基于混合数据驱动和模型集成的燃煤电厂SCR脱硝出口动态NOx预测模型,Industrial & Engineering Chemistry Research

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基于混合数据驱动和模型集成的燃煤电厂SCR脱硝出口动态NOx预测模型
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2023-08-29 , DOI: 10.1021/acs.iecr.3c01559
Bo An ₁ , Minan Tang ₁ , Jiandong Qiu ₂ , Zhenfen Li ₃ , Wenjuan Wang ₄ , Yaqi Zhang ₁ , Yaguang Yan ₁

Affiliation

针对燃煤电厂选择性催化还原（SCR）系统精确建模的困难，提出一种基于混合数据驱动和模型集成的建模方案，实现NO x 的动态_估算SCR 系统出口处的浓度。首先，采用LOF算法检测原始生产数据中的异常值。采用最大信息系数（MIC）算法计算每个输入变量的时间延迟，并进行相空间重构以增强输入和输出之间的非线性相关性。其次，采用弹性网络算法选择潜在的输入变量，以确定最优输入变量。第三，为了进一步提高建模精度，利用核模糊C均值（KFCM）聚类算法对选定的输入变量进行聚类，并引入Xie-Beni指数来确定最佳聚类数。_{最后，建立了一个预测NOx}的模型。结合模型集成和误差修正策略建立SCR系统出口浓度，集成模型的子学习器为遗传算法优化的反向传播神经网络（GA-BP）模型、最小二乘支持向量机（LSSVM）模型和极限梯度提升（XGBoost）模型。基于实际现场运行数据的验证结果表明，建立的预测模型能够准确预测SCR系统出口NOx_排放浓度，具有良好的动态跟踪性能和预测一致性，满足工业应用的运行要求，并能提供良好的指导作用。用于生产领域。

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Dynamic NOx Prediction Model for SCR Denitrification Outlet of Coal-Fired Power Plants Based on Hybrid Data-Driven and Model Ensemble

Aiming at the difficulty of accurate modeling of selective catalytic reduction (SCR) systems in coal-fired power plants, this paper proposes a modeling scheme based on hybrid data-driven and model ensemble to achieve the dynamic estimation of NO_x concentration at the outlet of SCR systems. First, the LOF algorithm is adopted to detect outliers in the raw production data. The maximum information coefficient (MIC) algorithm is employed to calculate the time delay of each input variable, and phase space reconstruction is performed to enhance the nonlinear correlation between inputs and outputs. Second, the elastic net algorithm is employed to select potential input variables to determine the optimal input variables. Third, to further improve the modeling accuracy, the kernel fuzzy C-means (KFCM) clustering algorithm is utilized to cluster the selected input variables, and the Xie–Beni index is introduced to determine the optimal number of clusters. Finally, a model for predicting the NO_x concentration at the outlet of the SCR system is established by combining the model ensemble and error correction strategies, and the sublearners of the ensemble model are the genetic algorithm optimized back-propagation neural network (GA-BP) model, least-squares support vector machine (LSSVM) model, and extreme gradient boosting (XGBoost) model. The validation results based on actual field operation data show that the established prediction model can accurately predict the outlet NO_x emission concentration of the SCR system with excellent dynamic tracking performance and prediction consistency, which meets the operational requirements of industrial applications and can provide excellent guidance for the production field.

更新日期：2023-08-29

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