대체연료를 사용하는 시멘트 킬른 온도와 전류 사용량 예측
Prediction of Temperature and Electricity Consumption in Cement Rotary Kiln Using Alternative Fuel
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초록
최근 시멘트 산업은 다양한 가연성 폐자원을 소성 공정의 대체연료로 재사용하고 있다. 폐합성수지와 같은 폐기물 고형연료(RDF, Refuse-Derived Fuel)를 사용하면 천연자원인 유연탄 사용량을 절감할 수 있고, 이산화탄소(CO2), 질소산화물(NOx) 등 대기 오염 물질의 발생량 또한 저감할 수 있다는 장점이 있다. 특히, 폐기물을 매립하지 않고 고온의 킬른에서 대기오염물질 발생 없이 소각할 수 있기 때문에 폐기물 처리 문제를 해결하고 자원순환에 기여할 수 있다. 본 연구는 인공지능 기반 예측 모델과 설명가능 인공지능을 결합하여 대체연료를 사용하는 시멘트 소성 공정의 불안정한 킬른 온도와 모터 전류를 예측하는 기술을 개발하였다. 국내 시멘트 소성 공정 데이터에적합한 인공 신경망을 설계하였으며, 설명가능 인공지능을 통해 보다 정확한 예측 결과를 도출할 수 있고, 예측 결과에 대한 근거를 제공할 수 있다.
Abstract
The cement industry has been increasingly reusing combustible waste materials as alternative fuels in the calcination process. Refuse-derived fuels (RDF), including waste synthetic resins, offer a sustainable solution by reducing reliance on natural resources like bituminous coal and lowering emissions of air pollutants such as carbon dioxide (CO2) and nitrogen oxides (NOx). This practice is particularly advantageous because high-temperature kilns can incinerate waste without releasing additional air pollutants, thus addressing waste disposal challenges and promoting resource circulation. In this study, we developed a novel technology that integrates artificial intelligence (AI)-based prediction models with explainable AI to predict unstable kiln temperatures and motor currents in cement calcination processes using alternative fuels. We designed an artificial neural network customized for domestic cement calcination process data, enabling more accurate predictions through explainable AI, while providing justifications for the prediction results.
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