This paper mainly studied how to determine the best location of the self-operated flow control valve at the heating system entrance.Since the location of regulating valve directly affects the pipe network performance,...This paper mainly studied how to determine the best location of the self-operated flow control valve at the heating system entrance.Since the location of regulating valve directly affects the pipe network performance,the simulation and analysis of pressure change in heating system was carried out with Computational Fluid Dynamics(CFD)software.The study shows the best location of regulating valve varies with the change of the supply and return pipe length when the heating area of each user is small,and when the heating area of each user is large(2 000 000～3 000 000 m2),the best location is on the supply pipe.展开更多
针对电动调节阀控制系统在实际生产过程中存在的非线性、多扰动等问题,提出一种基于改进蚁群算法优化单神经元PID(proportional integral derivative)的控制方法并将其应用于阀门开度控制中。该方法利用单神经元网络的自学习和自适应能...针对电动调节阀控制系统在实际生产过程中存在的非线性、多扰动等问题,提出一种基于改进蚁群算法优化单神经元PID(proportional integral derivative)的控制方法并将其应用于阀门开度控制中。该方法利用单神经元网络的自学习和自适应能力,实现PID控制参数的在线整定,并采用改进的蚁群优化算法优化单神经元PID中的学习速率和神经元比例系数,有效克服了单神经元PID中的学习速率和神经元比例系数因经验设定而无法达到预期控制效果的不足。仿真对比结果显示,相比于传统PID、单神经元PID以及基于蚁群优化算法优化单神经元PID 3种控制方法,本文提出的控制方法超调量分别减少了10.2%、6.1%和1.8%,同时调节时间也相应缩短了0.22、0.07、0.03 s,并且表现出更强的自适应和抗干扰能力,能够使阀门开度控制更加稳定可靠。展开更多
基金Supported by the projects of Beijing Municipal Educational Committee(KM200710016012)Beijing Municipal Office of Philosophy and Social Science(06BaJG0095)Beijing Municipal Organization Committee(20071D0501700235)
文摘This paper mainly studied how to determine the best location of the self-operated flow control valve at the heating system entrance.Since the location of regulating valve directly affects the pipe network performance,the simulation and analysis of pressure change in heating system was carried out with Computational Fluid Dynamics(CFD)software.The study shows the best location of regulating valve varies with the change of the supply and return pipe length when the heating area of each user is small,and when the heating area of each user is large(2 000 000～3 000 000 m2),the best location is on the supply pipe.
文摘针对电动调节阀控制系统在实际生产过程中存在的非线性、多扰动等问题,提出一种基于改进蚁群算法优化单神经元PID(proportional integral derivative)的控制方法并将其应用于阀门开度控制中。该方法利用单神经元网络的自学习和自适应能力,实现PID控制参数的在线整定,并采用改进的蚁群优化算法优化单神经元PID中的学习速率和神经元比例系数,有效克服了单神经元PID中的学习速率和神经元比例系数因经验设定而无法达到预期控制效果的不足。仿真对比结果显示,相比于传统PID、单神经元PID以及基于蚁群优化算法优化单神经元PID 3种控制方法,本文提出的控制方法超调量分别减少了10.2%、6.1%和1.8%,同时调节时间也相应缩短了0.22、0.07、0.03 s,并且表现出更强的自适应和抗干扰能力,能够使阀门开度控制更加稳定可靠。
