth of coal seam floors such as mining depth, coal seam pitch, mining thickness, workface length and faults, we propose a combined artificial neural networks (ANN) prediction model for failure depth of coal seam floors on the basis of existing engineering data by using genetic algorithms to train the ANN. A practical engineering application at the Taoyuan Coal Mine indicates that this method can effectively determine the network struc- ture and training parameters, with the predicted results agreeing with practical measurements. Therefore, this method can be applied to relevant engineering projects with satisfactory results.

Prediction and calculation for new energy development

Some important questions for new energy development were discussed, such as the prediction and calculation of sea surface temperature, ocean wave, offshore platform price, typhoon track, fire status, vibration due to earthquake, energy price, stock market’s trend and so on with the fractal methods (including the four ones of constant dimension fractal, variable dimension fractal, complex number dimension fractal and fractal series) and the improved rescaled range analysis (R/S analysis).

Empirical analysis of network measures for predicting high severity software faults

Network measures are useful for predicting fault-prone modules. However, existing work has not distinguished faults according to their severity. In practice, high severity faults cause serious problems and require further attention. In this study, we explored the utility of network measures in high severity faultproneness prediction. We constructed software source code networks for four open-source projects by extracting the dependencies between modules. We then used univariate logistic regression to investigate the associations between each network measure and fault-proneness at a high severity level. We built multivariate prediction models to examine their explanatory ability for fault-proneness, as well as evaluated their predictive effectiveness compared to code metrics under forward-release and cross-project predictions. The results revealed the following:(1) most network measures are significantly related to high severity fault-proneness;(2) network measures generally have comparable explanatory abilities and predictive powers to those of code metrics; and(3) network measures are very unstable for cross-project predictions. These results indicate that network measures are of practical value in high severity fault-proneness prediction.

An effective fault prediction model developed using an extreme learning machine with various kernel methods

System analysts often use software fault prediction models to identify fault-prone modules during the design phase of the software development life cycle. The models help predict faulty modules based on the software metrics that are input to the models. In this study, we consider 20 types of metrics to develop a model using an extreme learning machine associated with various kernel methods. We evaluate the effectiveness of the mode using a proposed framework based on the cost and efficiency in the testing phases. The evaluation process is carried out by considering case studies for 30 object-oriented software systems. Experimental results demonstrate that the application of a fault prediction model is suitable for projects with the percentage of faulty classes below a certain threshold, which depends on the efficiency of fault identification(low: 47.28%; median: 39.24%; high: 25.72%). We consider nine feature selection techniques to remove the irrelevant metrics and to select the best set of source code metrics for fault prediction.