SPARSE GP-BASED SOFT SENSOR APPLIED TO THE POWER PLANT

Gaussian processes(GP) are probabilistic kernel machines and moderately simple to implement and use without loss of performance compared with other kernel methods. In order to solve the problem of the invalidation of thermal parameters and optimal running, we investigate an efficient soft sensor approach using sparse online Gaussian processes which is based on a combination of a Bayesian online algorithm together with a sequential construction of a relevant subsample of the data to specify the prediction of the GP model. By using an appealing parameterization and projection techniques that use the reproducing kernel Hilbert space(RKHS) norm, recursions for the effective parameters and a sparse Gaussian approximation of the posterior process are obtained. The thermal parameter soft sensor is of importance for economic monitoring and optimal running in the power plant.