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轉(zhuǎn)爐煉鋼過程中，終點的精確控制是合理組織生產(chǎn)、提高鍋爐板 質(zhì)量和降低煉鋼成本的重要保證。目前煉鋼終點還不能達到一次命中，還需要進行副槍檢測，根據(jù)檢測結(jié)果進行補吹。

In the process of converter steelmaking, the precise control of the end point is the important guarantee for the rational organization of production, the improvement of the quality of the boiler plate and the reduction of the cost of the steel making. At present, the steelmaking end point can not reach a hit, but also need to carry on the auxiliary gun detection, according to the detection results to carry on the repair.

目前，國內(nèi)在轉(zhuǎn)爐控制的研究多集中在終點預測領域，而對轉(zhuǎn)爐工藝參數(shù)優(yōu)化的研究并不多見轉(zhuǎn)爐煉鋼是一個具有多輸入多輸出、存在嚴重非線性并具有大滯后特征的復雜工業(yè)過程，而且對過程無法做到準確適時的檢測，因此給其控制帶來很大困難。

At present, the domestic research on converter control is more concentrated in the end forecast field, while the research on the optimization of technological parameters of converter steelmaking is rare is a multi input and multi output, serious nonlinear and complex industrial process with large lag features, and can not be accurate timely detection of the process, so to control the bring great difficulties.

煉鋼終點的準確控制是煉鋼廠確保生產(chǎn)有序合理、保證鋼水質(zhì)量的重要前提。武漢科技大學的學者從輔料資源運行特性的角度分析了煉鋼終點控制過程工藝，并在此基礎上建立了以改進粒子群優(yōu)化算法求解的終點優(yōu)化控制模型，得出了終點優(yōu)化控制策略。

The accurate control of steelmaking end point is an important prerequisite to ensure the orderly and reasonable production and ensure the quality of molten steel. Wuhan University of Science and Technology scholars from the perspective of resource materials operation characteristics analysis of process Steelmaking Endpoint control process, and on this basis to establish a modified particle swarm optimization algorithm for solving the optimal control model of the end, the end of optimal control strategy.

試驗結(jié)果表明，終點預報誤差ω(ΔC)<±0.03%的命中率提高至93.1%，ω(ΔC)＜±12K的命中率為94%，補吹氧時間為2.5min，輔料資源量節(jié)約15%左右，該模型達到了較好的優(yōu)化效果。

Experimental results show that the endpoint prediction error (C < omega) + 0.03% hit rate increased to 93.1% (C < omega) + 12K hit rate of 94%, up blowing time is 2.5min, the amount of resources saving materials about 15%, the model has a good optimization effect.

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