Boiler combustion modeling and optimization based on reinforcement learning algorithm

With the increasingly harsh air environment and the gradual reduction of coal resources, the optimization technology of boiler combustion can achieve efficient and low pollution work, which is of great significance for protecting the environment and resources. Aiming at the problem that traditional learning algorithms are difficult to construct boiler combustion models, a boiler combustion optimization method is proposed by combining the reinforcement learning algorithm with principal component analysis. Principal component analysis is applied to reduce the dimensionality of high-dimensional data transmitted from the boiler combustion system, standardize the original data, and construct a boiler combustion model using deep reinforcement learning algorithms. From the results, the root mean square error of the designed method was 0.0217, and the mean absolute error was 0.0191. Compared with the traditional deep reinforcement learning algorithm, the root mean square error and average absolute error were reduced by 0.0158 and 0.0188, respectively. The algorithm is more stable than double deep Q-Network and Q-learning algorithm. In the practical application, the efficiency of boiler combustion reached 92.911%. The above results indicate that the improved deep reinforcement learning algorithm can optimize and construct models for the characteristic variables of boiler combustion. The research method provides more precise technical support for the efficient modeling and optimization of the boiler combustion process, effectively enhancing combustion efficiency and reducing pollutant emissions. This method has good adaptability and stability in actual industrial scenarios, providing a feasible path for the intelligent transformation of energy utilization.