Dynamics and Predictive Control of Gas Phase Propylene Polymerization in Fluidized Bed Reactors

A two-phase dynamic model,describing gas phase propylene polymerization in a fluidized bed reactor,was used to explore the dynamic behavior and process control of the polypropylene production rate and reactor temperature.The open loop analysis revealed the nonlinear behavior of the polypropylene fluidized bed reactor,justifying the use of an advanced control algorithm for efficient control of the process variables.In this case,a centralized model predictive control(MPC) technique was implemented to control the polypropylene production rate and reactor temperature by manipulating the catalyst feed rate and cooling water flow rate respectively.The corresponding MPC controller was able to track changes in the setpoint smoothly for the reactor temperature and production rate while the setpoint tracking of the conventional proportional-integral(PI) controller was oscillatory with overshoots and obvious interaction between the reactor temperature and production rate loops.The MPC was able to produce controller moves which not only were well within the specified input constraints for both control variables,but also non-aggressive and sufficiently smooth for practical implementations.Furthermore,the closed loop dynamic simulations indicated that the speed of rejecting the process disturbances for the MPC controller were also acceptable for both controlled variables.

Mathematical Model and Advanced Control for Gas-phase Olefin Polymerization in Fluidized-bed Catalytic Reactors

在这研究，在为煤气阶段的催化石蜡聚合建模的开发用 Ziegler-Natta 催化剂的使流体化床的反应堆(FBR ) 被介绍。在乳剂阶段说明在稳固的粒子和包围气体之间的质量和传热的修改数学模型在这个工作被开发包括地点激活反应。在现在的学习开发的这个模型是随后与著名模型相比，也就是水泡生长，混合得好并且为多孔、非多孔的催化剂的模子常数水泡尺寸模型。我们从模型获得了的结果离经常的水泡尺寸模型很靠近，在反应的开始的混合得好的模型和水泡生长模型但是它的全面行为变化了并且在半工作时间以后与水泡生长模型和经常的水泡尺寸模型相比接近混合得好的模型。神经网络的基于的预兆的控制器被实现控制系统，给可接受的结果并且与常规 PID 控制器相比。

Explainable deep transfer learning for energy efficiency prediction based on uncertainty detection and identification

Energy efficiency is an important aspect of increasing production capacity, minimizing environmental impact, and reducing energy usage in the petrochemical industries. However, in practice, data quality can be degraded by measurement malfunction throughout the operation, leading to unreliable and inaccurate prediction results. Therefore, this paper presents a transfer learning fault detection and identification-energy efficiency predictor (TFDI-EEP) model formulated using long short-term memory. The model aims to predict the energy efficiency of the petrochemical process under uncertainty by using the knowledge gained from the uncertainty detection task to improve prediction performance. The transfer procedure resolves weight initialization by applying partial layer freezing before fine-tuning the additional part of the model. The performance of the proposed model is verified on a wide range of fault variations to thoroughly examine the maximum contribution of faults that the model can tolerate. The results indicate that the TFDI-EEP achieved the highest r-squared and lowest error in the testing step for both the 10% and 20% fault variation datasets compared to other conventional methods. Furthermore, the revelation of interconnection between domains shows that the proposed model can also identify strong fault-correlated features, enhancing monitoring ability and strengthening the robustness and reliability of the model observed by the number of outliers. The transfer parameter improves the prediction performance by 9.86% based on detection accuracy and achieves an r-squared greater than 0.95 on the 40% testing fault variation.

Energy efficiency and savings analysis with multirate sampling for petrochemical process using convolutional neural network-based transfer learning

Energy efficiency in the petrochemical industry is crucial in reducing energy consumption and environmental impact.An accurate energy efficiency model will provide valuable insight for supporting operational adjustment decisions.In practice,due to inconsistent sampling intervals in the petrochemical industry,the traditional approach for obtaining energy efficiency may be unreliable and difficult to handle these multirate data char-acteristics.Therefore,in this paper,a multi-channel convolutional neural network model integrating a model parameter-based transfer learning approach is proposed to improve the prediction of energy efficiency under inconsistent sampling intervals.The multi-channel structure aims to recognize a different pattern from the dataset by convolving the information along the time dimension.Concurrently,transfer learning allows the model to learn a new pattern of input after the model is fully trained.Finally,the performance for energy ef-ficiency prediction and saving analysis is validated by applying it to the vinyl chloride monomer production case study.The result shows that the proposed model outperformed traditional models and typical convolutional neural network structures in terms of accuracy and reproducibility,with an r-square of 0.97.The utilization of transfer learning prevents a significant drop in performance and enhances adaptability in model learning on real-time energy tracking.Moreover,the energy gap analysis of the prediction result identified a significant energysaving potential,which would decrease annual energy consumption by 7.25%on average and a 5,709-ton reduction in carbon dioxide emissions.