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轧钢油泥与典型危废配伍焚烧特性研究

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针对多源危险废物在协同焚烧过程中存在复杂的燃烧特性和交互反应,通过原料配伍和热重实验研究了轧钢油泥、油漆渣和树脂共混物的燃烧特性、动力学参数及相互作用,并通过建立人工神经网络模型(ANN)预测协同作用效果.研究表明:油泥Ⅰ、油泥Ⅱ、油漆渣、废树脂质量分数分别为 20%、25%、40%和 15%时共混物综合燃烧性能最佳,其最大失重速率为 9.77%/min,并且提高加热速率有利于提升共混物燃烧性能.由动力学分析得到配伍 3 共混物活化能的变化趋势,在转化率为 0.5 时达到最大,随后急剧减小.此外,ANN 9-10 模型的训练和测试 R2 高达 0.99,通过与实验结果对比验证,基本满足对相互作用效果的预测,该模型可以逆向指导和优化实验设计.
Incineration Characteristics of Steel-Rolling Oily Sludge Combined with Typical Hazardous Waste
Aiming at the complex combustion characteristics and interaction reactions of multi-source hazardous wastes in the process of co-incineration,the combustion characteristics,kinetic parameters and interactions of steel-rolling oily sludge,paint slag and resin mixtures were investigated through raw material compatibility and thermogravimetric experiments,and the synergistic effect was predicted by establishing an artificial neural net-work(ANN)model.The results showed that when the proportions of oil sludge Ⅰ,oil sludge Ⅱ,paint sludge and resin were 20%,25%,40%and 15%,respectively,the integrated combustion performance of the mixture was the best,with a maximum weight loss rate of 9.77%/min.Increasing heating rate was favorable for improving the combustion performance of the mixture.The trend of activation energy of the blend 3 was obtained from the kinetic analysis,which reached a maximum at a conversion rate of 0.5 and then decreased sharply.In addition,the training and testing R2 of the ANN 9-10 model was as high as 0.99,which was verified through the comparison with the experimental results and basically met the requirement of predicting the interaction effect,and the model could reversely guide and optimize the experimental design.

steel-rolling oily sludgecompatibilitycombustion characteristicsartificial neural network

杨浩冉、艾泽健、谢斌、李谦、周文浩、李海龙

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中南大学能源科学与工程学院,长沙 410083

中冶长天国际工程有限公司,长沙 410205

湖南华菱湘潭钢铁有限公司,湘潭 411101

轧钢油泥 配伍 燃烧特性 人工神经网络

2025

燃烧科学与技术
天津大学

燃烧科学与技术

北大核心
影响因子:0.617
ISSN:1006-8740
年,卷(期):2025.31(1)