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简述国外新型连续混炼工艺,重点总结和介绍国内低温一次法炼胶工艺的研究开发和应用进展。低温一次法炼胶工艺是将传统的多段混炼改为一次混炼,即胶料通过密炼机高温密炼后,先经过第一台开炼机进行冷却,然后通过中央输送系统对称地分配到周围多台开炼机进行连续低温混炼,直接得到终炼胶,整个过程强化了下辅机的混炼作用,且全过程实现自动化控制,取消了胶料中间传递和反复升降温过程,从而大幅度减少占用场地,降低能耗,缩短混炼时间。橡胶混炼技术未来的发展方向是利用物联网设计理念和可视化监控方式、高度集成成套智能装备的智能化炼胶车间总体设计。 相似文献
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节能降耗活动已经在轮胎行业深入开展。其中,一批新技术、新工艺的推广应用,使轮胎行业万元工业增加值的能耗大大降低。例如,炼胶工序是轮胎生产过程中能耗最高的一道工序,约占整个轮胎生产流程能耗的40%。目前,多家企业成功开发了低温一次法炼胶工艺,将传统的多段混炼简化,即胶料通过密炼机高温密炼后,先经过第1台开炼机进行冷却,再通过中央输送系统对称地分配到周围多台开炼机进行连续低温混炼,直接得到终炼胶,整个过程强化了下辅机的混炼作用,且全过程实现自动控制。 相似文献
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提出一种新的基于递推部分最小二乘(RPLS)算法的自适应在线质量监控策略。利用隐变量选择算法,根据实时采集的现场数据,在不增加计算和存储容量的基础上递推更新RPLS过程监测模型,进而更新Qα控制限,从而使RPLS自适应质量监控系统具有强时变跟踪特性,能够有效克服传统监测算法Qα无法反映系统时变性的缺点,大大降低了监控系统的误报率和漏报率,提高监控系统性能。并根据橡胶混炼过程特点,将此方法运用于该时变间歇过程质量监控中,取得了满意效果。 相似文献
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Online property prediction in industrial rubber mixing processes is not an easy task. An efficient data‐driven prediction model is developed in this work. The regularized extreme learning machine (RELM) is utilized as the fundamental soft sensor model. To better capture distinguished characteristics in multiple recipes and operating modes, a just‐in‐time RELM modeling method is developed. The number of hidden neurons and the value of regularization parameter of the just‐in‐time RELM model can be efficiently selected using a fast leave‐one‐out strategy. Consequently, without the time‐consuming laboratory analysis process, the Mooney viscosity can be online predicted once a mixing batch has been discharged. The industrial Mooney viscosity prediction results show its better prediction performance in comparison with traditional approaches. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45391. 相似文献
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分析了混炼设备的机型分类与对比,以及开炼机与密炼机的容量及功率比较。介绍了各种混炼设备的发展过程与现状,对比了各种混炼设备的特性、适用范围及性能参数。介绍了混炼室、转子及上下辅机的发展概况。最后展望了混炼设备的未来发展。 相似文献
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To overcome the large time-delay in measuring the hardness of mixed rubber, rheological parameters were used to predict the hardness. A novel Q-based model updating strategy was proposed as a universal platform to track time-varying properties. Using a few selected support samples to update the model, the strategy could dramat-ical y save the storage cost and overcome the adverse influence of low signal-to-noise ratio samples. Moreover, it could be applied to any statistical process monitoring system without drastic changes to them, which is practical for industrial practices. As examples, the Q-based strategy was integrated with three popular algorithms (partial least squares (PLS), recursive PLS (RPLS), and kernel PLS (KPLS)) to form novel regression ones, QPLS, QRPLS and QKPLS, respectively. The applications for predicting mixed rubber hardness on a large-scale tire plant in east China prove the theoretical considerations. 相似文献