Acoustic signal processing with robust machine learning algorithm for improved monitoring of particulate solid materials in a gas flowline
- Submitting institution
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Glasgow Caledonian University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 33060458
- Type
- D - Journal article
- DOI
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10.1016/j.flowmeasinst.2018.11.015
- Title of journal
- Flow Measurement and Instrumentation
- Article number
- -
- First page
- 33
- Volume
- 65
- Issue
- -
- ISSN
- 0955-5986
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work, first presented at the oil and gas Sand Management Network in Aberdeen, has led to several enquiries from production and service companies. This paper presents a novel application of machine learning to the challenging problem of sand monitoring in flowlines in the oil and gas sector which has the potential to significantly enhance production and influence safety and maintenance strategies. The same technique was also presented to academics and industrialists from the process industries at the CHoPS conference. A funding application with a major O&G service company TechnipFMC was planned. Work is continuing with a new PhD project.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
