Method for predicting yield of calcium in a calcium treatment process based on deep neural network

Patent metadata

Field	Value
Publication number	US-11545239-B1
Application number	US-202217844364-A
Country	US
Kind code	B1
Filing date	Jun 20, 2022
Priority date	Jun 22, 2021
Publication date	Jan 3, 2023
Grant date	Jan 3, 2023

Abstract

A method for predicting a yield of calcium in a calcium treatment process based on deep neural network as provided relates to a calcium treatment process of molten steel refining in the field of iron and steel metallurgy, and includes steps of: obtaining production and operation data information of each of charges and thereby constructing a dataset; training and testing a deep neural network based on constructed dataset to establish a prediction model; and based on the prediction model, predicting and calculating current yield of calcium by taking actual production and operation data information of each charge as input. The method can predict the yield of calcium in the calcium treatment process, is favorable for accurately controlling a calcium content of steel, can stably control the calcium treatment process, improve the calcium treatment effect, improve the product quality, and ensure the production stability.

First claim

What is claimed is:

1. A method for predicting a yield of calcium in a calcium treatment process based on deep neural network, comprising:
S 1 , obtaining parameters affecting the yield of calcium in the calcium treatment process from production and operation data information, and thereby constructing a dataset; wherein the S 1 comprises:
S 11 , collecting production and operation data information in a refining process of each production charge and calculating the yield of calcium of each the production charge, as a piece of record;
wherein the yield of calcium of each the production charge comprises a yield of calcium η 1 in the refining process, a yield of calcium η 2 of tundish, and a yield of calcium η 3 of continuous casting billet;
a calculation formula for the yield of calcium in the refining process is that:

η
1

=

W
⁡
(

ω
[
Ca
]

R

-

ω
[
Ca
]

o

)

χβμ

×

10
3

,

a calculation formula for the yield of calcium of tundish is that:

η
2

=

W
⁡
(

ω
[
Ca
]

T

-

ω
[
Ca
]

o

)

χβμ

×

10
3

,

a calculation formula for the yield of calcium of continuous casting billet is that:

η
3

=

W
⁡
(

ω
[
Ca
]

B

-

ω
[
Ca
]

o

)

χβμ

×

10
3

,

where, W represents a molten steel weight, with a unit of ton (t); ω[Ca] o represents a calcium content of molten steel before calcium treatment, with a unit of parts per million (ppm); χ represents a calcium wire feeding length, with a unit of meter (m); β represents a calcium content of calcium wire, with a unit of %; μ represents a meter weight of calcium wire, with a unit of kilogram per meter (kg/m); ω[Ca] R represents a calcium content of molten steel after the refining process, with a unit of ppm; ω[Ca] T represents a calcium content of molten steel in a tundish during continuous casting, with a unit of ppm; and ω[Ca] B represents a calcium content in a continuous casting billet, with a unit of ppm;
S 12 , repeatedly collecting the production and operation data information in the refining process of each production charge, and thereby establishing a dataset; and
S 13 , preprocessing the dataset established in the S 12 to remove incomplete data and unreasonable data;

S 2 , performing normalization processing on the dataset constructed in the S 1 to obtain a normalized dataset;
S 3 , building a deep neural network, comprising: dividing the normalized dataset obtained in the S 2 into a training dataset and a testing dataset, using production and operation data information as input of the deep neural network and using an actual yield of calcium as output of the deep neural network, comparing a calculation result with an actual result, modifying weights and thresholds, and using data in the training dataset to train the deep neural network, thereby obtaining a trained deep neural network;
S 4 , using the testing dataset to test the trained deep neural network obtained in the S 3 , using input data in the testing dataset as input data of the trained deep neural network to obtain calculation results of yield of calcium, and taking an error generated by actual results of yield of calcium and the calculation results of yield of calcium of the trained deep neural network reaches a minimum threshold as an optimization objective to optimize the trained deep neural network;
S 5 , determining, based on the error in the S 4 , the trained deep neural network whether meets a requirement or not; in response to the error reaches the minimum threshold, taking current trained deep neural network as a resultant prediction model of yield of calcium; in response to the error does not reach the minimum threshold, modifying a hidden layer quantity, a node quantity, and a learning rate of the trained deep neural network, and repeating the S 4 until the error reaches the minimum threshold which indicates a yield of calcium calculated by the trained deep neural network meets a predetermined requirement; and
S 6 , predicting a yield of calcium in an actual calcium treatment process based on the resultant prediction model of yield of calcium obtained in the S 5 .

2. The method for predicting a yield of calcium in a calcium treatment process based on deep neural network as claimed in claim 1 , wherein the parameters affecting the yield of calcium in the calcium treatment process in the S 11 comprise: a carbon (C) content of molten s

Assignees

• Univ North China Technology
• Univ Yanshan
• Univ Beijing Science & Technology

Inventors

• Zhang Lifeng
• Wang Weijian
• Ren Qiang
• Ren Ying
• Luo Yan

Classifications

• G16C20/10 Primary

  Analysis or design of chemical reactions, syntheses or processes · CPC title

• G06F30/27 Primary

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title

• G16C20/70

  Machine learning, data mining or chemometrics · CPC title

• C21C7/0056

  using cored wires · CPC title

• G06F2111/06

  Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA] · CPC title