Novel coordinated control method with coal-quality correction of circulating field bed boiler
To improve the load-following capability and the coal-quality applicability of a circulating field bed boiler (CFBB), a new coordinated control method with coal-quality correction is proposed in this paper. This method takes the primary air as another new control variable into the coordinated control of CFBB, which can take advantage of the unburned carbon in bed to improve its load-following capability. Simultaneously, a multivariable predictive controller based on augmented state space model is designed to deal with the CFBB's characteristics such as time-delay and strong coupling. Finally, an improved strategy to correct coal-quality online based on the disturbance observer is provided. Compared with the conventional control scheme, the simulation results for a 330MW CFBB shows that the method proposed in this paper can significantly improve the load-following capability and the coal-quality applicability.
The Steam Pressure Self-Adaptive Control System of a Monoblock with Home-Made 75t/h CFBB
Based on analysis of the CFBB dynamic perfomance and combined the practical thchnological circumstances of the worksite,the authors have put forward a full-coefficients steam pressure self-adaptive control system. The practical application has obtained satisfactory results.
The Application of BP Neural Network to Bed Temperature Control System of CFB Boiler
The combustion in circulated fluidized-bed (CFB) boiler is a complex and multi-variables coupling process. It is too difficult to build an exact mathematic model to describe the process. Bed temperature control system is an important subsystem of burning system, maintaining normal bed temperature is the key for stable running of boiler, and it is difficult for the norm PID algorithm to control it. According to these, we use the bed temperature control system based BP neural network in DCS. The practice shows that it can improve the stability of bed temperature control system with the application of BP neural network.