Modeling method for catalytic cracking main fractionator with varying-population-size DNA genetic algorithm

A genetic algorithm and catalytic cracking technology, applied in the field of catalytic cracking main fractionator modeling, can solve problems such as slow convergence speed, over-learning, local minimum, etc., and achieve the effect of improving accuracy

Inactive Publication Date: 2012-09-12
ZHEJIANG UNIV
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However, the neural network itself has some defects, such as the network

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  • Modeling method for catalytic cracking main fractionator with varying-population-size DNA genetic algorithm
  • Modeling method for catalytic cracking main fractionator with varying-population-size DNA genetic algorithm
  • Modeling method for catalytic cracking main fractionator with varying-population-size DNA genetic algorithm

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[0073] As an important secondary processing technology in the oil refining industry, the catalytic cracking process can convert heavy raw materials into light products, which has a significant impact on the economic benefits of oil refineries. Among them, the catalytic cracking main fractionator is an important unit to realize product separation. Accurate modeling and operation optimization of the catalytic cracking main fractionator is one of the effective measures to reduce the energy consumption of the catalytic cracking unit and improve economic benefits.

[0074] The flow chart of the catalytic cracking main fractionation tower system is as follows: Figure 4 As shown, according to the analysis of the process flow, the top cycle MV1, the flow rate of the first medium MV2 and the flow rate of the second medium MV3 are the operating variables; the tower top temperature CV1, the dry point of naphtha CV2 and the pour point of light oil CV3 are the controlled variables. Accord...

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Abstract

The invention discloses a modeling method for a catalytic cracking main fractionator with a varying-population-size DNA genetic algorithm, comprising the following steps: 1) that input and output data of the catalytic cracking main fractionator are collected as modeling data through on-site operation or experimental sampling; 2) that the modeling data are used to train a support vector machine, and a mean square deviation collected by cross validation is taken as an objective function; 3) that operational parameters of the DNA genetic algorithm are set; and 4) that the varying-population-size DNA genetic algorithm is operated to optimize the parameters of the support machine, wherein collected optimization parameters of the support vector machine are used for training the support vector machine and further collecting a nonparametric model of the catalytic cracking main fractionator. The method of the invention combines the varying-population-size DNA genetic algorithm with the support vector machine, and at the same time introduces a mutation operator enlightened by flora drug resistance into the nonparametric modeling of the catalytic cracking main fractionator, thereby effectively increasing modeling precision of the support vector machine.

Description

technical field [0001] The invention relates to a modeling method of a catalytic cracking main fractionation tower based on a variant group scale DNA genetic algorithm. Background technique [0002] Catalytic cracking process is the production process of converting heavy raw materials into light products. It is an important secondary processing process in the oil refining industry, and its efficiency directly affects the economic benefits of the oil refinery. The catalytic cracking fractionator used in this process is the key unit to realize product separation in the secondary processing. Establishing an accurate model of the catalytic cracking fractionator is of great significance for reducing the energy consumption of the catalytic cracking unit, improving product yield and economic benefit. [0003] The traditional mechanism modeling method needs to rely on prior knowledge to establish the differential equation model of the process. Since the catalytic cracking fractiona...

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Application Information

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IPC IPC(8): G06F19/00G06N3/12
Inventor 叶海山王宁
Owner ZHEJIANG UNIV
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