Multiple Model Generalized Predictive Control for Circulating Fluidized Bed Boiler Bed Temperature

Multiple Model Generalized Predictive Control (GMPC) serves as an effective strategy for bed temperature regulation in circulating fluidized bed boilers. The GMPC approach employs multiple models to characterize system dynamic responses under different operating conditions, adaptively selecting optimal models for system control. In code implementation, this typically involves creating a model bank with transfer functions or state-space representations for various operational modes, coupled with a switching mechanism based on real-time performance indices. This method has found extensive application in numerous industrial sectors including chemical, petrochemical, and energy industries.

Within GMPC implementation, bed temperature predictions are generated and controller outputs are adjusted accordingly. To enhance prediction accuracy, various modeling techniques can be employed such as ARX-based GMPC utilizing autoregressive exogenous models with least-squares parameter estimation, or neural network-based GMPC employing multilayer perceptrons with backpropagation training. The algorithmic framework typically includes a cost function minimization routine using quadratic programming solvers. Furthermore, GMPC can incorporate constraints through optimization algorithms like interior-point methods, handling input/output limitations while optimizing performance metrics including controller convergence rate and steady-state error through weight matrix tuning in the objective function.

In conclusion, GMPC represents a robust control strategy applicable across diverse industrial scenarios. By leveraging multiple models for adaptive control through model probability weighting or hard switching algorithms, GMPC significantly enhances system control performance, achieving more precise and stable regulation with embedded fault tolerance capabilities.