公开(公告)号：US11526789B2

公开(公告)日：2022-12-13

申请号：US16500052

申请日：2018-09-12

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Zhongyang Han ,  Jun Zhao ,  Wei Wang ,  Linqing Wang

IPC: G06Q10/06 ,  G06Q10/10 ,  G06N5/04 ,  G06K9/62 ,  G06N7/00 ,  G06Q10/04

Abstract: The present invention belongs to the field of information technology, involving the techniques of fuzzy modeling, reinforcement learning, parallel computing, etc. It is a method combining granular computing and reinforcement learning for construction of long-term prediction interval and determination of its structure. Adopting real industrial data, the present invention constructs multi-layer structure for assigning information granularity in unequal length and establishes corresponding optimization model at first. Then considering the importance of the structure on prediction accuracy, Monte-Carlo method is deployed to learn the structural parameters. Based on the optimal multi-layer granular computing structure along with implementing parallel computing strategy, the long-term prediction intervals of gaseous generation and consumption are finally obtained. The proposed method exhibits superiority on accuracy and computing efficiency which satisfies the demand of real-world application. It can be also generalized to apply on other energy systems in steel industry.

公开(公告)号：US11126765B2

公开(公告)日：2021-09-21

申请号：US16928672

申请日：2020-07-14

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Jun Zhao ,  Yang Liu ,  Fan Zhou ,  Zhongyang Han ,  Linqing Wang ,  Wei Wang

IPC: G06F30/20 ,  G06N7/08 ,  G06F111/04 ,  G06F119/06 ,  G06F111/10

Abstract: The present invention provides a method for an optimal scheduling decision of an air compressor group based on a simulation technology, which belongs to the technical field of information. The present invention uses expert experience to construct an air compressor energy consumption model sample set, and applies a least squares algorithm to learn relevant parameters of an air compressor energy consumption model; uses maximum energy conversion efficiency and minimum economic cost based on an equivalent electricity as target functions, and applies the simulation technology and a depth first tree search algorithm to solve a multi-target optimal scheduling model of the air compressor group; and finally uses a fuzzy logic theory to describe the preferences of decision makers, and introduces the decision maker preference information into interactive decision making, thereby assisting production staff to formulate safe, economical, efficient and environmentally friendly operation schemes to achieve an operation mode of maximum resource utilization of the air compressor group. The method also has wide application value in different industrial fields.

公开(公告)号：US11487273B1

公开(公告)日：2022-11-01

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.

公开(公告)号：US11755976B2

公开(公告)日：2023-09-12

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 ,  G06Q10/0637 ,  G06Q10/0631 ,  G06Q50/04

CPC classification number: G06Q10/06375 ,  G06Q10/06313 ,  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：US11070056B1

公开(公告)日：2021-07-20

申请号：US17197831

申请日：2021-03-10

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Feng Jin ,  Jun Zhao ,  Xingxing Gao ,  Linqing Wang ,  Wei Wang

IPC: H02J3/00 ,  G06F17/18 ,  G06F17/16

Abstract: The present disclosure belongs to the technical field of information, provides a short-term interval prediction method for photovoltaic power output, and is a short-term interval prediction method for photovoltaic power output based on a combination of a multi-objective optimization algorithm and a least square support vector machine. The present disclosure firstly proposes a similar day classification method considering both numerical value and pattern similarity to enhance the regularity of samples, then constructs an adaptive proportional interval estimation model based on dual-LSSVM model, and optimizes model parameters by using NSGA-II algorithms to realize the interval prediction of photovoltaic power output. Results obtained by the method have high accuracy, and computation efficiency meets actual application requirements. The method can also be popularized and applied in the fields of grid connection and scheduling of renewable energy sources.