A Novel Multi-Objective Non-Dominated Sorting Beluga Whale Optimization Algorithm for WFGD Process

The operation of coal-fired power plants emits a significant amount of SO
2
, causing a considerable environmental challenge. Wet flue gas desulfurization (WFGD) is the mostly used desulfurization method in current practice for address this issue, however, the simultaneous optimization of desulfurization efficiency and cost remains challenging. Aiming to achieve optimal performance and reduce the computational complexity, this paper proposes a novel multi-objective non-dominated sorting beluga whale optimization (NSBWO) algorithm to concurrently optimize desulfurization efficiency and cost. Derived from the NSGA-II framework, the NSBWO algorithm adopts the beluga whale algorithm to generate offspring instead of the conventional genetic algorithm used in the original algorithm. This modification alleviates the concerns about performance dependence on user-defined parameters, enhancing its performance in multi-objective optimization. Experimental results demonstrate that, compared to classical algorithms, NSBWO exhibits superior overall performance and faster convergence speed. Finally, the algorithm is applied to real-world data sourced from the flue gas desulfurization system of a 1000MW coal-fired power plant unit. The obtained results validate the algorithm's effectiveness in optimizing both the efficiency and cost of the wet flue gas desulfurization system.