State estimation-based control of a coal gasifier

On-line state estimation techniques provide a means of inferring real-time values for key process variables that cannot themselves be measured directly. Such state estimates can then form a basis for improved process control. Accordingly, Kalman filtering (KF) is applied to a non-linear coal gasifier system operated initially under a conventional feedback control strategy. Key unmeasured disturbances to gasifier operation arise in discharge pressure and coal feed ´quality´. The fact that by treating both of these variables as unknown process parameters, they can be included in an augmented state and parameter vector and estimated reliably by KF is shown. A proportional feedforward control strategy, acting on these disturbance estimates and linked into the existing conventional feedback control system, is then shown to enhance system performance to both step and sinusoidal discharge pressure changes and to steps of up to 18% around normal coal feed quality. No significant performance difference relative to conventional feedback control is shown in ramping the downstream-generated power output. An important future outcome may be enhanced operation and control of upstream coal preparation plant based on the capability demonstrated here to obtain reliable real-time estimates of coal quality by KF.