The ensemble learning approach based on random sampling or cluster sampling was developed to predict the number of desulfurization tower circulating pumps opened. The database was constructed from a realistic power plants, the outliers and unsteady values in the initial one were deleted, and the input parameters of the ensemble learning model with high correlation coefficients with the output were extracted. The problems of imbalanced samples in classification, missing evaluation criteria for optimal samples and desulfurization optimization were solved. Results showed that the improved ensemble learning model had a 33% increase in overall prediction accuracy compared with the original model. In addition, the cluster sampling was slightly better than the random sampling. Furthermore, the recall of a single category prediction was analyzed, and the recall values of different algorithms for the minority category and the majority category were compared. Results showed that the two improved sampling methods had greatly improved the minority category prediction, and the recall reached more than 90%, besides it also had certain improvement on the majority. Finally, the difference in sample distribution and model accuracy was discussed when the pump combination was used as the model’s output.
Zhi-hui GE,Jiang-kuan XING,Kun LUO,Jian-ren FAN. Application of ensemble learning based on preferred sample selection in desulfurization optimization process. Journal of ZheJiang University (Engineering Science), 2021, 55(8): 1566-1575.
Tab.1Correlation coefficient with output and feature importance to model of different input parameters
Fig.2Heat map of correlation coefficient between model input variables
编号
输入参数
平均值
最小值
最大值
1
1号脱硫原烟气O2体积分数/%
6.5
3.1
10.6
2
1号脱硫原烟气含尘质量浓度/（mg·m?3）
21.6
15.1
34.0
3
1号脱硫净烟气含尘质量浓度/（mg·m?3）
1.8
0.2
3.4
4
1号吸收塔石膏浆液质量浓度/（kg·m?3）
1 132.0
1 091.7
1 171.3
5
1号脱硫原烟气SO2质量浓度/（mg·m?3）
1 582.0
710.7
2 434.0
6
1号脱硫净烟气SO2体积分数/%
5.9
1.0
11.1
7
1号吸收塔石灰石供浆体积流量/（m3·h?1）
14.7
0.0
32.2
8
总风质量流量/（t·h?1）
1.8
0.2
5.0
9
pH值
5.5
4.8
6.2
10
进口烟气温度/℃
95.2
80.8
109.3
Tab.2Characteristic of samples after outlier removal
Fig.3Sample distribution map after normalization for different input features
Fig.4Change of unit power over time
Fig.5Division of steady-state data and unsteady-state data of unit power
类别
N
AdaBoost
RUSBoost
CUSBoost
2
10 977
5 857
5 857
3
22 781
5 857
5 857
4
5 857
5 857
5 857
Tab.3Number of samples selected by different algorithms for different categories
Fig.6Line chart of model’s accuracy variation for different cluster numbers
Fig.7Model prediction accuracy changes for different numbers of classify in train and test set
Fig.8Recall of different algorithms for different numbers of circulating pump
Fig.9Number of samples for different pump combinations
Fig.10Number of samples for different pump combinations after simplification
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