Optimizing desulfurization in industrial applications

Industrial waste gas — known as flue gas — often contains sulfur compounds which, when burned, release dangerous sulfur dioxide (SO₂). Desulfurization technologies use reflux swirling nozzles to remove SO₂ from these waste gases to reduce pollution, but these are often only studied experimentally using existing nozzle configurations.

To supplement experiments, Kuang et al. combined them with numerical studies to better understand how the structure of the nozzle affects water atomization during flue gas desulfurization.

“SO₂ is a harmful gas that not only contributes to the formation of acid rain, but also poses significant risks to human health and the environment,” said author Wenwang Li.

A reflux swirling nozzle works by accelerating water in a swirl chamber to achieve massive centrifugal forces. Water enters the nozzle, and after acceleration, flows out as atomized mist spray. This spray then cools sulfur to help it react with desulfurizers.

By combining and comparing numerical simulations and experiments with the same specifications, the researchers validated simulations for designing and optimizing nozzles for improved efficiency, operating cost, and stability.

They found the characteristics of the reflux swirling nozzle have a large impact on its performance. Increased pressure caused increased outlet velocity, which, in turn, led to less atomization. Noting this relationship, the researchers suggested an ideal reflux nozzle outlet pressure of 1.35 MPa or below.

“We hope this work can provide a theoretical and practical foundation for the optimization of nozzle design in industrial desulfurization systems,” said Li. “By understanding how internal flow characteristics and structural parameters affect atomization performance, engineers can design more efficient reflux swirling nozzles that enhance SO₂ absorption in flue gas treatment.”

Source: “Numerical simulation of reflux swirling nozzles for desulfurization tower,” by Shengqian Kuang, Xiang Wang, Runyang Zhang, Qitao Lin, Ganyong Wu, Yilong Rao, Jianchun Wang, and Wenwang Li, AIP Advances (2025). The article can be accessed at https://doi.org/10.1063/5.0265126 .