To reduce coal consumption, nitrogen oxide (NOx), and carbon emissions for coal-fired units, combustion optimization has become not only a hot issue for scientists but also a practical engineering for engineers. A data-driven multiple linear regression (MLR) model is proposed to solve the time-consuming problems of boiler online combustion optimization systems. Firstly, A whole year’s worth of the historical operating data preprocessing procedure of a coal-fired boiler in a power station including data resampling, data cleaning, steady-state selection, and cluster analysis is performed. In order to meet the applicable conditions of the linear model, the historical operating data are divided into different sub-datasets (combination mode of coal mills, main steam flow, ambient temperature, lower heating value of coal). Secondly, the multi-objective optimization strategy of economical, carbon, and NOx emissions indexes is employed to select operating optimum data packets, and a new dataset is established that is better than the average value of the optimization target in each sub-dataset. On this basis, a stepwise regression algorithm (SRA) is used to select the specific manipulated variables (MVs) that are significant to the multiple optimization targets from 47 candidate MVs in each sub-dataset (different partitions have different types of MVs), and an MLR prediction model is developed. In order to further realize combustion optimization control, the MVs are optimized by employing the MLR model. According to the deviation between the optimal value and the real-time value of the MVs, a boiler combustion closed-loop control system is developed, which is connected with the DCS using the sum of the deviation signal and the corresponding original one. Then, a boiler combustion application test was carried out under some working conditions to verify the feasibility and effectiveness of the approach. The update time of the system signals running on industrial computers is less than 1 s and suitable for online applications. Finally, a full-scale test of the combustion optimization online control system (OCS) is executed. The results show that the boiler thermal efficiency increased by 0.39% based on standard coal, the NOx emissions reduced by 2.85% and the decarbonization effect is significant.

With the proportion of renewable energy power in the electricity market gradually increasing, coal-fired power is transforming from primary to basic power, with it providing peak and frequency shaving. However, most current methods for peaking below 50% load have been applied industrially, sacrificing the efficiency of the unit. This is not in line with the goals of energy conservation and emission reduction. Therefore, this study proposes a new preheating-based peaking method. This study experimentally and simulatively explores the flow characteristics, pyrolysis gas law, and NO_x emission characteristics of a preheating burner at 40–100% load. The results show that the burner has a significant preheating effect, producing high-temperature char and large amounts of pyrolysis gas. As the load decreases, the burner exit temperature increases, whereas the airflow stiffness decreases. There is little variation in the pyrolysis gas concentration between 40% and 100% loads. The NO_x concentration at the burner outlet increases and the reduction efficiency decreases with decreasing load. At 40% load, NO_x emissions are 91.53 mg/Nm³ and the reduction efficiency reaches 95.9%. Therefore, preheating is an economical, stable, and low-NO_x-emission-peaking method. This study provides theoretical guidance for the application of preheating burners for the 40–100% load peaking of coal-fired units.