Temperature Prediction and Control of Cement Preheater Using Alternative Fuels

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.