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Soft sensor solutions for control of oil sands processes
Author(s) -
Khatibisepehr Shima,
Huang Biao,
Domlan Elom,
Naghoosi Elham,
Zhao Yu,
Miao Yu,
Shao Xinguang,
Khare Swanand,
Keshavarz Marziyeh,
Feng Enbo,
Xu Fangwei,
Espejo Aris,
Kadali Ramesh
Publication year - 2013
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.21833
Subject(s) - oil sands , environmental science , control (management) , petroleum engineering , process engineering , geology , computer science , materials science , engineering , asphalt , artificial intelligence , composite material
Oil sands development is both a costly and technically complex business with potential concerns in land use, water consumption and greenhouse gas emissions. Therefore, it is of practical interest to further investigate novel techniques to improve profitability while diligently maintaining environmental compliance. Our approach for finding solutions to achieve this objective is to develop innovative strategies for advanced monitoring, optimisation and control of plant operations. Development of reliable process models is a key requirement for investigating the behaviour of complex systems. Such descriptive models can help to improve analysis, simulation, optimisation, design, control and operation of process systems at both micro and macro levels. This paper presents a summary of some of the successful applications focussed on development and implementation of inferential process models, also known as soft sensors for oil sands processes.