Katsumi Shimohira

An adaptive State estimator for pulverizer control using moments of particle size distribution

An adaptive state estimator for pulverizers consisting of blending, grinding, and classifying processes has been developed in order to improve control of pulverized-coal-fired power stations. Though coal flow and non-Gaussian particle size distributions in the processes are mutually related, the estimator is able to efficiently simulate flow and normalized moments of the distributions with a state vector. The estimator also identifies coal grindability for adapting to variation in coal characteristic in parallel with the process simulation. The accuracy of the adaptive estimation and the effectiveness in improving the load-swinging performance have been validated at a 1000-MWe class power station.

An Advanced Boiler Start-Up Controller Using the Plant Inverse Model

In order to improve the boiler start-up performance including flexibility in schedules, management of the fatigue damage at pressured parts and reduction in the operational costs, an advanced controller is proposed. It consists of a “demand calculator for the steam pressure and temperature changing rate, considering the schedules and the fatigue damage” and a “fuel-minimum controller for the demanded changing rate applying an inverse model of the physical phenomena in the plant”. In this report, derivation of the inverse model and the controlling logic as well as a performance comparison on an actual 600MW power plant circumstance with the prior art are introduced.