Ethylene cracking severity modeling method based on expert knowledge and neutral network

A neural network model and ethylene cracking technology, applied in the interdisciplinary field of chemical engineering and information science, can solve problems such as difficult cracking furnace work, short residence time, high cost, etc.

Inactive Publication Date: 2010-11-10
EAST CHINA UNIV OF SCI & TECH
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Problems solved by technology

For the measurement of cracking depth, although analytical instruments are installed in general industrial sites, they all have a lag of 20-30 minutes, and the maintenance is complicated and costly
At the same time, the long lag is relatively short for the cracking raw material. If the process is adjusted according to the results of the analytical instrument, it is difficult to ensure that the cracking furnace works under the best operating conditions.

Method used

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  • Ethylene cracking severity modeling method based on expert knowledge and neutral network
  • Ethylene cracking severity modeling method based on expert knowledge and neutral network
  • Ethylene cracking severity modeling method based on expert knowledge and neutral network

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Embodiment

[0030] use as figure 1 In the shown neural network structure, the variables affecting the depth of ethylene cracking are determined first: dilution steam flow rate (x 1 , kg / h), cracking furnace feed load (x 2 , kg / h), raw oil density (x 3 ,kg / m 3 ), the average temperature at the furnace tube outlet (x 4 , ℃), the average temperature of waste heat boiler outlet (x 5 , ℃), the average temperature of the radiation section (x 6 , ℃). In this example, the key input variable dilution steam flow rate x 1 , Feed load of cracking furnace x 2 and the average temperature at the outlet of the furnace tube x 4 Do a sensitivity analysis. Therefore, according to the above variables, the number of input layer nodes of the neural network is determined to be 6, the number of hidden layer nodes is set to 8, and the number of output layer nodes is 1. The hidden layer activation function uses the tansig function, and the output layer activation function uses the logsig function.

[0...

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Abstract

The invention relates to a modeling method for soft sensing the cracking severity of an ethylene cracking furnace based on expert knowledge and a neutral network. A model established by using the method has high prediction precision, high reliability and high extrapolation performance. The model can reflect the actual operation characteristics of the cracking furnace accurately in real time so as to instruct an operator to adjust the operation variables of the cracking process in time, and thus, the economical benefits are increased. In the invention, the expert knowledge about the ethylene cracking process is added to the network training process of ethylene cracking severity neutral network modeling, and a training sample set is formed by acquiring and preprocessing onsite production data. Meanwhile, the neutral network is optimized by using an intelligent evolutionary algorithm, and the neutral network soft sensing model of ethylene cracking severity is established.

Description

technical field [0001] The invention belongs to the cross field of chemical engineering and information science, and relates to a soft sensor modeling method for ethylene cracking depth based on expert knowledge and neural network. Background technique [0002] Ethylene is an important basic raw material in the chemical industry, and its output has become the main symbol to measure the petrochemical development of a country or region. With the continuous capacity expansion of ethylene plants and the deepening of the integration of refining and chemical engineering, the shortage of ethylene raw material resources and the diversification of raw materials have become increasingly prominent. While the cracking unit is large-scaled and the raw materials are diversified, the raw material flexibility of the cracking unit should be improved. It is particularly important to improve ethylene selectivity and yield, and reduce energy and material consumption. [0003] The cracking furn...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/08C07C11/04C10G9/14
Inventor 李绍军李飞刘漫丹
Owner EAST CHINA UNIV OF SCI & TECH
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