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

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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However, the neural network itself has some defects, such as the network has problems such as over-learning, local minima, and slow convergence.

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