Study on missing data imputation and modeling for the leaching process
| Autor: | Zhengsong Wang, Wanwan Dai, Dakuo He, Le Yang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Engineering
Data collection business.industry General Chemical Engineering 02 engineering and technology General Chemistry Data loss computer.software_genre Mixture model 01 natural sciences 010104 statistics & probability Harshness Missing data imputation Expectation–maximization algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Process optimization Imputation (statistics) Data mining 0101 mathematics business computer |
| Zdroj: | Chemical Engineering Research and Design. 124:1-19 |
| ISSN: | 0263-8762 |
| DOI: | 10.1016/j.cherd.2017.05.023 |
| Popis: | The leaching process is an important component in hydrometallurgy. A predictive model of the leaching rate lays the foundation for soft measurement and process optimization, and data collection is the key in such a modeling effort. However, because of the complexity and harshness of leaching process, data can only be collected sparsely, which results in data deficiency in the modeling process. Therefore, data imputation before modeling seems to be extremely significant. In this paper, expectation maximization imputation based on the Gaussian mixture model (GMM-EM) and multiple imputation (MI) are respectively applied to perform missing data imputation for leaching process under different data loss rates and data loss patterns, and then the imputation performances are evaluated. Simulation experiment results have shown that GMM-EM and MI both have advantages with regard to data imputation. Therefore, MI based on GMM (GMM-MI), which combines the advantages of GMM and MI, is proposed in this paper. The effectiveness of GMM-MI is verified by a series of simulations. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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