根据试验数据。确定了合适的烧结工艺参数。
Based on the testing data, the suitable sintering process parameters were defined.
并通过试验，用微波炉模拟微波烧结腔体测得温度数据，建立模型。
In experiment, microwave oven is used to simulate microwave sintering cavity, then temperature data are gained and model is established.
采用现场生产数据，运用数理分析方法，分析了返矿对烧结过程水碳的影响．。
Based on operational data, the influence of teture fines on variation of mios-ture and carbon rate in sintering process was analyzed by mathematical treatment.
为了给烧结返矿配比的确定提供数据上的支持，对烧结返矿量进行预测具有重要的实际意义。
In order to offer the basis of blending ratio in data, it is important to predict the actual output of return mines.
工业实际数据验证表明，智能集成模型的残硫估计误差平均值仅为7.5%，而且真实反映了烧结块残硫的变化趋势，可以为生产操作提供有益的指导。
The estimation model was tested by industrial practical data, its average error is 7.5%. So this model could be used as a guide in practical operation in sintering process.
采用数据分析的方法建立了废气温度上升点、烧结终点、垂直烧结速度等参数的预报模型；
Predicted models of rising position of gas temperature, burn through point, vertical sintering speed, and so on, are established using dada analysis method.
列举了烧结收缩、力学性能和最佳烧结温度等数据。
Sintering shrinkage, mechanical properties and the optimized sintering temperatures are also presented.
文章通过对包钢生产烧结矿质量检测数据的统计分析，研究了包钢烧结矿的化学成分和矿物组成对其冶金性能的影响，提出了包钢烧结矿质量指标的适宜范围。
Based on the testing data of the quality of sinter, the influences of chemical composition and mineral composition on metallurgical properties of the sinter are investigated.
本文利用实际烧结生产数据通过运用人工神经网络技术，不仅对烧结固体燃耗进行了预测，而且研究了各主要参数对烧结固体燃耗的影响。
The neural networks with data from sintering plant is used in this paper. It not only can predict the solid fuel consumption but also can study every kind of parameter as to its influence.
实验的结果验证了现有的烧结理论，并为进一步完善烧结理论以及建立扩散和本构模型提供了高质量的实验数据。
The experiment results are contrasted with the sintering theory and provide an effective experimental data for further analysis the sintering process and the mechanical characteristics of ceramics.
根据试验数据，应用多元非线性回归方法建立了密度与摆放位置关系的数学模型，并讨论了改善烧结密度的措施。
Maths model is built up to show the relationship of density with locating place using multiple nonlinear regression analysis of experimental results.
马钢二烧P LC系统推动了烧结生产自动化，但其生产数据库资源未进行信息开发应用。
The NO. 2 sintering plant of Magang promoted sintering production automation by PLC system, but its data warehouse to produce information was not applied.
通过对两种不同成型压力烧结的试样进行测试，得到不同压力下的材料致密度和铜含量的变化数据。发现随着成型压力的增大，材料的烧结致密度升高。
The effect of densification process on the microstructure of these W-Cu composites and the pressureless agglomeration process of the samples fabricated by variant copper content are investigated.
通过现场采集数据对该模型进行仿真，其实验结果表明，该模型具有较好的学习能力和泛化能力，为烧结终点的预测提供了一种新的解决方法。
The simulation results show that the network has excellent learning capacities and generalization ability. Therefore the model is a new effective approach for BTP prediction.
通过现场采集数据对该模型进行仿真，其实验结果表明，该模型具有较好的学习能力和泛化能力，为烧结终点的预测提供了一种新的解决方法。
The simulation results show that the network has excellent learning capacities and generalization ability. Therefore the model is a new effective approach for BTP prediction.
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