Development of industrial process characterisation through data analysis

Aleksandar Josifovic; Jonathan Corney

doi:10.1109/SSCI.2016.7849990

Development of industrial process characterisation through data analysis

Aleksandar Josifovic, Jonathan Corney

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

1 Citation (Scopus)

47 Downloads (Pure)

Abstract

Recently the world has seen an explosion of available data, gathered from sensors, experiments and direct measurements. The engineering world is shifting from model-centred simulations to the exploitation of large datasets for insights into machine behaviour. In the case of optimisation, where usually a simulation model is provided for the exploration of the parameter space, the new data-driven approach translates into the use of data for quantifying problems and delivering an optimal solution.

Original language	English
Title of host publication	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Place of Publication	Piscataway, N.J.
Publisher	IEEE
Number of pages	7
ISBN (Print)	978-1-5090-4241-8
DOIs	https://doi.org/10.1109/SSCI.2016.7849990
Publication status	Published - 13 Feb 2017
Event	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Royal Olympic Hotel, Athens, Greece Duration: 6 Dec 2016 → 9 Dec 2016 http://ssci2016.cs.surrey.ac.uk/

Conference

Conference	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Abbreviated title	SSCI 2016
Country/Territory	Greece
City	Athens
Period	6/12/16 → 9/12/16
Internet address	http://ssci2016.cs.surrey.ac.uk/

Keywords

oil technology
accelerometers
data aquisition

Access to Document

10.1109/SSCI.2016.7849990

Josifovic-Corney-IEEE-SSCI-2016-Development-of-industrial-process-characterisationAccepted author manuscript, 1.16 MB

Cite this

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title = "Development of industrial process characterisation through data analysis",

abstract = "Recently the world has seen an explosion of available data, gathered from sensors, experiments and direct measurements. The engineering world is shifting from model-centred simulations to the exploitation of large datasets for insights into machine behaviour. In the case of optimisation, where usually a simulation model is provided for the exploration of the parameter space, the new data-driven approach translates into the use of data for quantifying problems and delivering an optimal solution.",

keywords = "oil technology, accelerometers, data aquisition",

author = "Aleksandar Josifovic and Jonathan Corney",

note = "(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.; 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, SSCI 2016 ; Conference date: 06-12-2016 Through 09-12-2016",

year = "2017",

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doi = "10.1109/SSCI.2016.7849990",

language = "English",

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AU - Corney, Jonathan

N1 - (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

PY - 2017/2/13

Y1 - 2017/2/13

N2 - Recently the world has seen an explosion of available data, gathered from sensors, experiments and direct measurements. The engineering world is shifting from model-centred simulations to the exploitation of large datasets for insights into machine behaviour. In the case of optimisation, where usually a simulation model is provided for the exploration of the parameter space, the new data-driven approach translates into the use of data for quantifying problems and delivering an optimal solution.

AB - Recently the world has seen an explosion of available data, gathered from sensors, experiments and direct measurements. The engineering world is shifting from model-centred simulations to the exploitation of large datasets for insights into machine behaviour. In the case of optimisation, where usually a simulation model is provided for the exploration of the parameter space, the new data-driven approach translates into the use of data for quantifying problems and delivering an optimal solution.

KW - oil technology

KW - accelerometers

KW - data aquisition

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U2 - 10.1109/SSCI.2016.7849990

DO - 10.1109/SSCI.2016.7849990

M3 - Conference contribution book

AN - SCOPUS:85016082628

SN - 978-1-5090-4241-8

BT - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

PB - IEEE

CY - Piscataway, N.J.

T2 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

Y2 - 6 December 2016 through 9 December 2016

ER -

Development of industrial process characterisation through data analysis

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Reducing the environmental impact of hydraulic fracturing through design optimisation of positive displacement pumps

Valve dynamics in multi-cylinder positive displacement pump model

Modeling a variable speed drive for positive displacement pump

Elsevier (Publisher)

Cite this

Development of industrial process characterisation through data analysis

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Research output

Reducing the environmental impact of hydraulic fracturing through design optimisation of positive displacement pumps

Valve dynamics in multi-cylinder positive displacement pump model

Modeling a variable speed drive for positive displacement pump

Activities

Elsevier (Publisher)

Cite this