Liquid convective heat transfer in microchannels exhibits high efficiency.However,the large pressure drop causes much concern for practical application.Water flowing in hydrophobic tubes shows low pressure drop owing ...Liquid convective heat transfer in microchannels exhibits high efficiency.However,the large pressure drop causes much concern for practical application.Water flowing in hydrophobic tubes shows low pressure drop owing to the slippage on the tube walls.Super-hydrophobic/hydrophilic micro aluminum tubes of 0.68mm inner diameter were fabricated with a two step procedure of chemical etching and then surface chemical modification.A kind of micro-nanometric hierarchical structure was formed on the surface,which could trap air serving as the slip agent.Heat transfer and fluid flow of deioned water flowing laminary in the super-hydrophobic/hydrophilic microchannels were studied experimentally.The results showed that the air-layer existing in the micro-nano hierarchical structures of the super-hydrophobic surface decreased flow resistance evidently but decreased heat transfer coefficient only a little,which was still higher than the superficial heat transfer coefficient while considering the heat conduction of stationary nanolayer of air.It was supposed that eddy flow was generated in the micro-nano bubbles by the slip flow of water on the tube wall,which enhanced the heat transfer.展开更多
煤气化过程由于煤质波动、反应系统复杂及仪表测量滞后,难以及时反映气化炉实时运行状况,使得气化炉运行存在操作保守、响应滞后等问题。针对上述问题,将先进控制技术(Advanced Process Control Technology)应用在气化炉上,有助于实现...煤气化过程由于煤质波动、反应系统复杂及仪表测量滞后,难以及时反映气化炉实时运行状况,使得气化炉运行存在操作保守、响应滞后等问题。针对上述问题,将先进控制技术(Advanced Process Control Technology)应用在气化炉上,有助于实现气化过程的卡边操作,提高装置的运行平稳率并优化空间。国家能源投资集团榆林化工公司3000 t/d水煤浆气化炉已开车成功,完备的测量仪表和可靠的DCS系统使其具有作为先进控制技术实施的基础。以榆林化工公司的超大型水煤浆气化炉作为智能工厂开发实践的切入点,从提高煤气化装置的自控投用率、气化炉数字孪生、多变量协同约束控制3方面介绍先进控制技术在煤气化装置中的应用与研究进展。结果表明:通过回路整定软件PID-GO对1号气化炉系统的35条控制回路进行PID整定,气化装置的优良投用率由44%提高至84%以上,自控投用率由65%提高至91%以上;通过Unisim建立了稳态和动态数字孪生模型。通过稳态模型计算的合成气温度、粗合成气流量及主要气体组分均能与运行值吻合,模拟相对误差小于5%,动态模型捕捉到煤浆流量在1 min内由140 m^(3)/h降至100 m^(3)/h,出现气化温度由1276℃升高至1461℃的短时间超温现象;在仿真平台上实现了多变量协同优化软件PD-Master与Unisim模型的动态链接,通过PD-Master的滚动优化,气化炉的优化变量均达到了既定目标,氧煤比、气化炉温、合成气流量及有效气含量计算值与目标值误差在2.2%以内,其中权重最高的变量C_(V1)和C_(V2)的计算偏差分别为-0.06%和0.11%。展开更多
文摘Liquid convective heat transfer in microchannels exhibits high efficiency.However,the large pressure drop causes much concern for practical application.Water flowing in hydrophobic tubes shows low pressure drop owing to the slippage on the tube walls.Super-hydrophobic/hydrophilic micro aluminum tubes of 0.68mm inner diameter were fabricated with a two step procedure of chemical etching and then surface chemical modification.A kind of micro-nanometric hierarchical structure was formed on the surface,which could trap air serving as the slip agent.Heat transfer and fluid flow of deioned water flowing laminary in the super-hydrophobic/hydrophilic microchannels were studied experimentally.The results showed that the air-layer existing in the micro-nano hierarchical structures of the super-hydrophobic surface decreased flow resistance evidently but decreased heat transfer coefficient only a little,which was still higher than the superficial heat transfer coefficient while considering the heat conduction of stationary nanolayer of air.It was supposed that eddy flow was generated in the micro-nano bubbles by the slip flow of water on the tube wall,which enhanced the heat transfer.
文摘煤气化过程由于煤质波动、反应系统复杂及仪表测量滞后,难以及时反映气化炉实时运行状况,使得气化炉运行存在操作保守、响应滞后等问题。针对上述问题,将先进控制技术(Advanced Process Control Technology)应用在气化炉上,有助于实现气化过程的卡边操作,提高装置的运行平稳率并优化空间。国家能源投资集团榆林化工公司3000 t/d水煤浆气化炉已开车成功,完备的测量仪表和可靠的DCS系统使其具有作为先进控制技术实施的基础。以榆林化工公司的超大型水煤浆气化炉作为智能工厂开发实践的切入点,从提高煤气化装置的自控投用率、气化炉数字孪生、多变量协同约束控制3方面介绍先进控制技术在煤气化装置中的应用与研究进展。结果表明:通过回路整定软件PID-GO对1号气化炉系统的35条控制回路进行PID整定,气化装置的优良投用率由44%提高至84%以上,自控投用率由65%提高至91%以上;通过Unisim建立了稳态和动态数字孪生模型。通过稳态模型计算的合成气温度、粗合成气流量及主要气体组分均能与运行值吻合,模拟相对误差小于5%,动态模型捕捉到煤浆流量在1 min内由140 m^(3)/h降至100 m^(3)/h,出现气化温度由1276℃升高至1461℃的短时间超温现象;在仿真平台上实现了多变量协同优化软件PD-Master与Unisim模型的动态链接,通过PD-Master的滚动优化,气化炉的优化变量均达到了既定目标,氧煤比、气化炉温、合成气流量及有效气含量计算值与目标值误差在2.2%以内,其中权重最高的变量C_(V1)和C_(V2)的计算偏差分别为-0.06%和0.11%。
