Paper Info
Title
A Data-Driven Compensation Method for Production Index of Hydrometallurgical Process
Abstract
The process of hydrometallurgical has the characteristics of many sub-processes with complicated reaction mechanism and long process flow. How to keep the hydrometallurgical process running in the state of optimal economic efficiency is the difficulty task. In this paper, a method based on industrial big data is proposed to compensate the production index of the hydrometallurgical process. Based on the current production index, the just-in-time learning (JITL) idea is used to establish the model that describes the relationship between the compensation value and the economic benefit increment. Then, the compensation value of the current production index is calculated, and the result is applied to the production process. The simulation and offline experiment results verify the effectiveness of the proposed method.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2911357
IEEE ACCESS
Keywords
Field
DocType
Hydrometallurgical,industrial big data,optimal compensation,JITL
Economic efficiency,Process engineering,Data-driven,Industrial production index,Computer science,Flow (psychology),Scheduling (production processes),Big data,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Kang Li160779.66
Fuli Wang25212.61
Da-kuo He394.02
Luping Zhao400.34