Multi-objective optimization of CO boiler combustion chamber in the RFCC unit using NSGA II algorithm

In this study, the combustion chamber of a CO steam boiler is multi-objectively optimized (MOO) using computational fluid dynamics (CFD) and non-dominated sorted genetic algorithm II (NSGA II) algorithm. Multi-objective optimization with two input parameters and two objectives are followed. Input parameters are the two inlet air streams into the chamber called primary and secondary air streams. The optimization aims to maximize the outlet gas's temperature and minimize the chamber's wall's temperature. Therefore, these two parameters are considered as the objectives of the optimization. To obtain an adequate number of nodes in the discretized system, a step-wise increase of the mesh is followed and finally, 6.1 million elements are selected. Due to the steam boiler complexity, some simplifications are assumed while it is assured that no remarkable error is introduced. Computations reveal that ignoring radiation at the beginning of the combustion chamber can cause an error as significant as 400 degrees C. In contrast, for the end of the combustion chamber, where optimization is performed over, an error up to 7.4 degrees C emerges. NSGA II algorithm is used for multi-objective optimization and the Pareto diagram is extracted and reported. Ultimately, the effect of different fluid intensity of primary and secondary air streams on different parameters like outlet temperature, gas composition in the outlet and the chamber's wall temperature is presented. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

In this study, the combustion chamber of a CO steam boiler is multi-objectively optimized using computational fluid dynamics and NSGA II algorithm, with the main goals of maximizing outlet gas temperature and minimizing chamber wall temperature. A step-wise increase of the mesh was used to ensure an adequate number of nodes, and simplifications were made to deal with the complexity of the steam boiler system.