Abstract
The degradation in the performance of the plant is observed in form of oscillations in time trends of measurements. These disturbances propagate throughout the plant and also affect the performance of healthy loops. Thus, it becomes increasingly important to detect all the loops that lead to plant-wide oscillations. In this paper, spectral decomposition based on Evolutionary Algorithms is proposed for the detection of plant-wide oscillations. The key feature of the proposed technique is that it retains causal basis spectrum like shapes consisting of narrow band peaks by searching the solution space globally. Two industrial case studies are presented to demonstrate the efficiency of GA based Evolutionary Algorithms over existing techniques like independent component analysis (ICA) and non-negative matrix factorization (NMF) in detecting plant-wide oscillations. Results show that the proposed technique outperforms ICA and NMF with respect to reconstruction error.
| Original language | English |
|---|---|
| Pages (from-to) | 321-329 |
| Number of pages | 9 |
| Journal | Journal of Process Control |
| Volume | 22 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2012 |
Bibliographical note
Funding Information:The authors would like to acknowledge the support of King Fahd University of Petroleum and Minerals for supporting this research work. The authors are thankful to Arun Tangirala for providing the data of the pulp quality blending process used in case 1 of our study. We are also grateful to anonymous referees for constructive comments that lead to noticeable improvements in the paper.
Keywords
- Control loop performance
- Evolutionary Algorithms
- Oscillation detection
- Plant-wide disturbance
- Spectral decomposition
ASJC Scopus subject areas
- Control and Systems Engineering
- Modeling and Simulation
- Computer Science Applications
- Industrial and Manufacturing Engineering