摘要
随着全球经济的飞速发展,高炉炼铁追求的目标也更高。鼓风机是高炉炼铁的核心设备,控制喘振的发生势在必行。喘振是高炉鼓风机特有的不稳定运行工况,生产过程中如果发生喘振而没有及时采取控制措施,便会造成很大经济损失,同时会对鼓风机在极短时间内造成重大损害。通过BP神经网络对高炉鼓风机是否发生喘振进行检测,再采用恒压逼近防喘振线的控制策略及防喘振阀模糊PID控制策略加以控制,实现了高炉鼓风机安全有效运行的目标。其中,恒压逼近防喘振线的控制策略较固定极限流量法和可变极限流量法提高了鼓风机的性能和效率,并且加大了有效的工作区间;防喘振阀开度的模糊PID控制策略,使动态响应及控制精度得到了很大的提高。Matlab仿真试验结果表明,模糊PID控制稳态调节时间较短,响应速度加快,超调量明显减少,控制精度及稳定性良好。这些控制方法的共同作用,使监控系统达到了提高工作效率和减少能耗的控制目标,使控制系统的实时性、可靠性、自动化水平得到了全面的提升。
With the rapid development of the global economy,the goal of blast furnace ironmaking is higher. The blower is the core equipment of blast furnace ironmaking,and it is imperative to control the surge. The surge is not stable operation of blast furnace blower unique production process,if the surge happens and there is no time to take control measures,it will cause great economic loss,but also make the blower causes serious damage in a very short period of time. Test whether for blast furnace blower surge by BP neural network,then the approximate control strategy are applied are applied of anti surge line of anti surge valve and constant pressure fuzzy PID control strategy are applied to control,realize the effective operation of the blast furnace blower safety target. The constant pressure approximation control strategy of anti surge line than the fixed limit flow and variable limit flow method to improve the performance and efficiency of the blower,and increase the effective working range; anti surge valve opening degree of fuzzy PID control strategy,the dynamic response and control precision has been greatly improved.Finally,the Matlab simulation,results show that the fuzzy PID control has a shorter steady-state regulation time,faster response speed,less overshoot,better control accuracy and good stability. The joint action of these control methods makes the monitoring system achieve the control goal of improving work efficiency and reducing energy consumption,so that the real-time,reliability and automation level of the control system have been comprehensively promoted.
出处
《自动化仪表》
CAS
2017年第8期33-36,44,共5页
Process Automation Instrumentation
基金
内蒙古自治区自然科学基金资助项目(2017MS0603)