转炉炉口的微差压调控对提升煤气回收品质具有重要意义。为了合理调控炉口压力,减少吸入空气量,提升回收煤气热值,结合转炉炉内化学反应、历史吹氧数据和神经网络算法,建立了炉气预测模型,模型精度可达96.3%;根据微差压与风机转速、环缝开度、烟罩高度、烟罩开度以及炉气量之间的关系,建立了炉口微差压平衡模型,并耦合炉气预测模型构成微差压预测双驱动模型。炉气预测模型提供预测炉气量,微差压平衡模型提供微差压平衡关系式,两者结合设定的风机转速、环缝开度、烟罩高度,耦合成为微差压预测双驱动模型。当预测的微差压超过了设定值,对风机转速、环缝开度、烟罩开度、烟罩高度等影响因素进行调节,控制微差压在合理的范围内。通过实际生产数据分析,双驱动模型预测与调控结果与设定的微差压平均值相对误差为2.7%,吹炼期间炉口压力的平均值由13.42 Pa降低到4.88 Pa,平均每炉吸入空气量由原来的40.26 km3减少为14.64 km3。煤气热值（标准态）从7 422 kJ/m3增加到8 812 kJ/m3,提高了18.73%,表明该模型可实现精准预测和调控微差压,改善以往预测方法时间滞后的缺点,为提升转炉煤气热值提供了有效的方法。
Abstract
The micro-differential pressure control at the converter mouth is of great significance to improving the quality of gas recovery. In order to reasonably regulate the furnace mouth pressure, reduce the amount of air inhalation, and increase the calorific value of the recovered gas, a converter gas prediction model was established based on chemical reactions in the converter, historical oxygen blowing data, and neural network algorithms. The accuracy of the model can reach 96.3%. Based on the relationship between the micro-differential pressure and the fan speed, annular seam opening, hood height, hood opening and converter gas volume, a converter mouth micro-differential pressure balance model was established, and coupled with the converter gas prediction model to form a micro-differential pressure prediction dual drive model. The converter gas prediction model provides the predicted converter gas volume, and the micro-differential pressure balance model provides the micro-differential pressure balance relationship. The two are combined with the set fan speed, ring gap opening, and hood height to form a micro-differential pressure prediction dual-drive model. When the predicted differential pressure exceeds the set value, the fan speed, ring gap opening, hood opening, hood height and other influencing factors are adjusted to control the differential pressure within a reasonable range. Through the analysis of actual production data, the relative error between the prediction and control results of the dual-drive model and the set average value of micro-differential pressure is 2.7%. The average converter mouth pressure during blowing is reduced from 13.42 Pa to 4.88 Pa. The average suction per furnace The air volume is reduced from the original 40.26 km3 to 14.64 km3. The calorific value （standard state） of coal gas increased from 7 422 kJ/m3 to 8 812 kJ/m3, an increase of 18.73%, indicating that the model can accurately predict and regulate the micro-differential pressure, improve the shortcomings of the time lag of previous prediction methods, and provide a way to improve converter gas calorific value provides an effective method.
关键词
微差压 /
转炉煤气 /
预测与调控 /
双驱动模型 /
转炉炉口
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Key words
micro differential pressure /
converter gas /
prediction and regulation /
dual-driven model /
converter mouth
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脚注
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基金
国家重点研发计划资助项目(2020YFB1711101）; 安徽高校自然科学研究资助项目（KJ2021A0411）
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