Online detection of shutdown periods in chemical plants: A case study
Authors: Salvador, M.M., Gabrys, B. and Žliobaite, I.
Journal: Procedia Computer Science
Volume: 35
Issue: C
Pages: 580-588
eISSN: 1877-0509
DOI: 10.1016/j.procs.2014.08.139
Abstract:In process industry, chemical processes are controlled and monitored by using readings from multiple physical sensors across the plants. Such physical sensors are also supplemented by soft sensors, i.e. adaptive predictive models, which are often used for computing hard-to-measure variables of the process. For soft sensors to work well and adapt to changing operating conditions they need to be provided with relevant data. As production plants are regularly stopped, data instances generated during shutdown periods have to be identified to avoid updating these predictive models with wrong data. We present a case study concerned with a large chemical plant operation over a 2 years period. The task is to robustly and accurately identify the shutdown periods even in case of multiple sensor failures. State-of-the-art methods were evaluated using the first half of the dataset for calibration purposes and the other half for measuring the performance. Results show that shutdowns (i.e. sudden changes) can be quickly detected in any case but the detection delay of startups (i.e. gradual changes) is directly related with the choice of a window size.
https://eprints.bournemouth.ac.uk/23389/
Source: Scopus
Online detection of shutdown periods in chemical plants: A case study
Authors: Salvador, M.M., Gabrys, B. and Zliobaite, I.
Journal: KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 18TH ANNUAL CONFERENCE, KES-2014
Volume: 35
Pages: 580-588
ISSN: 1877-0509
DOI: 10.1016/j.procs.2014.08.139
https://eprints.bournemouth.ac.uk/23389/
Source: Web of Science (Lite)
Online Detection of Shutdown Periods in Chemical Plants: A Case Study
Authors: Salvador, M.M., Gabrys, B. and Žliobaitė, I.
Journal: Procedia Computer Science
Volume: 35
Pages: 580 - 588
ISSN: 1877-0509
DOI: 10.1016/j.procs.2014.08.139
Abstract:Abstract In process industry, chemical processes are controlled and monitored by using readings from multiple physical sensors across the plants. Such physical sensors are also supplemented by soft sensors, i.e. adaptive predictive models, which are often used for computing hard-to-measure variables of the process. For soft sensors to work well and adapt to changing operating conditions they need to be provided with relevant data. As production plants are regularly stopped, data instances generated during shutdown periods have to be identified to avoid updating these predictive models with wrong data. We present a case study concerned with a large chemical plant operation over a 2 years period. The task is to robustly and accurately identify the shutdown periods even in case of multiple sensor failures. State-of-the-art methods were evaluated using the first half of the dataset for calibration purposes and the other half for measuring the performance. Results show that shutdowns (i.e. sudden changes) can be quickly detected in any case but the detection delay of startups (i.e. gradual changes) is directly related with the choice of a window size.
https://eprints.bournemouth.ac.uk/23389/
http://www.sciencedirect.com/science/article/pii/S1877050914011041
Source: Manual
Preferred by: Manuel Salvador
Online Detection of Shutdown Periods in Chemical Plants: A Case Study.
Authors: Salvador, M.M., Gabrys, B. and Zliobaite, I.
Conference: Knowledge-Based and Intelligent Information & Engineering Systems 18th Annual Conference (KES-2014)
Pages: 580-588
ISSN: 1877-0509
Abstract:In process industry, chemical processes are controlled and monitored by using readings from multiple physical sensors across the plants. Such physical sensors are also supplemented by soft sensors, i.e. adaptive predictive models, which are often used for computing hard-to-measure variables of the process. For soft sensors to work well and adapt to changing operating conditions they need to be provided with relevant data. As production plants are regularly stopped, data instances generated during shutdown periods have to be identified to avoid updating these predictive models with wrong data. We present a case study concerned with a large chemical plant operation over a 2 years period. The task is to robustly and accurately identify the shutdown periods even in case of multiple sensor failures. State-of-the-art methods were evaluated using the first half of the dataset for calibration purposes and the other half for measuring the performance. Results show that shutdowns (i.e. sudden changes) can be quickly detected in any case but the detection delay of startups (i.e. gradual changes) is directly related with the choice of a window size.
https://eprints.bournemouth.ac.uk/23389/
http://www.sciencedirect.com/science/article/pii/S1877050914011041
Source: BURO EPrints