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Mathematical Model and Advanced control for Gas Phase Olefin Polymerization in Fluidized-Bed Catalytic Reactors

机译:流化床催化反应器中气相烯烃聚合的数学模型和高级控制

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In this study we present the developments in modeling gas-phase catalyzed olefin polymerization fluidized-bed reactors (FBR) using Ziegler-Natta catalyst. 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 wellknown 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, wellmixed model and bubble growth model at the beginning of reaction but its overall behavior changed and is closer to the well-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 compared with the conventional PID controller, giving acceptable results.
机译:在这项研究中,我们介绍了使用Ziegler-Natta催化剂模拟气相催化烯烃聚合流化床反应器(FBR)的进展。在这项工作中开发了改进的数学模型,以解决固体颗粒与周围气体在乳液相之间的质量和热传递,从而包括位点活化反应。随后将本研究中开发的该模型与众所周知的模型进行比较,即用于多孔和非多孔催化剂的气泡增长,良好混合和恒定气泡尺寸模型。从模型中获得的结果在反应开始时非常接近恒定气泡大小模型,良好混合模型和气泡生长模型,但与气泡增长模型和常数相比,其总体行为发生了变化,并且更接近均匀混合模型。运行半小时后的气泡尺寸模型。实现了基于神经网络的预测控制器来控制系统,并将其与常规PID控制器进行比较,得出了可接受的结果。

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