Temperature Prediction and Control of Cement Preheater Using Alternative Fuels

Baasan-Ochir Baljinnyam; Yerim Lee; Boseon Yoo; Jaesik Choi

doi:10.7844/kirr.2024.33.4.3

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2024 Vol.33, Issue 4 Next Page

Research Paper

Temperature Prediction and Control of Cement Preheater Using Alternative Fuels 대체연료를 사용하는 시멘트 예열실 온도 예측 제어

31 August 2024. pp. 3-14

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Abstract

The preheating and calcination processes in cement manufacturing, which are crucial for producing the cement intermediate product clinker, require a substantial quantity of fossil fuels to generate high-temperature thermal energy. However, owing to the ever-increasing severity of environmental pollution, considerable efforts are being made to reduce carbon emissions from fossil fuels in the cement industry. Several preliminary studies have focused on increasing the usage of alternative fuels like refuse-derived fuel (RDF). Alternative fuels offer several advantages, such as reduced carbon emissions, mitigated generation of nitrogen oxides, and incineration in preheaters and kilns instead of landfilling. However, owing to the diverse compositions of alternative fuels, estimating their calorific value is challenging. This makes it difficult to regulate the preheater stability, thereby limiting the usage of alternative fuels. Therefore, in this study, a model based on deep neural networks is developed to accurately predict the preheater temperature and propose optimal fuel input quantities using explainable artificial intelligence. Utilizing the proposed model in actual preheating process sites resulted in a 5% reduction in fossil fuel usage, 5%p increase in the substitution rate with alternative fuels, and 35% reduction in preheater temperature fluctuations.

Keywords

Alternative Fuels

Cement Preheater

DNN (Deep Neural Network)

XAI (Explainable AI)

Predictive Control System

시멘트 제조공정 중 예열 및 소성 공정은 시멘트 반제품인 클링커를 생산하는 주요 공정으로, 고온의 열에너지를 발생시키기 위해 많은 양의 화석연료를 사용한다. 하지만, 최근 환경오염 문제의 심각성으로 인해 시멘트 산업에서 화석연료로부터 기인하는 탄소 배출량을 저감하고자 하는 시도가 지속되고 있다. 대표적인 해결 방안으로 화석연료 대신 폐기물 유래 연료(RDF, Refuse-Derived Fuel)와 같은 대체연료의 사용량을 증대시키기 위한 선행 연구 사례들이 많다. 대체연료는 탄소뿐만 아니라 질소산화물 발생량 또한 저감시킬 수 있고 폐기물을 매립하는 대신 예열실 및 소성로에서 연소시켜 처리할 수 있다는 장점이 있다. 하지만 다양한 성분으로 구성된 대체연료의 특성상 열량을 추정할 수 없다는 문제점이 있으며, 이로 인해 대체연료 사용량을 증대시키고 안정적으로 예열실을 제어하는 데 어려움이 있다. 따라서 본 연구에서는 심층 신경망을 기반으로 예열실 온도를 예측하는 모델을 개발하여 미래의 예열실 온도에 대한 비교적 정확한 예측값을 제공하고, 설명가능 인공지능을 활용하여 최적의 연료 투입량을 제시하는 솔루션을 제안하였다. 제안된 솔루션은 실제 예열 공정 현장에 적용되어 화석연료 사용량 5% 감소, 대체연료 대체율 5%p 증가, 예열실 온도 변동 35% 감소하는 성과를 달성할 수 있었다.

키워드

대체연료

시멘트 예열실

심층 신경망

설명가능 인공지능

예측 제어 시스템

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Information

Publisher :The Korean Institute of Resources Recycling
Publisher(Ko) :한국자원리싸이클링학회
Journal Title :Resources Recycling
Journal Title(Ko) :자원리싸이클링
Volume : 33
No :4
Pages :3-14
Received Date : 2024-06-18
Revised Date : 2024-07-02
Accepted Date : 2024-07-03
DOI :https://doi.org/10.7844/kirr.2024.33.4.3

Resources Recycling ISSN:2765-3439(Print) 2765-3447(Online) 자원리싸이클링

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