석탄 가스화 복합 발전(coal-based IGCC power plant)에서 가스화기의 동적 상태와 성능이 플랜트 전체에 큰 영향을 미치므로, 가스화기가 문제 없이 운전 되도록 제어 하는 것은 전체 플랜트의 가동률을 높이는 데 있어 매우 중요한 일이라 할 수 있다. 가스화기의 안정적인 운전을 위해서는 고체 슬래그 층의 두께가 일정하게 유지되어야 하는데, 고체슬래그 두께는 실시간 측정이 불가능하기 때문에 상태를 추정하여 추론 제어해야 한다. 본 연구에서는 Shell-type 가스화기의 동적 모사 모델을 개발하고 다변수 시스템의 추론 제어를 위한 방법으로 두 가지 선형 예측 제어 기법을 적용하여 그 특성을 분석하였다. 측정되지 않는 변수의 상태 추정을 위해 Kalman 필터 기법을 이용하였다. 측정 불가능한 1차 변수를 대신하여 측정 가능한 2차 변수를 제어하는 전통적인 추론 제어 기법으로는 외란의 종류에 따라 추론 제어가 불가능 할 수 있음을 확인하였고, 측정되지 않는 슬래그 두께를 Kalman 필터 기법을 이용하여 추정하여 성능 예측에 반영하고 외란 모델을 사용하여 예측 제어하는 경우 두 가지 측정 불가능한 외란 모두에 대해 추론 제어가 가능함을 확인하였다.

In the Integrated Gasification Combined Cycle (IGCC), the stability of the gasifier has strong influences on the rest of the plant as it supplies the feed to the rest of the power generation system. In order to ensure a safe and stable operation of the entrained-flow gasifier and for protection of the gasifier wall from the high internal temperature, the solid slag layer thickness should be regulated tightly but its control is hampered by the lack of on-line measurement for_x000D_ it. In this study, a previously published dynamic simulation model of a Shell-type gasifier is reproduced and two different linear model predictive control strategies are simulated and compared for multivariable control of the entrained-flow gasifier. The first approach is to control a measured secondary variable as a surrogate to the unmeasured slag thickness. The control results of this approach depended strongly on the unmeasured disturbance type. In other words, the slag thickness could not be controlled tightly for a certain type of unmeasured disturbance. The second approach is to estimate the unmeasured slag thickness through the Kalman filter and to use the estimate to predict and control the slag thickness directly. Using the second approach, the slag thickness could be controlled well regardless of the type of unmeasured disturbances.

IGCC Gasifier Slag MPC Inferential Control

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