首页|基于SVR-PCA的DMTO过程监测研究

基于SVR-PCA的DMTO过程监测研究

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由于DMTO反应器的反应温度和建模变量呈非线性特征,常规的线性模型无法准确捕捉其特征,为此建立了基于SVR-PCA的过程监测模型,能够避免非线性特征对模型的影响.结果表明:SVR-PCA过程监测模型能够比PCA过程监测模型提前 63 min、比DCS系统预警提前 163 min对DMTO反应温度长时间大幅波动进行报警,能够减少反应温度短时间小幅波动和反应温度长时间小幅波动的误报率.
Research on process monitoring of DMTO based on SVR-PCA
Due to the nonlinear nature of DMTO reaction temperature and modeling data,conventional linear model could not accurately capture its characteristics.Therefore,the process monitoring model based on SVR-PCA was constructed to mitigate the impact of nonlinear features on the model.The results showed that the SVR-PCA process monitoring model could provide an alarm for significant and long-term fluctuations in DMTO reaction temperature 63 minutes earlier than the PCA process monitoring model,and 163 minutes earlier than the DCS system.It could also reduce the false alarm rate for short-term small fluctuations and long-term small fluctuations in reaction temperature.

DMTOreaction temperatureprocess monitoringsupport vector regressionprincipal component analysis

赵泽盟、李洋、李超、史元腾

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中煤能源研究院有限责任公司,陕西 西安 710054

中煤西安设计工程有限责任公司,陕西 西安 710054

清华大学无锡应用技术研究院,江苏 无锡 214072

DMTO 反应温度 过程监测 支持向量回归 主元分析模型

中国中煤能源集团重点科技项目

20211CY005

2024

煤化工
赛鼎工程有限公司(原中国化学工业第二设计院),全国煤化工信息站,全国煤化工设计技术中心

煤化工

CSTPCD
影响因子:0.629
ISSN:1005-9598
年,卷(期):2024.52(4)
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