Comparison of Missing Data Imputation Methods for Leaching Process Modelling

DOI

10.2991/aiie-15.2015.134How to use a DOI?

Keywords

missing data imputation; leaching process; modelling; multiple imputation (MI); gaussian mixture model (GMM)

Abstract

As the original information of production process, industrial production data is the important basis and foundation of process modelling and optimization. However, the data acquisition operation, the restriction of instrument operation environment and malfunction often lead to data missing. Under this condition, the research on missing data imputation in the leaching process is vital and significant. In this paper, the leaching process mechanism model is presented firstly. Missing data characteristics, the basic principle of imputation methods are introduced in detail next. Based on the analysis of data deficiency and its features during the acid intermittent leaching of cobalt compound ore, this article will launch the research on the deficiency of crucial values, such as sulphur dioxide flow, PH value of leaching agent, leaching rate, and apply various data packing methods into leaching process modelling. According to the simulation results, this paper evaluates the application performance of different imputation and modelling methods in accuracy and concludes the method with which could pack the missing data effectively under different data missing condition.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - D.K. He
AU  - T.S. Chu
AU  - Y.B. Lang
AU  - G.X. Sun
PY  - 2015/07
DA  - 2015/07
TI  - Comparison of Missing Data Imputation Methods for Leaching Process Modelling
BT  - Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering
PB  - Atlantis Press
SP  - 497
EP  - 500
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-15.2015.134
DO  - 10.2991/aiie-15.2015.134
ID  - He2015/07
ER  -