ScholarWorks@성균관대학교

초록

This study presents a multi-objective optimization approach to improve the overall performance of a swirl-type biomass furnace fueled by bagasse. The inlet angles of the top, middle, and bottom air injection chambers were selected as design variables, while the maximum inner wall temperature and the pattern factor (PF), which quantifies the uniformity of temperature distribution, were defined as objective functions. A conjugate heat transfer (CHT) model, incorporating both fluid and solid domains, was employed to accurately simulate the coupled thermal-flow behavior of the furnace. To efficiently explore the design space, design of experiments (DoE) were implemented using a combination of Latin hypercube sampling (LHS) and central composite design (CCD), with additional points added in regions of low predictive accuracy. A surrogate model was developed using genetic algorithm (GA) and employed to construct a response surface for optimization. Multi-objective genetic algorithm (MOGA) was then applied to identify Pareto-optimal solutions. The optimal configuration was found to consist of air injection angles of 29°, 149°, and 0° for the top, middle, and bottom chambers, respectively. Compared to the reference model, the optimized model achieved a 5 °C reduction in maximum wall temperature and a 1.54% improvement in PF. These results demonstrate that the proposed optimization strategy effectively improves the structural integrity and thermal uniformity of the biomass furnace.

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