ScholarWorks@성균관대학교

초록

Small-scale waste incinerators commonly employ selective non-catalytic reduction (SNCR) for NOx control because of its low installation cost. However, inefficient urea injections frequently occur due to limited instrumentation and reliance on manual operation. This study developed and experimentally validated a dynamic SNCR control strategy for a 48 t/d grate-type incinerator, aiming to improve NOx reduction efficiency while reducing reagent consumption. The proposed approach determines the urea injection rate using readily measurable parameters–the furnace exit gas temperature (FEGT) and the O2 concentration at the boiler outlet. Cross-correlation and statistical analyses of long-term operating data revealed that the O2 concentration responds promptly to changes in combustion conditions, whereas FEGT exhibits a stronger correlation with NOx emissions. The stack NOx concentration lags approximately three minutes behind the O2 signal and shows large short-term fluctuations, making direct feedback control of SNCR inefficient. Based on these findings, a dynamic control algorithm was formulated to determine the urea pump frequency primarily using the O2 concentration, supplemented by temperature-based correction. The algorithm was applied to the incinerator for three months via an IoT-based data platform. Field results demonstrated substantial improvements compared with conventional manual control: urea consumption decreased by 27.9%, the average NOx concentration was reduced by 45.0%, and emission variability was significantly suppressed. Direct measurements also confirmed a marked reduction in ammonia slip. These findings suggest that the proposed strategy offers a practical and cost-effective solution for enhancing deNOx performance in small-scale waste incinerators with limited instrumentation, while providing a foundation for future data-driven optimization.

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