A novel operation cost optimization system for mix-burning coal slime circulating fluidized bed boiler unit

Wei Zhang*, Jizhen Liu, Ming-ming Gao, Jakob K. Huusom

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

At present, mix-burning of coal slime in a circulating fluidized bed boiler is an effective method to cleanly utilize low-price coal slime. This study proposed a data-based operation cost optimization system for mix-burning coal slime CFB boiler unit that instructs operators to more scientifically adjust the operation parameters. Based on actual operating data from a 300 MW CFB unit, least squares support vector machine was used to build the steady-state operation cost model, and partial mutual information variable selection method was applied to choose the input variables and lower the model complexity. Based on the pre-built operation cost model, the genetic algorithm was used to establish an offline expert knowledge database within the safety threshold range. The utility cost was introduced into association rule measurement standards to improve the traditional fuzzy association rules mining. The improved fuzzy association rule mining was used to extract the associations between the unit load and the optimal operation parameters from the off-line expert knowledge database after receiving the load instruction, so as to achieve fast instruct on online operation optimization. Results showed that the proposed economic optimization system performances were better than traditional methods and can improve operation of the unit being studied.
Original languageEnglish
JournalApplied Thermal Engineering
Volume148
Pages (from-to)620-631
ISSN1359-4311
DOIs
Publication statusPublished - 2019

Keywords

  • Circulating fluidized bed
  • Partial mutual information
  • Least squares support vector machine
  • Genetic algorithm
  • Improved fuzzy association rule mining

Cite this

@article{404ba4f85d794c01b7514d4446c0f5b9,
title = "A novel operation cost optimization system for mix-burning coal slime circulating fluidized bed boiler unit",
abstract = "At present, mix-burning of coal slime in a circulating fluidized bed boiler is an effective method to cleanly utilize low-price coal slime. This study proposed a data-based operation cost optimization system for mix-burning coal slime CFB boiler unit that instructs operators to more scientifically adjust the operation parameters. Based on actual operating data from a 300 MW CFB unit, least squares support vector machine was used to build the steady-state operation cost model, and partial mutual information variable selection method was applied to choose the input variables and lower the model complexity. Based on the pre-built operation cost model, the genetic algorithm was used to establish an offline expert knowledge database within the safety threshold range. The utility cost was introduced into association rule measurement standards to improve the traditional fuzzy association rules mining. The improved fuzzy association rule mining was used to extract the associations between the unit load and the optimal operation parameters from the off-line expert knowledge database after receiving the load instruction, so as to achieve fast instruct on online operation optimization. Results showed that the proposed economic optimization system performances were better than traditional methods and can improve operation of the unit being studied.",
keywords = "Circulating fluidized bed, Partial mutual information, Least squares support vector machine, Genetic algorithm, Improved fuzzy association rule mining",
author = "Wei Zhang and Jizhen Liu and Ming-ming Gao and Huusom, {Jakob K.}",
year = "2019",
doi = "10.1016/j.applthermaleng.2018.11.087",
language = "English",
volume = "148",
pages = "620--631",
journal = "Applied Thermal Engineering",
issn = "1359-4311",
publisher = "Pergamon Press",

}

A novel operation cost optimization system for mix-burning coal slime circulating fluidized bed boiler unit. / Zhang, Wei; Liu, Jizhen; Gao, Ming-ming; Huusom, Jakob K.

In: Applied Thermal Engineering, Vol. 148, 2019, p. 620-631.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - A novel operation cost optimization system for mix-burning coal slime circulating fluidized bed boiler unit

AU - Zhang, Wei

AU - Liu, Jizhen

AU - Gao, Ming-ming

AU - Huusom, Jakob K.

PY - 2019

Y1 - 2019

N2 - At present, mix-burning of coal slime in a circulating fluidized bed boiler is an effective method to cleanly utilize low-price coal slime. This study proposed a data-based operation cost optimization system for mix-burning coal slime CFB boiler unit that instructs operators to more scientifically adjust the operation parameters. Based on actual operating data from a 300 MW CFB unit, least squares support vector machine was used to build the steady-state operation cost model, and partial mutual information variable selection method was applied to choose the input variables and lower the model complexity. Based on the pre-built operation cost model, the genetic algorithm was used to establish an offline expert knowledge database within the safety threshold range. The utility cost was introduced into association rule measurement standards to improve the traditional fuzzy association rules mining. The improved fuzzy association rule mining was used to extract the associations between the unit load and the optimal operation parameters from the off-line expert knowledge database after receiving the load instruction, so as to achieve fast instruct on online operation optimization. Results showed that the proposed economic optimization system performances were better than traditional methods and can improve operation of the unit being studied.

AB - At present, mix-burning of coal slime in a circulating fluidized bed boiler is an effective method to cleanly utilize low-price coal slime. This study proposed a data-based operation cost optimization system for mix-burning coal slime CFB boiler unit that instructs operators to more scientifically adjust the operation parameters. Based on actual operating data from a 300 MW CFB unit, least squares support vector machine was used to build the steady-state operation cost model, and partial mutual information variable selection method was applied to choose the input variables and lower the model complexity. Based on the pre-built operation cost model, the genetic algorithm was used to establish an offline expert knowledge database within the safety threshold range. The utility cost was introduced into association rule measurement standards to improve the traditional fuzzy association rules mining. The improved fuzzy association rule mining was used to extract the associations between the unit load and the optimal operation parameters from the off-line expert knowledge database after receiving the load instruction, so as to achieve fast instruct on online operation optimization. Results showed that the proposed economic optimization system performances were better than traditional methods and can improve operation of the unit being studied.

KW - Circulating fluidized bed

KW - Partial mutual information

KW - Least squares support vector machine

KW - Genetic algorithm

KW - Improved fuzzy association rule mining

U2 - 10.1016/j.applthermaleng.2018.11.087

DO - 10.1016/j.applthermaleng.2018.11.087

M3 - Journal article

VL - 148

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EP - 631

JO - Applied Thermal Engineering

JF - Applied Thermal Engineering

SN - 1359-4311

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