将神经网络与传统专家系统有机地结合，建立了用于高炉炉况预测与判断的神经网络专家系统。
An expert system based on neural networks for predicting and judging the state of blast furnace is developed by applying the method of combining neural networks with traditional expert system.
正确确定发生炉在气化不同煤种时的正常炉出煤气温度指标，有利于操作人员根据出煤气温度的变化，准确判断并正确处理炉况。
Correctly identify the gas outlet temperature target of different coal will help the operator accurately determine and correctly handle the running condition according to the gas outlet temperature.
实践证明，采用本法炼铁，炉况稳定顺行。
The practice proves that operation of BF is smooth and stable by the ironmaking method.
冷却设备破损向高炉内漏水，影响炉况及操作。
The broken cooling plant leaked through the blast furnace, resulting in poor furnace condition and poor operation.
指出，未燃煤粉的存在对炉况的影响有利有弊。
It points out there are both advantageous and disadvantageous influence of UPC (unburnt pulverized coal) on the work of BF.
提出操业对策以减少炉况波动，减少冷却壁水管破损。
Meanwhile, the operating countermeasures are put forward to reduce the fluctuation of BF condition and the stave pipe damage.
干熄焦可显著改善高炉炉况、降低高炉消耗、提高喷煤比。
Using dry cooling coke may obviously improve blast furnace conditions, decrease blast furnace consumption and increase coal ejection ratio.
因此，高炉炉热水平、异常炉况预报一直是国内外研究的热点。
Thus, BF temperature level and exceptional situation are the hot points of research in the field.
因此探索建立神经网络高炉异常炉况判断专家系统是当前的研究方向。
It is the current research direction to set up the neural network expert system of judging the abnormal state of blast furnace.
认为原燃料质量变化，特别是人炉焦炭热性能的急剧下降，引发了炉况波动。
It is believed that the quality change of material and fuel, especially the big reduction of coke thermal property, results in the furnace condition fluctuation.
研究石灰炉控制模型，建立了专家知识库，开发了石灰炉炉况在线诊断模型和软件；
The expert repository was established by studying the control model of lime furnace. Online diagnosing model and software of operation conditions of lime furnace were also developed.
焦炭质量对高炉炉况具有重要影响，会导致高炉炉况变差，不利于生产指标的改善。
The quality of coke have an important effect on the blast furnace conditions. It will cause the variation of blast furnace conditions, which is disadvantageous to production indexes improvement.
介绍2号高炉炉况失常时的处理措施，分析了炉况失常的原因，提出相应的预防措施。
The countermeasures for adverse condition of No. 2 BF have been introduced, and the causes of aberration have been analyzed as well as the relevant preventive measures have been put forward.
碱金属是导致高炉料柱透气性恶化、炉况失常、频繁悬料和在炉身结瘤的一个重要原因。
Alkali metals are one of the important causes that leads to poor permeability of charge column, irregular furnace operation, frequent hanging and scaffolding in the stack.
在处理高炉悬料时长时间亏料加上冷却设备漏水未及时更换，是导致炉况失常的主要原因。
The main causes of aberration are that the condition of deficient burden lasts for a long time and the leaking cooling equipments did not be changed in time during the period of treating BF hanging.
本论文的背景是高炉炉况诊断系统，论文主要包括扩充的时态黑板模型、推理机的设计与实现。
The background of the paper is Status Diagnosis System in Blast Furnaces. The dissertation includes: extended blackboard model, design and realization of inference.
根据高炉传感器的数据，BFDES系统可对高炉炉况作出实时判断，并可预测高炉运行的故障。
On the basis of blast furnace sensor data, BFDES system can make real-time judgement of furnace condition and predict the failure of the blast furnace.
用智能优化设定模型、炉况诊断模型、智能预报模型及回路控制，实现了优化综合生产指标的目标。
Overall production index is achieved using intelligent optimal setting model, fault diagnosis model, intelligent prediction model and loop control.
根据电弧炉的熔炼工艺特性对电弧炉各种炉况进行研究，并给出了一种基于专家系统的电弧炉炉况判断方法。
Based on the research of smelting technics and furnace state, this thesis gives an EAF state judgement method based on expert system.
首钢2号高炉降料面喷补炉衬后送风恢复炉况，送风第2天炉况基本恢复正常，第7天焦炭负荷加重到5.0以上。
Shougang No. 2 BF blows for recovery after spraying painting, furnace condition gets right on No. 2 day and coke burden aggravates upwards 5.0 on No. 7 day, furnace condition recovery is rapid.
通过对高炉操作炉型的分析认为，炼铁4#高炉炉况变差、炉腹结厚的原因是炉渣性能差、碱度高，炉体冷却强度大。
The reasons why the condition of #4 BF is poor and its bosh thickens are that poor performance, high basicity and great cooling intension of slag.
本课题旨在建立神经网络高炉异常炉况判断专家系统，并解决传统专家系统在知识获取方面的“瓶颈”问题，使其具有在线学习能力。
The object of this research is to set up the neural network expert system of judging the abnormal state in acquiring knowledge , so to make it have the ability of self-learning on-line.
本课题旨在建立神经网络高炉异常炉况判断专家系统，并解决传统专家系统在知识获取方面的“瓶颈”问题，使其具有在线学习能力。
The object of this research is to set up the neural network expert system of judging the abnormal state in acquiring knowledge , so to make it have the ability of self-learning on-line.
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