Neural Network Optimization of Solid Fuel Combustion Process Parameters in a Steam Boiler

The problem of the negative impact of power units on the environment can be addressed by optimizing the operational parameters of the Maikuben coal combustion process. The article demonstrates the potential of using neural network technologies to reduce harmful emissions. The implementation of neural network-based control systems can enhance the efficiency of steam boiler operation and solid fuel utilization. A developed method based on artificial neural networks has been tested to optimize the solid fuel combustion process in a steam boiler, aiming to minimize harmful emissions into the environment. The optimization was carried out using a mathematical model based on artificial neural networks, created in a development environment that enables the design, training, and testing of intelligent software modules. The possibility of minimizing the concentration of $\text{NO}_{\mathrm{x}}$ and $\text{SO}_{2}$ based on inputs to the artificial neural networks has been demonstrated. The article also shows the feasibility of solving optimization problems using a mathematical model based on artificial neural networks, exemplified by finding the minimum values of output parameters (concentrations of $\text{NO}_{\mathrm{x}}$ and $\text{SO}_{2}$) for the efficient operation of a power plant. An effective and reliable system for monitoring and controlling the solid fuel combustion process is proposed, utilizing an adaptive control system based on artificial neural networks that takes into account all parameters influencing the solid fuel combustion process.