Data Analytics Based Dual‐Optimized Adaptive Model Predictive Control for the Power Plant Boiler
Zhenhao Tang, Haiyang Zhang, Ping Che, Shengxian Cao, Zhiyong Zhao
Source abstract
To control the furnace temperature of a power plant boiler precisely, a dual‐optimized adaptive model predictive control (DoAMPC) method is designed based on the data analytics. In the proposed DoAMPC, an accurate predictive model is constructed adaptively by the hybrid algorithm of the least squares support vector machine and differential evolution method. Then, an optimization problem is constructed based on the predictive model and many constraint conditions. To control the boiler furnace temperature, the differential evolution method is utilized to decide the control variables by solving the optimization problem. The proposed method can adapt to the time‐varying situation by updating the sample data. The experimental results based on practical data illustrate that the DoAMPC can control the boiler furnace temperature with errors of less than 1.5% which can meet the requirements of the real production process.
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