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Computational modeling study demonstrates that multi-input fuzzy control reduces gas leakage and boosts carbon conversion in chemical looping systems, highlighting enhanced reactor stability.
Most chemical reactors and processes are complex and dynamic and process controllers are usually equipped to improve system stability, particularly for highly dynamic reactors like chemical looping combustion (CLC). However, understanding the in-furnace reacting flow behaviours and, meanwhile, their responses to controllers remains challenging because suitable research methods are not available. In this work, an innovative generic collaborative modelling method is developed to simulate the full-loop CLC system with process controllers by capturing both the detailed behaviour of the reacting flow and its dynamic responses to process control interventions. Specifically, the multiphase reacting flow is simulated by the computational fluid dynamics (CFD) models, and two types of fuzzy logic controllers, i.e., single-input multi-output (SIMO) and multi-input multi-output (MIMO) for comparison, are coupled with the CFD simulation for mitigating the CLC instability. Then, the model is applied to a specific case—mitigating the gas leakage in a CLC, to demonstrate the effectiveness and versatility of this method. The simulation shows that compared to SIMO, the MIMO shows a more comprehensive and balanced impact that can mitigate gas leakage while keeping the oxygen carrier (OC) circulating rate sufficient for oxygen transport. Further, among the three control intervals investigated in the MIMO cases, the 2 s interval exhibits the best performance, reducing gas leakage to 0.38 %, while maintaining a stable and sufficient OC circulating rate (72 kg/m 2 /s) and meanwhile, the CO 2 yield of the fuel reactor is increased from 86.75 % to 89.34 %, indicating a higher carbon conversion efficiency. This generic collaborative simulation method offers a new cost-effective tool for simulating and controlling the CLC and other complex dynamic reacting flow systems with controllers.
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Yu et al. (2025) studied this question.
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