Data Analytics Based Dual-Optimized Adaptive Model Predictive Control for the Power Plant Boiler. (13th February 2017)

• Record Type: Journal Article
• Authors: Tang, Zhenhao; Zhang, Haiyang; Che, Ping; Cao, Shengxian; Zhao, Zhiyong
• Other Names: Kavun Sergii V. Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2017(2017)
• Publication Date: 2017-02-13
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2017/8048962
• Languages: English
• ISSNs: 1024-123X

Abstract

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.