【作者】 张玉东；

【导师】 鄂加强； 谭军礼；

【作者基本信息】 湖南大学 ， 机械工程， 2008， 硕士

【摘要】 长期以来,无热再生式空气干燥器的控制技术导致在进气工况温度较低和压力降低变化时,出现再生气不必要的能耗浪费和干燥度降低等问题,而根据用户的实际露点要求和工况变化及时调整干燥器的吸附、再生和均压时间,并采用传感器技术与自控技术相结合对进气温度和压力参数进行智能控制是保证无热再生式空气干燥器可靠性高以及节能效果显著的重要技术条件。为此,本文采用泛函分析方法和自适应变尺度粒子群算法进行再生气量的最优化控制,采用神经元-模糊推理融合的组合控制器进行再生气量以及温度和压力的智能控制,论文主要工作与创新之处如下:(1)建立了再生过程再生气流量消耗的真实目标泛函,利用自适应变尺度粒子群优化算法对单位时间内再生气体量消耗目标函数的全局优化问题进行优化,设计了再生过程再生气量泛函优化器,为再生过程在线优化控制以及节能降耗提供了理论基础。(2)设计了可克服普通变结构控制选择切换点时控制参数突变的困难,实现切换区域内的相对平滑切换的神经元-模糊推理融合的组合控制器,仿真结果表明,该组合控制器跟踪性能好,抗干扰能力强,响应快,具有较强的鲁棒性。(3)开发了无热再生式空气干燥器PLC控制系统,并采用神经元-模糊推理融合的组合控制器对再生气流以及流程进行智能控制。应用结果表明,在试运行再生气耗量泛函优化器后,空气压缩机电费由5.3万元下降到4.6万元以下,每年至少可降低电费成本15%左右。更多还原

【Abstract】 For a long time, traditional control methods of heatless regeneration air dryers caused a lot of problems such as energy waste of regeneration gas and decrease of aridity when the intake air temperatures is low and the pressure drops. The high reliability and remarkable energy-saving effect can be obtained by timely adjusting the time of adsorption, regeneration and pressure equalization based on the user’s actual requirements of dew point and the change of operation conditions, and using of sensor technology and control technology for intelligent control of intake air temperatures and pressure parameters. In this paper, we deal with the optimal control of the regeneration gas quantity by the methods of functional analysis and adaptive variable metric particle swarm optimization, and intelligent control of the regeneration gas quantity, air temperatures and pressure by a combination controller based on the neuron fuzzy inference system. The main works of the paper is as follows:(1) The practical cost functional of regeneration gas consumption during regeneration is established. The consumption objective function of the regeneration gas quantity in unit time is optimized based on adaptive variable metric particle swarm optimization. The functional optimizer of the regeneration gas quantity is designed, which provides a theoretical basis for the online optimizing control and energy saving during regeneration.(2) A combination controller based on the neuron fuzzy inference system is designed. The simulation results show that the combination controller has good tracking performance, strong anti-interference ability, fast response and good robustness.(3) A PLC control system of heatless regeneration air dryers is developed. Then, the combination controller of the neuron fuzzy inference system is used to intelligent control the regeneration flow and process.The application results show that the electricity charge of air compressor dropped to 46,000 RMB from 53,000 RMB, and the cast of electricity charge will be reduced at least 15% every year when the functional optimizer of the regeneration gas consumption has been test run.更多还原