基于随机配置网络的井下供给风量建模

王前进; 杨春雨; 马小平; 张春富; 彭思敏

doi:10.16383/j.aas.c190602

基于随机配置网络的井下供给风量建模

doi: 10.16383/j.aas.c190602

1.
盐城工学院电气工程学院盐城 224051
2.
中国矿业大学信息与控制工程学院徐州 221116

基金项目: 国家自然科学基金(61873272, 61603392), 江苏省自然科学基金(BK20191043), 江苏省“双创团队” 项目(2017), 盐城工学院校级科研项目(xjr2019018)资助

详细信息

作者简介:
王前进：盐城工学院电气工程学院讲师. 2018年获得中国矿业大学博士学位. 主要研究方向为数据驱动建模与控制, 机器学习算法. E-mail: wangqianjinabc@163.com

杨春雨：中国矿业大学信息与控制工程学院教授. 2009年获得东北大学博士学位. 主要研究方向为广义系统和鲁棒控制. E-mail: chunyuyang@cumt.edu.cn

马小平：中国矿业大学信息与控制工程学院教授. 主要研究方向为过程控制, 网络控制系统, 故障诊断. 本文通信作者. E-mail: xpma@cumt.edu.cn

张春富：盐城工学院电气工程学院副教授. 2007年获得哈尔滨工业大学博士学位. 主要研究方向为自动化测控. E-mail: zhangchunfu@hit.edu.cn

彭思敏：盐城工学院副教授, IEEE高级会员. 2013年获得上海交通大学电力系统及其自动化专业博士学位. 主要研究方向为新能源发电, 电池储能系统及微电网控制技术. E-mail: psmsteven@163.com

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出版历程
- 收稿日期: 2019-08-24
- 录用日期: 2019-11-16
- 网络出版日期: 2021-06-30
- 刊出日期: 2021-08-20

Underground Airflow Quantity Modeling Based on SCN

1.
School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051
2.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116

Funds: Supported by National Natural Science Foundation of China (61873272, 61603392), Natural Science Foundation of Jiangsu Province (BK20191043), Jiangsu Dual Creative Teams Programme Project (2017), Funding for School-Level Research Projects of Yancheng Institute of Technology (xjr2019018)

More Information

Author Bio:
WANG Qian-Jin　Lecturer at the School of Electrical Engineering, Yancheng Institute of Technology. He received his Ph. D. degree from China University of Mining and Technology in 2018. His research interest covers data-driven modeling and control, and machine learning algorithm

YANG Chun-Yu　Professor at the School of Information and Control Engineering, China University of Mining and Technology. He received his Ph. D. degree from Northeastern University in 2009. His research interest covers descriptor systems and robust control

MA Xiao-Ping　Professor at the School of Information and Control Engineering, China University of Mining and Technology. His research interest covers process control, networked control system, and fault detection. Corresponding author of this paper

ZHANG Chun-Fu　Associate professor at the School of Electrical Engineering, Yancheng Institute of Technology. He received his Ph. D. degree from Harbin Institute of Technology in 2007. His research interest covers automatic test and control

PENG Si-Min　 Associate professor at Yancheng Institute of Technology, IEEE senior member. He received his Ph. D. degree in electric power system and its automation from Shanghai Jiao Tong University in 2013. His research interest covers control of renewable sources generation, battery energy storage system and microgrid

摘要

摘要:
主通风机切换过程中, 取压风量测量作为监测井下供给风量的主要手段, 是矿井主扇通风系统安全、稳定与经济运行的重要保障. 然而, 由于取压孔极易出现堵塞现象, 需要频繁维护, 导致无法实时测量井下供给风量, 难以实现主通风机切换过程的闭环优化控制. 同时, 随着隐含层节点数的增加, 基于随机配置网络(Stochastic configuration network, SCN)的估计模型存在过拟合和泛化能力差的缺点. 为了解决上述问题, 结合正则化(Regularization, R)技术, 本文提出一种新型的改进SCN算法, 即RSC算法, 用于井下供给风量的建模. 基准回归分析和工业实验表明: 与SCN方法相比, 建立的RSC模型具有较高的模型精度和较好的泛化性能.
- 主通风机 /
- 切换过程 /
- 井下供给风量 /
- 随机配置网络 /
- 正则化
Abstract:
In a main fan switchover process (MFSP), airflow quantity measuring by pressure drop across a duct is the main means for monitoring the distribution of underground airflow quantity (UAQ). It ensures the safe, stable and economic operation of the mine main fan ventilation system. However, as the pressure port is prone to blocking and requires frequent maintenance, it is impossible to realize the real-time measurement of UAQ and closed-loop optimal control of MFSP. Meanwhile, with the increase of number of hidden nodes, the estimation model based on stochastic configuration network (SCN) has the disadvantages of overfitting and poor generalization ability. To solve the above problems, this paper develops a novel improved SCN algorithm by incorporating the regularization (R) technique called RSC algorithm. Benchmark regression analysis and industrial tests show that compared with SCN, the established RSC model possesses higher model accuracy and better generalization ability.
- Main fan /
- switchover process /
- underground airflow quantity /
- stochastic configuration network /
- regularization

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图 1 主通风机切换过程示意图

Fig. 1 Diagram of a main fan switchover process

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图 2 不同$C$下RSC-II和SCN-III算法对函数近似的训练与测试精度结果对比

Fig. 2 The result comparison of training and testing accuracy of RSC-II and SCN-III on function approximation with varying $C$

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图 3 (a)包含5 %异常值的1 000个函数近似训练样本和目标函数; (b)两种算法对测试数据的近似性能

Fig. 3 (a) 1 000 training samples containing 5 % outliers for function approximation and target function; (b) Approximation performance on the test dataset by two learning algorithms

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图 4 平均两种算法在测试数据集上的测试RMSE

Fig. 4 Average testing RMSE of the two algorithms on the test dataset

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图 5 不同$L_{\max}$下RSC-II和SCN-III算法对基准数据集的测试RMSE对比

Fig. 5 Test RMSE comparison of the RSC-II and SCN-III algorithms on four benchmark datasets with different $L_{\max}$

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图 6 平均两种算法在实际MFSP数据集上的测试RMSE

Fig. 6 Average testing RMSE of the two algorithms on the actual MFSP dataset

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图 7 $L_{\max} = 50$所对应的MFSP数据集的测试性能: (a) SCN-III; (b) RSC-II

Fig. 7 Test performance at $L_{\max} = 50$ on the actual MFSP dataset: (a) SCN-III; (b) RSC-II

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表 1 主通风机切换过程相关变量

Table 1 Related variables in the MFSP

变量	符号	单位
井下供给风量	$Q_{\rm 0}$	${\rm m^3/s}$
井下风阻	$R_{\rm 0}$	${\rm kg/m^7}$
一号垂直风门风量	$Q_{\rm 1c}$	${\rm m^3/s}$
一号垂直风门风阻	$R_{\rm 1c}$	${ \rm kg/m^7}$
一号水平风门风量	$Q_{\rm 1s}$	${\rm m^3/s}$
一号水平风门风阻	$R_{\rm 1s}$	${\rm kg/m^7}$
一号主通风机风量	$Q_{\rm 1m}$	${\rm m^3/s}$
一号主通风机压头	$H_{\rm 1d}$	${\rm Pa}$
二号垂直风门风量	$Q_{\rm 2c}$	${\rm m^3/s}$
二号垂直风门风阻	$R_{\rm 2c}$	${\rm kg/m^7}$
二号水平风门风量	$Q_{\rm 2s}$	${\rm m^3/s}$
二号水平风门风阻	$R_{\rm 2s}$	${\rm kg/m^7}$
二号主通风机风量	$Q_{\rm 2m}$	${\rm m^3/s}$
二号主通风机压头	$H_{\rm 2d}$	${\rm Pa}$

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表 2 函数近似的性能比较

Table 2 Performance comparisons on the function approximation

算法	不同$L_{\max}$所对应的测试性能 (Mean, STD)
算法	50	70	90	110	130	150	170	190
SCN-III	0.0315, 0.0032	0.0329, 0.0033	0.0388, 0.0048	0.0396, 0.0032	0.0459, 0.0043	0.0512, 0.0058	0.0526, 0.0048	0.0557, 0.0095
RSC-II	0.0229, 0.0019	0.0209, 0.0013	0.0209, 0.0008	0.0209, 0.0003	0.0210, 0.0005	0.0211, 0.0004	0.0213, 0.0004	0.0218, 0.0003

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表 3 回归数据集与参数设置

Table 3 Specifications of benchmark problems and some parameter settings

数据集	属性	输出	训练数据	测试数据	${\lambda_{\min}}$	${\Delta\lambda}$
Wine Quality	12	1	3918	980	1	0.1
Concrete	9	1	772	258	1	0.1
Yacht	7	1	227	81	1	1
Airfoil Self-noise	6	1	1352	151	1	0.1

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表 4 对基准数据集的性能对比

Table 4 Performance comparisons on benchmark datasets

数据集	算法	不同$L_{\max}$所对应的测试性能 (Mean, STD)
数据集	算法	50	70	90	110	130	150	170	190
(a)	SCN-III	0.2476, 0.0233	0.2546, 0.0531	0.2543, 0.0492	0.2559, 0.0464	0.2908, 0.0612	0.2997, 0.0841	0.3480, 0.1484	0.3934, 0.1886
(a)	RSC-II	0.2232, 0.0011	0.2229, 0.0014	0.2229, 0.0016	0.2225, 0.0016	0.2231, 0.0017	0.2235, 0.0022	0.2236, 0.0021	0.2238, 0.0021
(b)	SCN-III	0.4017, 0.1060	0.5209, 0.1587	0.7120, 0.2247	0.7739, 0.2010	0.8640, 0.2324	1.3580, 0.7945	1.4025, 0.4526	1.6700, 0.5415
(b)	RSC-II	0.2248, 0.0068	0.2277, 0.0059	0.2295, 0.0094	0.2295, 0.0076	0.2323, 0.0089	0.2341, 0.0090	0.2384, 0.0063	0.2416, 0.0056
(c)	SCN-III	0.3167, 0.2289	0.3528, 0.1436	0.3820, 0.1781	0.6620, 0.2900	0.9062, 0.5600	1.2226, 0.3571	2.7450, 1.1718	4.0242, 1.0377
(c)	RSC-II	0.1308, 0.0209	0.1216, 0.0107	0.1197, 0.0152	0.1168, 0.0111	0.1164, 0.0118	0.1113, 0.0117	0.1056, 0.0112	0.1066, 0.0113
(d)	SCN-III	0.2607, 0.0313	0.3489, 0.1007	0.4332, 0.1219	0.6615, 0.2487	1.2668, 0.4213	1.4295, 0.5883	1.8264, 0.5194	2.0539, 0.9744
(d)	RSC-II	0.2281, 0.0091	0.2378, 0.0147	0.2332, 0.0153	0.2395, 0.0214	0.2443, 0.0279	0.2615, 0.0402	0.2437, 0.0234	0.2848, 0.0512

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表 5 在实际MFSP数据集上的性能对比

Table 5 Performance comparisons on the actual MFSP dataset

算法	不同$L_{\max}$所对应的测试性能 (Mean, STD)
算法	50	110	170	230	270	330	370	410
SCN-III	0.0287, 0.0029	0.0494, 0.0039	0.0623, 0.0069	0.0758, 0.0063	0.0837, 0.0059	0.0983, 0.0107	0.1033, 0.0096	0.1146, 0.0095
RSC-II	0.0269, 0.0021	0.0316, 0.0017	0.0372, 0.0035	0.0411, 0.0046	0.0424, 0.0047	0.0454, 0.0066	0.0460, 0.0054	0.0509, 0.0077

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