Online quality prediction of nonlinear and non-Gaussian chemical processes with shifting dynamics using finite mixture model based Gaussian process regression approach

Chemical Engineering Science

Volumn 82, Issue , 2012, Pages 22-30

  Yu, Jie ^a  

^a MCMASTER UNIVERSITY   (Canada)

Author keywords

Chemical processes; Gaussian process regression; Instrumentation; Mathematical modeling; Nonlinear dynamics; Soft sensor prediction

Indexed keywords

BAYESIAN INFERENCE; CHEMICAL PROCESS; FINITE MIXTURE MODELS; GAUSSIAN PROCESS REGRESSION; GAUSSIAN PROCESS REGRESSION MODEL; GLOBAL MODELS; INSTRUMENTATION; MULTI-MODEL; NON-LINEARITY; NONGAUSSIANITY; NONLINEAR AND NON-GAUSSIAN; NONLINEAR KERNELS; ONLINE QUALITY; OPERATING MODES; POSTERIOR PROBABILITY; PREDICTION ACCURACY; QUALITY PREDICTION; SOFT SENSORS; SWITCHING DYNAMICS; SYSTEM UNCERTAINTIES; TENNESSEE EASTMAN; TEST CASE; TEST SAMPLES; VARIABLE PREDICTIONS;

BAYESIAN NETWORKS; CHEMICAL ENGINEERING; DYNAMICS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); INFERENCE ENGINES; MATHEMATICAL MODELS; MIXTURES; REGRESSION ANALYSIS; SENSORS;

FORECASTING;

EID: 84864805251     PISSN: 00092509     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ces.2012.07.018     Document Type: Article

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