Induction heating demonstrates effective ammonium bisulfate removal in power plant systems, suggesting improved efficiency and sustainability.
• Induction heating removes ammonium bisulfate (ABS) wastes for the first time • A 3D FEM model simulates inductance, temperature, and heat distribution • High-efficiency power supply uses SiC-based MOSFETs • Hybrid control combines phase shift and frequency tracking methods Selective Catalytic Reduction (SCR) is widely used in coal-fired power plants and industrial facilities to reduce nitrogen oxide (NO x ) emissions. However, it also leads to the formation of Ammonium Bisulfate (ABS), a byproduct that builds up on catalysts and air preheater walls and heat-transfer elements, reducing the efficiency and increasing maintenance costs. Traditional ABS cleaning methods are either inefficient or unsustainable and pose an environmental hazard. This paper introduces a new approach to ABS removal using induction heating, where eddy currents generated by the induction heating system are used to heat up the areas affected by ABS buildup, allowing for quick and efficient deposit removal. Furthermore, we propose an accurate finite element method (FEM) model of the proposed induction heating system to simulate the system’s thermal and electromagnetic behavior, optimize the selection of the electromagnetic components of the system, and predict the temperature and distribution. Moreover, we also present a hybrid control strategy that combines phase-shift and frequency-tracking techniques to optimize and improve the controllability and the efficiency of the proposed induction heating system. Finally, a 2 kW custom-built series resonant inverter powers the system, and its performance was validated through experiments. Results show that at 1500 W, ABS began to melt within 15 seconds and evaporated after 40 seconds, far outperforming conventional methods in efficiency and sustainability. The proposed control approach enhanced system reliability, and simulation outcomes closely matched experimental data, confirming the accuracy of the proposed FEM model.
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Khaleel et al. (2026) studied this question.
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