TY - JOUR
T1 - Knowledge acquisition and representation for intelligent operation support in offshore fields
AU - Wu, Jing
AU - Lind, Morten
AU - Zhang, Xinxin
AU - Pardhasaradhi, Karnati
AU - Pathi, Sharat Kumah
AU - Myllerup, Claus Marner
PY - 2021
Y1 - 2021
N2 - Introducing Artificial Intelligence (AI) tools is one of the development trends in complex industrial systems in the industry 4.0 environment. Unique challenges in system operations need to be handled by effective operation support systems. The knowledge-based operation support systems are developing rapidly in recent years. The paper aims at highlighting the concerns of knowledge acquisition and representation in one of the knowledge-based methodologies, the Multilevel Flow Modelling (MFM). A procedure of knowledge acquisition and representation for building MFM models is proposed to aim at improving the overall model quality and consistency. An interface linking systems' instrumentations to MFM functions are introduced. The new reasoning engine is used for MFM based real-time cause-consequence reasoning about dynamic plant situations. The model verification and validation, and the model performance evaluation analysis method are proposed. This paper also provides case studies that illustrate the effectiveness of intelligent operation support by applying MFM to an off-shore water injection system. It demonstrates that the procedure of knowledge acquisition and representation can facilitate the model builders, and ensure the quality of the models used for operation support. (c) 2021 The Author(s). Published by Elsevier B.V. on behalf of Institution of Chemical Engineers.
AB - Introducing Artificial Intelligence (AI) tools is one of the development trends in complex industrial systems in the industry 4.0 environment. Unique challenges in system operations need to be handled by effective operation support systems. The knowledge-based operation support systems are developing rapidly in recent years. The paper aims at highlighting the concerns of knowledge acquisition and representation in one of the knowledge-based methodologies, the Multilevel Flow Modelling (MFM). A procedure of knowledge acquisition and representation for building MFM models is proposed to aim at improving the overall model quality and consistency. An interface linking systems' instrumentations to MFM functions are introduced. The new reasoning engine is used for MFM based real-time cause-consequence reasoning about dynamic plant situations. The model verification and validation, and the model performance evaluation analysis method are proposed. This paper also provides case studies that illustrate the effectiveness of intelligent operation support by applying MFM to an off-shore water injection system. It demonstrates that the procedure of knowledge acquisition and representation can facilitate the model builders, and ensure the quality of the models used for operation support. (c) 2021 The Author(s). Published by Elsevier B.V. on behalf of Institution of Chemical Engineers.
KW - Knowledge acquisition
KW - Knowledge representation
KW - Intelligent decision support
KW - Offshore fields
KW - Functional modelling
U2 - 10.1016/j.psep.2021.09.036
DO - 10.1016/j.psep.2021.09.036
M3 - Journal article
SN - 0957-5820
VL - 155
SP - 415
EP - 443
JO - Process Safety and Environmental Protection
JF - Process Safety and Environmental Protection
ER -