Data mining based feedback regulation in operation of hematite ore mineral processing plant

Jinliang Ding; Qi Chen; Tianyou Chai; Hong Wang; Chun Yi Su

doi:10.1109/ACC.2009.5160724

Data mining based feedback regulation in operation of hematite ore mineral processing plant

Jinliang Ding, Qi Chen, Tianyou Chai, Hong Wang, Chun Yi Su

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

To deal with the variation of production operation of the mineral processing plant, the data-mining based feedback regulation strategy is proposed to compensate the open loop steady state setting of the production unit at the plant-wide level. Rough set and increment association rule learning are used for the feedback regulation rule extraction from the historical operation data. To realize the feedback compensator two steps are carried out: (1) Determining the variables to be compensated based on rough set, (2) Mining the compensation rules through the increment association rule learning and rough set. The efficiency of the proposed strategy is proven by the experiments. © 2009 AACC.

Original language	English
Title of host publication	Proceedings of the American Control Conference\|Proc Am Control Conf
Pages	907-912
Number of pages	5
DOIs	https://doi.org/10.1109/ACC.2009.5160724
Publication status	Published - 2009
Event	2009 American Control Conference, ACC 2009 - St. Louis, MO Duration: 1 Jul 2009 → …

Conference

Conference	2009 American Control Conference, ACC 2009
City	St. Louis, MO
Period	1/07/09 → …

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10.1109/ACC.2009.5160724

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title = "Data mining based feedback regulation in operation of hematite ore mineral processing plant",

abstract = "To deal with the variation of production operation of the mineral processing plant, the data-mining based feedback regulation strategy is proposed to compensate the open loop steady state setting of the production unit at the plant-wide level. Rough set and increment association rule learning are used for the feedback regulation rule extraction from the historical operation data. To realize the feedback compensator two steps are carried out: (1) Determining the variables to be compensated based on rough set, (2) Mining the compensation rules through the increment association rule learning and rough set. The efficiency of the proposed strategy is proven by the experiments. {\textcopyright} 2009 AACC.",

author = "Jinliang Ding and Qi Chen and Tianyou Chai and Hong Wang and Su, {Chun Yi}",

year = "2009",

doi = "10.1109/ACC.2009.5160724",

language = "English",

isbn = "9781424445240",

pages = "907--912",

booktitle = "Proceedings of the American Control Conference|Proc Am Control Conf",

note = "2009 American Control Conference, ACC 2009 ; Conference date: 01-07-2009",

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T1 - Data mining based feedback regulation in operation of hematite ore mineral processing plant

AU - Ding, Jinliang

AU - Chen, Qi

AU - Chai, Tianyou

AU - Wang, Hong

AU - Su, Chun Yi

PY - 2009

Y1 - 2009

N2 - To deal with the variation of production operation of the mineral processing plant, the data-mining based feedback regulation strategy is proposed to compensate the open loop steady state setting of the production unit at the plant-wide level. Rough set and increment association rule learning are used for the feedback regulation rule extraction from the historical operation data. To realize the feedback compensator two steps are carried out: (1) Determining the variables to be compensated based on rough set, (2) Mining the compensation rules through the increment association rule learning and rough set. The efficiency of the proposed strategy is proven by the experiments. © 2009 AACC.

AB - To deal with the variation of production operation of the mineral processing plant, the data-mining based feedback regulation strategy is proposed to compensate the open loop steady state setting of the production unit at the plant-wide level. Rough set and increment association rule learning are used for the feedback regulation rule extraction from the historical operation data. To realize the feedback compensator two steps are carried out: (1) Determining the variables to be compensated based on rough set, (2) Mining the compensation rules through the increment association rule learning and rough set. The efficiency of the proposed strategy is proven by the experiments. © 2009 AACC.

U2 - 10.1109/ACC.2009.5160724

DO - 10.1109/ACC.2009.5160724

M3 - Conference contribution

SN - 9781424445240

SP - 907

EP - 912

BT - Proceedings of the American Control Conference|Proc Am Control Conf

T2 - 2009 American Control Conference, ACC 2009

Y2 - 1 July 2009

ER -

Data mining based feedback regulation in operation of hematite ore mineral processing plant

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