Volume 61

A machine learning based intelligent control method for wide-load optimization of COâ‚‚ capture process in coal-fired power plant Teng Zhang, Ming-Jia Li, Lin Gao

Abstract

Under the background of deep peak-shaving in coal-fired power plants, the flexible control of the COâ‚‚ capture process is of great significance for achieving efficiency decarbonization under wide-load range conditions. In this paper, an intelligent control method based on machine learning is proposed to maintain the flue gas capture rate requirements while keeping the energy consumption at a low level across a wide-load range. First, a thermodynamic model for the typical CO2 capture process with 30 wt.% monoethanolamine (MEA) solution is established. Second, considering the key operating parameters of the CO2 capture process, a machine learning model is trained to achieve accurate predictions of the capture rate and specific regeneration energy. Finally, an intelligent control method is proposed to minimize energy consumption while maintaining the required CO2 capture rate across wide-load range. The results show that the energy consumption of CO2 capture can be maintained near the minimum range using the intelligent control method, while maintaining a CO2 capture rate with a relative error of less than 5%, achieving the flexibility, high efficiency and low carbon of the coal-fired power plant.

Keywords CO2 capture process, coal-fired power plant, machine learning, intelligent control, wide-load optimization