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International Conference on Database Systems for Advanced Applications

DASFAA 2020: Database Systems for Advanced Applications. DASFAA 2020 International Workshops pp 153–163Cite as

Kernel Design of Intelligent Historical Database for Multi-objective Combustion Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12115))

Abstract

In order to make the methods and strategies of artificial intelligence with data-driven and massive historical operation data combine directly with each other, and then meet the intelligent demand of multi-objective combustion optimization for coal-fired power stations, an intelligent historical database (IHDB), which is a combination of artificial intelligence and historical database, is proposed. The structures of file, index and module, which compose the kernel of IHDB, are presented as well. The kernel design doesn’t only ensure processing data efficiently, but also incorporates data analysis, optimization decisions and other intelligent elements, so that a strong platform can be developed for solving the problem of multi-objective combustion optimization. The performance tests about storing and querying data of IHDB have obtained satisfactory results. Developing IHDB is beneficial to improve the intelligent level of coal-fired power plants and is helpful to promote the development of real-time database system involving IHDB.

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Author information

Authors and Affiliations

  1. Tianjin Key Laboratory of Process Measurement and Control, School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China

    Wei Zheng & Chao Wang

  2. School of Mechatronical Engineering and Automation, Tianjin Vocational Institute, Tianjin, 300410, China

    Wei Zheng

Authors
  1. Wei Zheng
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  2. Chao Wang
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Corresponding author

Correspondence to Chao Wang .

Editor information

Editors and Affiliations

  1. Dankook University, Yongin, Korea (Republic of)

    Yunmook Nah

  2. Seoul, Korea (Republic of)

    Chulyun Kim

  3. University of Southern California, Daejeon, Korea (Republic of)

    Seon-Young Kim

  4. Kangwon National University, Chunchon, Korea (Republic of)

    Yang-Sae Moon

  5. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Steven Euijong Whang

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Zheng, W., Wang, C. (2020). Kernel Design of Intelligent Historical Database for Multi-objective Combustion Optimization. In: Nah, Y., Kim, C., Kim, SY., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020 International Workshops. DASFAA 2020. Lecture Notes in Computer Science(), vol 12115. Springer, Cham. https://doi.org/10.1007/978-3-030-59413-8_13

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  • DOI: https://doi.org/10.1007/978-3-030-59413-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59412-1

  • Online ISBN: 978-3-030-59413-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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