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1.发明申请公开(公告)号：US20200285982A1
公开(公告)日：2020-09-10
申请号：US16500052
申请日：2018-09-12
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Zhongyang HAN , Jun ZHAO , Wei WANG , Linqing WANG
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.
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2.发明申请公开(公告)号：US20200342150A1
公开(公告)日：2020-10-29
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
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.
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