Nonlinear model identification and adaptive control of CO 2 sequestration process in saline aquifers using artificial neural networks

Applied Soft Computing Journal

Volumn 12, Issue 11, 2012, Pages 3379-3389

  Salahshoor, Karim ^a   Hajisalehi, Mohammad Hasan ^a   Sefat, Morteza Haghighat ^b  

^a PETROLEUM UNIVERSITY OF TECHNOLOGY   (Iran)

^b Iranian Offshore Oil Company   (Iran)

Author keywords

Carbon dioxide sequestration; Extended Kalman filter (EKF); GAP RBF neural network; Nonlinear model predictive control (NMPC); System identification; Unscented Kalman filter (UKF)

Indexed keywords

ADAPTIVE CONTROL; BLACK-OIL RESERVOIRS; CAP ROCK; CARBON DIOXIDE SEQUESTRATION; CONTROL METHODOLOGY; CONTROL SCHEMES; GROWING AND PRUNING RADIAL BASIS FUNCTIONS; INJECTION PROCESS; INTENSIVE RESEARCH; INTERFACE PROGRAM; MATLAB SOFTWARE PACKAGE; NEURAL NETWORK MODEL; NONLINEAR MODEL IDENTIFICATION; NONLINEAR MODEL PREDICTIVE CONTROL; PREDICTION OF RESERVOIR; PRESSURE PROFILES; PRIMARY OBJECTIVE; RESEARCH STUDIES; RESERVOIR PRESSURES; SALINE AQUIFERS; SEQUESTRATION PROCESS; TEST SCENARIO; TIME-DEPENDENT; UNSCENTED KALMAN FILTER;

AQUIFERS; CARBON DIOXIDE PROCESS; EXTENDED KALMAN FILTERS; HYDROGEOLOGY; IDENTIFICATION (CONTROL SYSTEMS); MATLAB; NEURAL NETWORKS; NONLINEAR FILTERING; NONLINEAR SYSTEMS; PETROLEUM RESERVOIRS; PREDICTIVE CONTROL SYSTEMS; RADIAL BASIS FUNCTION NETWORKS;

CARBON DIOXIDE;

EID: 84865851166     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2012.07.006     Document Type: Article

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