Intelligent detection and yield optimization method for cascade polymerization of high-density polyethylene
A cascade polymerization and high-density polyethylene technology, which is applied in the field of intelligent detection and yield optimization of high-density polyethylene cascade polymerization process, can solve the problem of reducing the fitting accuracy of a single ELM neural network, and achieve fast online Detect, ensure generalization ability, and fast learning effect
Active Publication Date: 2016-04-13
BEIJING UNIV OF CHEM TECH
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However, since the input layer weights of the ELM learning algorithm are randomly generated, these weights are not optimal, so the fitting accuracy of a single ELM neural network may be reduced
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[0023] Such as figure 1 Shown is the flow chart of HDPE cascade polymerization reaction. The reaction process is mainly composed of two slurry reactors. Ethylene is introduced in a gaseous state, dissolved in a solvent and diffused to the surface of catalyst particles. The reaction is accelerated by stirring to form polyethylene particles, which are then dried with a diluent to produce high-density polyethylene. product. Due to its low price and good performance, the HDPE products produced have now become the third most demanded polyolefin variety in the world, and are widely used in film, blow molding, pipes, etc. Among them, the specifications of polyethylene products in the industry are mainly distinguished according to the melt index of the second reactor. Due to the lack of online measurement means, it takes two hours for a laboratory analysis of the melt index. Obviously, there is a big lag in this frequency. When it is found that the quality of the resin is not up to ...
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The invention provides an intelligent detection and yield optimization method for an HDPE (high density polyethylene) cascade polymerization reaction course, which solves the problems that the HDPE cascade polymerization reaction course is complicated in technology, on-line measurement of a key quality variable is difficult, and the operating cost of a production course is high. The method comprises the steps of preprocessing by adopting data correction and data excavation techniques, searching a rule from production and analysis data, adopting an artificial neural network technique to establish an intelligent soft measuring instrument and a polyethylene product unit consumption model, and adopting an extension engineering technique to optimize an artificial neural network structure and improve the neural network modeling accuracy. The method has the characteristics of high response time, high modeling accuracy, high inferential capability and convenience in management, and assists in ensuring safe production of HDPE, improving the quality of a polymer product, and saving the production cost.
Description
technical field [0001] The present invention takes high-density polyethylene (HighDensityPolyethylene, HDPE) cascaded polymerization reaction process as the object, and aims at two-reactor melt index soft measurement and polyethylene product unit consumption, and proposes an online preprocessing method including complex process industrial data , Extreme Learning Machine (Extreme Learning Machine, ELM) algorithm and ELM neural network integration (ELM-E) technology of extension clustering algorithm. Background technique [0002] With the rapid progress of polymer material science and technology and the continuous improvement of production technology, a revolutionary progress has taken place in the field of pipelines, that is, "replacing steel with plastic". In this revolution, high-density polyethylene pipes are favored because of their extremely high mechanical strength and high-speed processing performance. At present, they have been widely used in gas transportation, water...
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IPC IPC(8): G06N3/02
Inventor 徐圆朱群雄彭荻陈彦京贺彦林叶亮亮耿志强林晓勇李芳史晟辉
Owner BEIJING UNIV OF CHEM TECH