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Multivariable Hybrid Intelligent Optimal Control for Coke Oven Heating Process

机译:焦炉加热过程的多变量混合型智能控制

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Coke oven heating process in coking production is a synthetic process, whose product key index, i.e. flue temperature, is hard to measure online, and the optimal control of such process is very difficult. Based on analysis of its dynamic characteristics and manual operation mode, a multivariable intelligent optimal control method is developed, combining intelligent method, feedforward control and feedback control. The target is the optimization of integrated production indices including coke quality and amount of consumed energy per ton coke. The proposed method is composed by a control loop setting model of flue temperature, hybrid prediction model of the techniques indices, a fault work-condition diagnosis model, feedforward and feedback compensators. The settlement value of control loops of the process are adjusted online automatically according to the targeted integrated production indices and variations of boundary conditions, so the optimal control is achieved. The practical running indicates that the system can realize intelligent control of coke oven heating, stabilize production of coke oven, effectively improve quality of coke and decrease energy consumption, and has great practical value. The application results have proven the effectiveness of the proposed method.
机译:焦炉加热过程在炼焦化生产中是一种合成过程,其产品密钥指数,即烟道温度,很难在线测量,并且对这种过程的最佳控制非常困难。基于分析其动态特性和手动操作模式,开发了一种多变量智能最优控制方法,结合智能方法,前馈控制和反馈控制。目标是优化综合生产指数,包括每吨可口可乐的焦炭质量和消耗的能量。所提出的方法由烟道温度,技术索引的混合预测模型,故障工作条件诊断模型,前馈和反馈补偿器的控制回路设定模型组成。根据目标综合生产指标和边界条件的变化,根据目标综合生产指标在线调整过程的定居点,因此实现了最佳控制。实际运行表明,该系统可以实现焦炉加热的智能控制,稳定焦炉的生产,有效提高焦炭质量并降低能耗,具有很大的实用价值。申请结果证明了该方法的有效性。

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