Journal
WATER SCIENCE AND TECHNOLOGY
Volume 81, Issue 8, Pages 1740-1748Publisher
IWA PUBLISHING
DOI: 10.2166/wst.2020.026
Keywords
anaerobic digestion; BSM2; data-driven; fault detection; support vector machine
Funding
- Ministerio de Economia, Industria y Competitividad
- Ministerio de Ciencia, Innovacion y Universidades
- Agencia Estatal de Investigacion (AEI)
- European Regional Development Fund (ERDF) [CTM2015-67970-P, RTI2018-096467-B-I00]
- Universitat Rovira i Virgili (URV) [2017PFR-URV-B2-33, 2019OPEN]
- Fundacio Bancaria 'la Caixa' [2017ARES06]
- Comissionat per a Universitats i Recerca, DIUE de la Generalitat de Catalunya [2017 SGR 396]
Ask authors/readers for more resources
Early detection of small-magnitude faults in anaerobic digestion (AD) processes is a mandatory step for preventing serious consequence in the future. Since volatile fatty acids (VFA) accumulation is widely suggested as a process health indicator, a VFA soft-sensor was developed based on support vector machine (SVM) and used for generating the residuals by comparing real and predicted VFA. The estimated residual signal was applied to univariate statistical control charts such as cumulative sum (CUSUM) and square prediction error (SPE) to detect the faults. A principal component analysis (PCA) model was also developed for comparison with the aforementioned approach. The proposed framework showed excellent performance for detecting small-magnitude faults in the state parameters of AD processes.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available