Name
Title | ||
|---|---|---|
A Hybrid First Principles and Data-Driven Process Monitoring Method for Zinc Smelting Roasting Process |
Abstract | ||
|---|---|---|
Roasting is the first procedure in the zinc smelting process. The stable and safe operation of the roasting process is significant to guarantee the quality of output zinc and reduce industrial pollution and energy consumption. In order to realize safe and stable operation for the roasting process, it is particularly important to monitor the roasting process accurately. However, the first principles model of the roasting process is complex with coupled physical and chemical reactions, and the normal operating conditions may transfer to abnormal conditions such as over-decomposition, under-oxidation, and fluidized bed deposition, due to fluctuation of raw material composition. On the other hand, the data-driven process monitoring method will suffer from unbalanced data volume between different operating conditions, especially for the abnormal conditions, of which data are always insufficient. In order to address these problems and achieve accurate process monitoring for the zinc smelting roasting process, this article proposed a hybrid first principles and data-driven process monitoring method. In detail, an integrated principal component analysis and common subspace learning (PCA-CSL) method is first proposed to address the problem that different operating condition always has large divergence and imbalanced data volume. Here, the PCA algorithm is established for the operating conditions with sufficient data. On the contrary, for the operating condition with insufficient data, a CSL algorithm is proposed, which uses the operating condition with sufficient data to assist modeling for the operating condition with insufficient data in a common subspace, so as to realize accurate fault detection. Finally, an operating condition decision rule library is established based on integrated first principles and data-driven approach, and the parameters of decision rule were optimized according to particle swarm optimization (PSO) method to realize rule-based reasoning (RBR) based abnormal condition diagnosis. Extensive experiments are implemented to verify the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TIM.2021.3126390 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT |
Keywords | DocType | Volume |
Process monitoring, Zinc, Smelting, Furnaces, Production, Raw materials, II-VI semiconductor materials, Hybrid approach, process monitoring, roasting process, rule-based reasoning, subspace learning | Journal | 70 |
ISSN | Citations | PageRank |
0018-9456 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huiping Liang | 1 | 0 | 0.34 |
| Chunhua Yang | 2 | 435 | 71.63 |
| Keke Huang | 3 | 41 | 10.22 |
| Yonggang Li | 4 | 0 | 5.07 |
| Weihua Gui | 5 | 577 | 90.82 |