基于MPSO-RBF预测控制的瓦斯监控系统研究
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摘要
煤矿瓦斯突出是制约煤矿安全生产的主要因素,采掘工作面是瓦斯突出的多发地点,一旦发生将造成重大经济损失和人员伤亡。煤矿监控系统是保障煤矿安全生产的重要手段,而局部通风机和井下移动抽排泵是监控系统的主要监控设备。然而其现有的控制技术落后,很难满足采掘工作面连续通风和瓦斯安全排放的要求,因此,研究其控制技术具有重要的实际意义。
本文结合瓦斯监控系统的特点,基于非线性模型预测控制的基本框架,首次提出了基于RBF预测模型和粒子群优化的煤矿瓦斯监控系统结构和算法。首先对该算法进行仿真研究,说明了RBF网络模型的泛化能力和粒子群优化的寻优速度和能力,并进行了可行性分析。最后将其应用于煤矿瓦斯监控系统,仿真结果显示此方法为降低瓦斯突出危险性提供了一个崭新的有效途径,结束了以往传统的控制瓦斯排放方式,减小了掘进工作面瓦斯事故发生率,具有较高的实用价值和广阔的应用前景。
Gas outburst is considered as the main restrictive factor in coal mine production safety. In viewing of the fact that accident frequently happens in heading face, which could cause damage economic losses and casualties, the supervision system is served as an important measurement in guaranteeing the safety of mine production. Local fans and moving pump underground are the primary monitoring equipment in supervision system. However, the existing control technology is poor and hard to meet the requirements of heading face continuous ventilation and gas safety emission. Therefore, control technology studying has important practical significance.
With consideration of the characteristics of the gas monitoring system and the basic framework based on nonlinear model predictive control, a new architecture and algorithm of the coal mine gas monitoring system is proposed in this paper. Prediction model based RBF and particle swarm optimization are adopted firstly in this theory. In order to demonstrate the generalization ability of RBF network model and the speed and capability optimization searching for particle swarm optimizing, a feasibility analysis is conducted and emulation test is processed. Thus, the verified theory is applied to coal mine gas monitoring system. Simulation results show that this method provides a new effective way to reduce the risk of gas outburst and puts an end to the traditional control of gas emissions from the previous way of reducing the accident rate of gas heading face. This method has a high practical value and broad application prospects in the coal mine gas monitoring system.
本文结合瓦斯监控系统的特点,基于非线性模型预测控制的基本框架,首次提出了基于RBF预测模型和粒子群优化的煤矿瓦斯监控系统结构和算法。首先对该算法进行仿真研究,说明了RBF网络模型的泛化能力和粒子群优化的寻优速度和能力,并进行了可行性分析。最后将其应用于煤矿瓦斯监控系统,仿真结果显示此方法为降低瓦斯突出危险性提供了一个崭新的有效途径,结束了以往传统的控制瓦斯排放方式,减小了掘进工作面瓦斯事故发生率,具有较高的实用价值和广阔的应用前景。
Gas outburst is considered as the main restrictive factor in coal mine production safety. In viewing of the fact that accident frequently happens in heading face, which could cause damage economic losses and casualties, the supervision system is served as an important measurement in guaranteeing the safety of mine production. Local fans and moving pump underground are the primary monitoring equipment in supervision system. However, the existing control technology is poor and hard to meet the requirements of heading face continuous ventilation and gas safety emission. Therefore, control technology studying has important practical significance.
With consideration of the characteristics of the gas monitoring system and the basic framework based on nonlinear model predictive control, a new architecture and algorithm of the coal mine gas monitoring system is proposed in this paper. Prediction model based RBF and particle swarm optimization are adopted firstly in this theory. In order to demonstrate the generalization ability of RBF network model and the speed and capability optimization searching for particle swarm optimizing, a feasibility analysis is conducted and emulation test is processed. Thus, the verified theory is applied to coal mine gas monitoring system. Simulation results show that this method provides a new effective way to reduce the risk of gas outburst and puts an end to the traditional control of gas emissions from the previous way of reducing the accident rate of gas heading face. This method has a high practical value and broad application prospects in the coal mine gas monitoring system.
引文
[1]国务院《关于预防煤矿生产安全事故的特别规定》(国务院令446号)
[2] X. Star Wu, Ertugrul Topuz. Analysis of Mine Ventilation Systems Using Operations Research Methods. Intrans Oplres. 1998.5: 245-254
[3] Sleziak G. An approach to safety in developing new mining technolog. Queensland Government Mining Journal. 1992.193:28-34
[4]尚三军.基于忧控制的单巷掘进多局部通风机集成控制系统的研究[D].太原理工大学硕士学位论文,2007.5
[5]姜媛媛.煤矿瓦斯移动抽排系统的智能控制与优化运行[D].安徽理工大学硕士学位论文,2006
[6]黄凯峰.基于在线插值模糊控制的煤矿瓦斯抽排系统[D].安徽理工大学硕士学位论文,2008.5
[7]付华,王雨虹.基于数据挖掘的瓦斯灾害信息融合模型的研究[J].传感器与微系统.2008,(01):52-54
[8]付华.多传感器信息融合技术在矿井瓦斯预测控制中的应用.中国科协年会.2003年
[9]付华,刘亚宁.基于ARM和数据融合技术的网络化矿井安全监测系统[J].计算机系统应用.2007(05):60-63
[10]付华,李博,薛永存.基于D-S证据理论的数据融合井下监测方法分析[J].传感器与微系统.2007(01):27-29
[11] Rechalet J, et al.Model Predictive Heuristic Control. Applications to Industrial Processes. Automatica, 1978, 17(5): 413-428
[12]王汝琳.矿井瓦斯传感器的近代研究方法及方向[J].煤矿自动化.1998(4):16-18.
[13]况世华,何丽华.矿用通风机发展概述[J].云南冶金,2008,37(6):3-7
[14]中国电工技术学会.风机水泵交流调速节能技术[M].北京:机械工业出版社,1990.
[15]张国枢.通风安全学[M].徐州:中国矿业大学出版社,2000.
[16]戴良军.基于模糊控制的局部通风机瓦斯智能排放研究[D].西安科技大学硕士学位论文,2008.4
[17]林玉娥.粒子群优化算法的改进及其在管道保温优化设计中的应用[D].大庆石油学院硕士学位论文,2006.3
[18] Cutler C R, Ramaker B L. Dynamic Matrix Control—A Computer Control Algorithm. Proc JACC Sam Francisco, 1980
[19] Rouhani R, Mehra R K. Model Algorithmic Control Basic Theoretical Properties. Automatica, 1982, 18(4):401-414
[20] Clarke D W, et al. Generalized Predictive Control—Part 1 and Part 2 Basic Algorithm. Automatica, 1987, 23(2): 137-148, 149-162
[21] Lelic M A, Zarrop M B. Generalized Pole Placement Self-tuning Controller. Int J Control, 1987, 46(2): 547-568
[22]席裕庚.预测控制[M].国防工业出版社,1993.
[23]魏东.非线性系统神经网络参数预测及控制[M].北京:机械工业出版社,2007:38-44
[24] H.-P. Schwefel: Numerical Optimization of Computer Models by Means of the Evolutionary Strategy, Interdisciplinary Systems Research, Vol.26, 1977
[25]刘小河,李婕,李芳等.基于神经网络的自适应控制研究进展[J].西安理工大学学报,2001,17(4):351-355
[26] Powell M J D.Radial basis functions for multivariable interpolations: a review[J]. In IMA Conference on Algrorithms for the Approximation of Functions and Data. RMCS, Shrivenham UK, 1985, 143-167
[27] Broomhead D S,Lowe D.Multivariable functional interpolation and adaptive networks [J]. Complex Systems.1988,2(2):321-355
[28] Moody J, Darken C.Learning with localized receptive fields[C]. In Sejnowski T, Touretzky D, Hinton G, editors, Connectionist Models Summer School, Carnegie Mellon University, 1988
[29] Holcomb T,et al.Local Training for Radial Basis Function Networks:Towords Solving the Hidden Vnit Problem. Proc ACC,1991,2231-2235
[30] Powell M J D.Radial Basis Function Approscimations to Polynomials.Proc of the 12th Biennial Numerial Analysis Conference,Dundee.1987,223-241
[31] Poggio T, Girosi F. Networks for approximation and leaning[J]. Proceedings of IEEE, 1990, 78(9): 1481-1497
[32]李天军. RBF神经网络及其在锅炉过热汽温控制中的应用[D].哈尔滨工业大学硕士论文,2007.07
[33]飞思科技产品研发中心.神经网络理论与MATLAB7实现[M].北京:电子工业出版社,2005.3:116-127
[34]尹建川.径向基函数神经网络及其在船舶运动控制中的应用研究[D].大连海事大学博士学位论文,2007.09
[35] M.Braae,D.A.Rutherford.Selection of Parameters for a Fuzzy Logic Controller[J].Fuzzy Sets and Systems,1999,22:185-199
[36] I.Rechenberg: Evolutionary Strategy: Optimization of Technical Systems According to the Principles of Biological Evolution, Frommann-Holzboog,1973
[37] L.J.Fogel, A.J.Owens, and M.J.Walsh: Artificial Intelligence Through Simulated Evolution, John Wiley,1966
[38] D.B.Fogel: Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, IEEE Press,1995
[39] J.H.Holland: Adaptation in Natural and Artificial Systems, MIT Press,1992
[40] D.E.Goldberg: Genetic Algorithms in Search, Optimization and Machine Leaning, Addison-Wesley, 1989
[41]潘正君,康立山,陈毓屏:演化计算,清华大学出版社,广西科学技术出版社,1998.7
[42] Kennedy J.and Eberhart R.C.,Particle swarm optimization[J],Proceedings of IEEE International Conference on Neural Networks,1995;1942-1948.
[43] Eberhart R.C.,Shi Y.,Particle swarm optimization developments,applications and resources[J], IEEE, 2001:81-86.
[44]张丽平.粒子群优化算法的理论及实践[D].浙江大学博士学位论文,2005.01
[45]王凌.智能优化算法及其应用[M].北京:清华大学出版社,施普林格出版社,2001.
[46]Shi Y. and Ebehtart R.C. A modified Particle swarm optimizer. Proceedings of IEEE International Conference on Evolutionary Computation(CEC 1998), Piscataway, NJ, 1998, 69-73.
[47] Clerc M.The swarm and the queen: towards a deterministic and adaptive particle swarm optimizition[C]. Proceedings of the 1999 congress on Evolutionary Computation. Washingdon, USA: [s.n.],1999: 1951-1957.
[48]金科,黄建功.《煤矿安全规程》及相关法规[M].北京:中国言实出版社,2005.
[49]俞启香.煤层发生煤和瓦斯突出瓦斯压力最小值的研究[M].煤炭科学研究总院重庆分院编.煤与瓦斯突出预测资料汇编.1987
[50]李涛.基于SVM和PSO的非线性模型预测控制及应用研究[D].上海交通大学硕士论文,2008.01
[51]肖本贤,王晓伟.基于改进PSO算法的过热汽温神经网络预测控制[J].控制理论与应用.2008,25(3),569-573
[52]刘金琨.智能控制[M].北京:电子工业出版社,2005.
[53]夏晓华,刘波,栾志业,金以慧.基于APSO的非线性预测控制及在pH中和反应中的应用[J].化工自动化及仪表. 2006,33 (1):24-27.
[54]王凯,俞启香.煤与瓦斯突出的非线性特征及预测模型[M].徐州:中国矿业大学出版社,2004.12:46-50
[55]李国勇.智能控制及其MATLAB实现[M].北京:电子工业出版社,2005
[56]王正林,王胜开,陈国顺等.MATALAB/Simulink与控制系统仿真[M].北京:电子工业出版社,2005 .
[2] X. Star Wu, Ertugrul Topuz. Analysis of Mine Ventilation Systems Using Operations Research Methods. Intrans Oplres. 1998.5: 245-254
[3] Sleziak G. An approach to safety in developing new mining technolog. Queensland Government Mining Journal. 1992.193:28-34
[4]尚三军.基于忧控制的单巷掘进多局部通风机集成控制系统的研究[D].太原理工大学硕士学位论文,2007.5
[5]姜媛媛.煤矿瓦斯移动抽排系统的智能控制与优化运行[D].安徽理工大学硕士学位论文,2006
[6]黄凯峰.基于在线插值模糊控制的煤矿瓦斯抽排系统[D].安徽理工大学硕士学位论文,2008.5
[7]付华,王雨虹.基于数据挖掘的瓦斯灾害信息融合模型的研究[J].传感器与微系统.2008,(01):52-54
[8]付华.多传感器信息融合技术在矿井瓦斯预测控制中的应用.中国科协年会.2003年
[9]付华,刘亚宁.基于ARM和数据融合技术的网络化矿井安全监测系统[J].计算机系统应用.2007(05):60-63
[10]付华,李博,薛永存.基于D-S证据理论的数据融合井下监测方法分析[J].传感器与微系统.2007(01):27-29
[11] Rechalet J, et al.Model Predictive Heuristic Control. Applications to Industrial Processes. Automatica, 1978, 17(5): 413-428
[12]王汝琳.矿井瓦斯传感器的近代研究方法及方向[J].煤矿自动化.1998(4):16-18.
[13]况世华,何丽华.矿用通风机发展概述[J].云南冶金,2008,37(6):3-7
[14]中国电工技术学会.风机水泵交流调速节能技术[M].北京:机械工业出版社,1990.
[15]张国枢.通风安全学[M].徐州:中国矿业大学出版社,2000.
[16]戴良军.基于模糊控制的局部通风机瓦斯智能排放研究[D].西安科技大学硕士学位论文,2008.4
[17]林玉娥.粒子群优化算法的改进及其在管道保温优化设计中的应用[D].大庆石油学院硕士学位论文,2006.3
[18] Cutler C R, Ramaker B L. Dynamic Matrix Control—A Computer Control Algorithm. Proc JACC Sam Francisco, 1980
[19] Rouhani R, Mehra R K. Model Algorithmic Control Basic Theoretical Properties. Automatica, 1982, 18(4):401-414
[20] Clarke D W, et al. Generalized Predictive Control—Part 1 and Part 2 Basic Algorithm. Automatica, 1987, 23(2): 137-148, 149-162
[21] Lelic M A, Zarrop M B. Generalized Pole Placement Self-tuning Controller. Int J Control, 1987, 46(2): 547-568
[22]席裕庚.预测控制[M].国防工业出版社,1993.
[23]魏东.非线性系统神经网络参数预测及控制[M].北京:机械工业出版社,2007:38-44
[24] H.-P. Schwefel: Numerical Optimization of Computer Models by Means of the Evolutionary Strategy, Interdisciplinary Systems Research, Vol.26, 1977
[25]刘小河,李婕,李芳等.基于神经网络的自适应控制研究进展[J].西安理工大学学报,2001,17(4):351-355
[26] Powell M J D.Radial basis functions for multivariable interpolations: a review[J]. In IMA Conference on Algrorithms for the Approximation of Functions and Data. RMCS, Shrivenham UK, 1985, 143-167
[27] Broomhead D S,Lowe D.Multivariable functional interpolation and adaptive networks [J]. Complex Systems.1988,2(2):321-355
[28] Moody J, Darken C.Learning with localized receptive fields[C]. In Sejnowski T, Touretzky D, Hinton G, editors, Connectionist Models Summer School, Carnegie Mellon University, 1988
[29] Holcomb T,et al.Local Training for Radial Basis Function Networks:Towords Solving the Hidden Vnit Problem. Proc ACC,1991,2231-2235
[30] Powell M J D.Radial Basis Function Approscimations to Polynomials.Proc of the 12th Biennial Numerial Analysis Conference,Dundee.1987,223-241
[31] Poggio T, Girosi F. Networks for approximation and leaning[J]. Proceedings of IEEE, 1990, 78(9): 1481-1497
[32]李天军. RBF神经网络及其在锅炉过热汽温控制中的应用[D].哈尔滨工业大学硕士论文,2007.07
[33]飞思科技产品研发中心.神经网络理论与MATLAB7实现[M].北京:电子工业出版社,2005.3:116-127
[34]尹建川.径向基函数神经网络及其在船舶运动控制中的应用研究[D].大连海事大学博士学位论文,2007.09
[35] M.Braae,D.A.Rutherford.Selection of Parameters for a Fuzzy Logic Controller[J].Fuzzy Sets and Systems,1999,22:185-199
[36] I.Rechenberg: Evolutionary Strategy: Optimization of Technical Systems According to the Principles of Biological Evolution, Frommann-Holzboog,1973
[37] L.J.Fogel, A.J.Owens, and M.J.Walsh: Artificial Intelligence Through Simulated Evolution, John Wiley,1966
[38] D.B.Fogel: Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, IEEE Press,1995
[39] J.H.Holland: Adaptation in Natural and Artificial Systems, MIT Press,1992
[40] D.E.Goldberg: Genetic Algorithms in Search, Optimization and Machine Leaning, Addison-Wesley, 1989
[41]潘正君,康立山,陈毓屏:演化计算,清华大学出版社,广西科学技术出版社,1998.7
[42] Kennedy J.and Eberhart R.C.,Particle swarm optimization[J],Proceedings of IEEE International Conference on Neural Networks,1995;1942-1948.
[43] Eberhart R.C.,Shi Y.,Particle swarm optimization developments,applications and resources[J], IEEE, 2001:81-86.
[44]张丽平.粒子群优化算法的理论及实践[D].浙江大学博士学位论文,2005.01
[45]王凌.智能优化算法及其应用[M].北京:清华大学出版社,施普林格出版社,2001.
[46]Shi Y. and Ebehtart R.C. A modified Particle swarm optimizer. Proceedings of IEEE International Conference on Evolutionary Computation(CEC 1998), Piscataway, NJ, 1998, 69-73.
[47] Clerc M.The swarm and the queen: towards a deterministic and adaptive particle swarm optimizition[C]. Proceedings of the 1999 congress on Evolutionary Computation. Washingdon, USA: [s.n.],1999: 1951-1957.
[48]金科,黄建功.《煤矿安全规程》及相关法规[M].北京:中国言实出版社,2005.
[49]俞启香.煤层发生煤和瓦斯突出瓦斯压力最小值的研究[M].煤炭科学研究总院重庆分院编.煤与瓦斯突出预测资料汇编.1987
[50]李涛.基于SVM和PSO的非线性模型预测控制及应用研究[D].上海交通大学硕士论文,2008.01
[51]肖本贤,王晓伟.基于改进PSO算法的过热汽温神经网络预测控制[J].控制理论与应用.2008,25(3),569-573
[52]刘金琨.智能控制[M].北京:电子工业出版社,2005.
[53]夏晓华,刘波,栾志业,金以慧.基于APSO的非线性预测控制及在pH中和反应中的应用[J].化工自动化及仪表. 2006,33 (1):24-27.
[54]王凯,俞启香.煤与瓦斯突出的非线性特征及预测模型[M].徐州:中国矿业大学出版社,2004.12:46-50
[55]李国勇.智能控制及其MATLAB实现[M].北京:电子工业出版社,2005
[56]王正林,王胜开,陈国顺等.MATALAB/Simulink与控制系统仿真[M].北京:电子工业出版社,2005 .