公开(公告)号：US20220318714A1

公开(公告)日：2022-10-06

申请号：US17297939

申请日：2020-07-28

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Jun ZHAO ,  Feng JIN ,  Guanghui YANG ,  Wei WANG

IPC: G06Q10/06 ,  G06Q50/04

Abstract: The present disclosure discloses a method for predicting oxygen load in iron and steel enterprises based on production plan, which relates to influencing factor extraction, neural network modeling and similar sequence matching technologies. The method uses the actual industrial operation data to first extract the relevant data such as the production plan and production performance of converter steel-making, analyze the influencing factors, and extract the main influencing variables of oxygen consumption. Then, the neural network prediction model of oxygen consumption of a single converter is established, the mean square error is taken as the evaluation index, and the predicting result of time granularity of a converter in the blowing stage is given. Finally, in combination with the information of smelting time and smelting duration of each device in the converter production plan, the prediction value of oxygen load in a planned time period is given.

公开(公告)号：US20220382263A1

公开(公告)日：2022-12-01

申请号：US17569671

申请日：2022-01-06

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Jun ZHAO ,  Feng JIN ,  Long CHEN ,  Fan ZHOU ,  Zhongyang HAN ,  Yang LIU ,  Wei WANG

IPC: G05B19/418

Abstract: A distributed industrial energy operation optimization platform which is capable of automatically constructing intelligent models and algorithms, is divided into three parts: a modeling terminal, a background service and a human-computer interface. The models like data pre-processing, energy generation-consumption-storage trend forecasting and optimal scheduling decision models are encapsulated in the modeling terminal as different visualization modules facing with multiple categories production scenarios, by dragging which the complex functional models can be realized conveniently. The background service is capable of automatically constructing the training samples and the production plans/manufacturing signals series according to the device model requirements of each edge side, interacts with the trained intelligent models through corresponding interfaces, and the computing results are saved in the specified relational database. The computing results are displayed through a friendly customer human-computer interface, and the real-time state of current working condition can also be adjusted.