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气相石蜡聚合在流化床催化反应器中的数学模型和先进控制

机译:气相石蜡聚合在流化床催化反应器中的数学模型和先进控制

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摘要

In this study, the developments in modeling gas-phase catalyzed olefin polymerization fluidized-bed reactors (FBR) using Ziegler-Natta catalyst is presented. The modified mathematical model to account for mass and heat transfer between the solid particles and the surrounding gas in the emulsion phase is developed in this work to include site activation reaction. This model developed in the present study is subsequently compared with well-known models, namely, the bubble-growth, well-mixed and the constant bubble size models for porous and non porous catalyst. The results we obtained from the model was very close to the constant bubble size model, well-mixed model and bubble growth model at the beginning of the reaction but its overall behavior changed and is closer to the weft-mixed model compared with the bubble growth model and constant bubble size model after half an hour of operation. Neural-network based predictive controller are implemented to control the system and com-pared with the conventional PID controller, giving acceptable results.
机译:在该研究中,介绍了使用齐格勒 - 纳塔催化剂建模气相催化的烯烃聚合流化床反应器(FBR)的发展。在该工作中开发了改进的数学模型,以考虑固体颗粒和乳液相中的周围气体之间的热传递,包括位点活化反应。随后与众所周知的模型进行比较了本研究的该模型,即多孔和非多孔催化剂的气泡生长,混合良好和恒定气泡尺寸模型。我们从模型中获得的结果非常接近反应开始时恒定的气泡尺寸模型,良好的模型和泡沫生长模型,但与泡沫增长相比,其整体行为发生变化并更接近纬编混合模型半小时后半小时后模型和恒定泡沫尺寸模型。基于神经网络的预测控制器被实现为控制系统和与传统PID控制器的COM,给出可接受的结果。

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