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Nonlinear Predictive Control for Polypropylene Grade Transition Process Based on Neural Network Modeling

Author: WangJingFang
Tutor: YuLi；ZouTao
School: Zhejiang University of Technology
Course: Systems Engineering
Keywords: Polypropylene Grade Transition Neural Network Differential Evolution Nonlinear Predictive Control
CLC: TQ325.14
Type: Master's thesis
Year: 2010
Downloads: 38
Quote: 0
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Abstract


Polypropylene is a synthetic resin based polymerization of propylene monomer is important in the plastics industry. Because of its excellent mechanical properties, corrosion resistance, heat resistance and injection performance, are widely used in light industry, chemical industry, chemical fiber, building materials, household appliances, packaging, automotive and other fields, as the country's core pillar industry of one. Polypropylene production plant from a set of production process to switch to another set of the production process operation is called grade transition. Due to the diversity of market demand, forcing companies to frequent grade transition, taking into account the presence of the transition material, the use of the traditional way will result in huge economic losses, the optimal control of the grade transition process has important theoretical and practical significance. In this paper, the Spheripol bulk polypropylene production process, for example, polypropylene continuous production of the product during grade transition modeling and control optimization study. The main contents and results are as follows: 1. Introduced of polypropylene production status quo at home and abroad as well as market supply and demand situation, pointed out the significance of the research grade transition process; From the technological characteristics of the polypropylene production process on polypropylene polymerization mechanism, domestic and foreign production process and grade transition research system summary. 2. To Spheripol Production, for example, the combination of system data collection, improved nonlinear least squares and BP neural network to establish a stable grade model. Consider modeling effects and universality, delay BP neural network to establish the grade transition model of the production process, the delay of BP-BP-BP network structure to establish the whole grade transition process model. The simulation results demonstrate the effectiveness of the modeling method. 3. The traditional optimization for nonlinear model predictive control algorithm to calculate the efficiency is low and easy to fall into local optimal solution, an improved differential evolution algorithm to optimize the running track of the grade transition model and nonlinear model predictive control is introduced into the dynamic grade transition model, Improved differential evolution algorithm-based neural network nonlinear predictive control of grade transition process line control. The algorithm is applied to the polypropylene grade transition process simulation study, can greatly reduce the switching time and reduce the transition material production, significantly improve economic efficiency. Finally, the full text of the summary and outlook.

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CLC: > Industrial Technology > Chemical Industry > Synthetic resins and plastics industry > Polymer resin and plastic > Polyolefin plastic > Polypropylene
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