突出矿井采掘接替与通风系统的动态模拟及优化
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摘要
针对煤与瓦斯突出矿井中近距离煤层群联合开采的特点，在总结和分析国内外已有研究成果的基础上，运用采矿学、矿井通风学、通风网络图论、流体力学、非线性优化理论，以及计算机科学中的软件开发、计算机图形学等学科相关内容，系统研究矿井采掘工程接替理论与矿井通风系统动态仿真及优化调节理论及技术。论文的主要研究内容和取得的主要成果如下：
⑴统计分析了综采面本煤层百米钻孔瓦斯抽放量与瓦斯预抽期的指数函数关系，确定出合理的瓦斯预抽时间；以相同班产量下综采面相对瓦斯涌出量的平均值作为当前班产量下的统计样本，得出了班产量与绝对瓦斯涌出量的幂函数关系，确定了瓦斯涌出不均衡系数；获得了不同班进尺下掘进工作面累计进尺与绝对瓦斯涌出量的指数函数关系；提出了基于统计法的瓦斯分源预测修正模型。
⑵在矿井采掘接替通用技术规则的基础上，建立了煤与瓦斯突出矿井中近距离煤层群联合开采的采掘工程接替因素指标体系，提出了基于可变模糊理论的采掘工程接替编制方法，设计开发了矿井采掘接替专家系统。
⑶建立了矿井独头巷道掘进与接替的状态模型、采煤工作面推进与接替状态模型、巷道注销模型、采掘推进过程中工作面巷道风阻值的变化模型及井下用风地点需风量变化模型，结合矿井通风系统静态模拟，提出了基于采掘推进与接替的矿井通风系统动态仿真理论，开发了矿井通风动态仿真系统。
⑷建立了以矿井总通风功耗、调节设施装置地点数目及采空区漏风通道压差最小化为目标函数，以余树弦分支风量、可调节分支阻力调节量为决策变量，巷道风速、用风地点需风量、矿井通风总阻力、巷道阻力调节量不超限作为不等式约束条件，以通风网络回路风压平衡方程、节点风量平衡方程及风机特性曲线方程作为等式约束条件的矿井通风网络优化调节非线性优化模型，提出了微分进化与关键路径法联合求解模型的算法，设计了算法的数据结构和程序。
盘江矿区金佳煤矿为煤与瓦斯突出矿井，采用中近距离煤层群联合开采的开采方式，矿井通风系统为多风井分区式。将研究成果在金佳煤矿进行了应用研究，获得了金佳矿采掘工程接替主要影响因素变化规律，编制了金佳矿经济性和安全性最优的采掘工程接替5年计划方案，在此基础上，应用矿井通风系统动态仿真理论，获得了矿井通风系统随采掘接替的近期变化情况，结合控制采空区漏风的通风系统优化调节技术，对提升矿井灾害防治、保障矿井安全生产具有重要的现实意义。
According to the characteristics of the medium-distance seam of combined mining in coaland gas outburst mine, on the basis of the summary and analysis of home and abroad researches,this article systemically analyzes the mining and excavation planning theory, the dynamicsimulation and optimal regulation of mine ventilation system by using the mining science, themine ventilation science, the graphic theory of mine ventilation network, the fluid mechanics,the nonlinear optimization theory, and the software development, computer graphics ofcomputer science. The main research contents and findings are illustrated as follows:
(1) From the statistical analysis results, it is an exponential function between the gasextraction rate of drill hole per hundred meters and gas extraction period, and then thereasonable gas extraction period is determined. Using the average value of gas emission rate ofthe same shift output as a statistical sample of the current shift output in the fully mechanizedworking face, the power function between the shift output and absolute gas emission rate, aswell as nonuniform coefficients of gas emission are obtained. Furthermore, the exponentialfunction between the tunneling length and absolute gas emission is acquired in the heading facewith different tunneling speed. A modified forcasting model of different gas source is putforward based on the statistical method to forcast the gas emission rate in the working face.
(2) On the basis of the general technical rules of the mining and excavation planning, thisarticle establishes the index system of the coal and gas outburst mine with the medium-distanceseam and combined mining technology, and puts forward the mining and excavation planningmethod based on variable fuzzy theory. After that, the expert system of mining and excavationplanning is development.
(3) This article establishes the model of excavating and superseding state of blind heading,the model of advancing and superseding state of working face, the model of canceling the blindheading, the changing model of the resistance value of the working face in the mining andexcavation process, and the changing model of the underground required airflow of chamber.Combining with the static simulation of mine ventilation system, the dynamic simulation theoryof mine ventilation system based on mining and excavation plan is put forward. Furthermore,the dynamic simulation system of mine ventilation system is developed.
(4)This article establishes the nonlinear optimization model of mine ventilation network.The objective function is composed of the smallest air power of main fans, the fewestregulation locations and minimum differential pressure of area air leakage passage of the gob;the variables are mixing of the air quantity of the cotree branches and the regulation resistanceof the regulable branch resistance; the inequality constraint conditions includes the lower and upper bound of the air velocity, the required air quantity of the chamber, the total fan pressure,the regulation air resistance of the roadway; the equality constraint conditions includes theKirchhoff’s current law, Kirchhoff’s voltage law and the characteristic curve of main fans. Then,the combination method of differential evolution algorithm and critical path is proposed in forsolving the mine ventilation optimization model. Furthermore, the data structure andprogramming of the algorithm is developed.
Jinjia coal mine, which is the coal and gas outburst mine in Panjiang mining area, adoptsmedium-distance seam of combined mining method and the parallel ventilation system withmulti-shaft. From the application of research findings in Jinjia coal mine, the change law ofmain influencing factors of mining and excavation plan is obtained, and the optimal economyand security of5years mining and excavation planning is weaved. On this basis, the recentchanging of the mine ventilation system due to the mining and excavation is simulated byapplying the dynamic simulation theory of mine ventilation system theory. Combining with theoptimal regulation technology of mine ventilation system for controlling the gob air leakage,this theory and technology has an important practical significance on enhancing the minedisaster prevention and protecting the mine safety production.
⑴统计分析了综采面本煤层百米钻孔瓦斯抽放量与瓦斯预抽期的指数函数关系，确定出合理的瓦斯预抽时间；以相同班产量下综采面相对瓦斯涌出量的平均值作为当前班产量下的统计样本，得出了班产量与绝对瓦斯涌出量的幂函数关系，确定了瓦斯涌出不均衡系数；获得了不同班进尺下掘进工作面累计进尺与绝对瓦斯涌出量的指数函数关系；提出了基于统计法的瓦斯分源预测修正模型。
⑵在矿井采掘接替通用技术规则的基础上，建立了煤与瓦斯突出矿井中近距离煤层群联合开采的采掘工程接替因素指标体系，提出了基于可变模糊理论的采掘工程接替编制方法，设计开发了矿井采掘接替专家系统。
⑶建立了矿井独头巷道掘进与接替的状态模型、采煤工作面推进与接替状态模型、巷道注销模型、采掘推进过程中工作面巷道风阻值的变化模型及井下用风地点需风量变化模型，结合矿井通风系统静态模拟，提出了基于采掘推进与接替的矿井通风系统动态仿真理论，开发了矿井通风动态仿真系统。
⑷建立了以矿井总通风功耗、调节设施装置地点数目及采空区漏风通道压差最小化为目标函数，以余树弦分支风量、可调节分支阻力调节量为决策变量，巷道风速、用风地点需风量、矿井通风总阻力、巷道阻力调节量不超限作为不等式约束条件，以通风网络回路风压平衡方程、节点风量平衡方程及风机特性曲线方程作为等式约束条件的矿井通风网络优化调节非线性优化模型，提出了微分进化与关键路径法联合求解模型的算法，设计了算法的数据结构和程序。
盘江矿区金佳煤矿为煤与瓦斯突出矿井，采用中近距离煤层群联合开采的开采方式，矿井通风系统为多风井分区式。将研究成果在金佳煤矿进行了应用研究，获得了金佳矿采掘工程接替主要影响因素变化规律，编制了金佳矿经济性和安全性最优的采掘工程接替5年计划方案，在此基础上，应用矿井通风系统动态仿真理论，获得了矿井通风系统随采掘接替的近期变化情况，结合控制采空区漏风的通风系统优化调节技术，对提升矿井灾害防治、保障矿井安全生产具有重要的现实意义。
According to the characteristics of the medium-distance seam of combined mining in coaland gas outburst mine, on the basis of the summary and analysis of home and abroad researches,this article systemically analyzes the mining and excavation planning theory, the dynamicsimulation and optimal regulation of mine ventilation system by using the mining science, themine ventilation science, the graphic theory of mine ventilation network, the fluid mechanics,the nonlinear optimization theory, and the software development, computer graphics ofcomputer science. The main research contents and findings are illustrated as follows:
(1) From the statistical analysis results, it is an exponential function between the gasextraction rate of drill hole per hundred meters and gas extraction period, and then thereasonable gas extraction period is determined. Using the average value of gas emission rate ofthe same shift output as a statistical sample of the current shift output in the fully mechanizedworking face, the power function between the shift output and absolute gas emission rate, aswell as nonuniform coefficients of gas emission are obtained. Furthermore, the exponentialfunction between the tunneling length and absolute gas emission is acquired in the heading facewith different tunneling speed. A modified forcasting model of different gas source is putforward based on the statistical method to forcast the gas emission rate in the working face.
(2) On the basis of the general technical rules of the mining and excavation planning, thisarticle establishes the index system of the coal and gas outburst mine with the medium-distanceseam and combined mining technology, and puts forward the mining and excavation planningmethod based on variable fuzzy theory. After that, the expert system of mining and excavationplanning is development.
(3) This article establishes the model of excavating and superseding state of blind heading,the model of advancing and superseding state of working face, the model of canceling the blindheading, the changing model of the resistance value of the working face in the mining andexcavation process, and the changing model of the underground required airflow of chamber.Combining with the static simulation of mine ventilation system, the dynamic simulation theoryof mine ventilation system based on mining and excavation plan is put forward. Furthermore,the dynamic simulation system of mine ventilation system is developed.
(4)This article establishes the nonlinear optimization model of mine ventilation network.The objective function is composed of the smallest air power of main fans, the fewestregulation locations and minimum differential pressure of area air leakage passage of the gob;the variables are mixing of the air quantity of the cotree branches and the regulation resistanceof the regulable branch resistance; the inequality constraint conditions includes the lower and upper bound of the air velocity, the required air quantity of the chamber, the total fan pressure,the regulation air resistance of the roadway; the equality constraint conditions includes theKirchhoff’s current law, Kirchhoff’s voltage law and the characteristic curve of main fans. Then,the combination method of differential evolution algorithm and critical path is proposed in forsolving the mine ventilation optimization model. Furthermore, the data structure andprogramming of the algorithm is developed.
Jinjia coal mine, which is the coal and gas outburst mine in Panjiang mining area, adoptsmedium-distance seam of combined mining method and the parallel ventilation system withmulti-shaft. From the application of research findings in Jinjia coal mine, the change law ofmain influencing factors of mining and excavation plan is obtained, and the optimal economyand security of5years mining and excavation planning is weaved. On this basis, the recentchanging of the mine ventilation system due to the mining and excavation is simulated byapplying the dynamic simulation theory of mine ventilation system theory. Combining with theoptimal regulation technology of mine ventilation system for controlling the gob air leakage,this theory and technology has an important practical significance on enhancing the minedisaster prevention and protecting the mine safety production.
引文
[1] Astashkin N.V.. Scheduling of mining operations[J]. Journal of Mining Science,1972,7(8):424-429.
[2]朱贵芳.新三维动态规划法确定露天矿最优境界[J].武汉理工大学学报,1984,(1):71-82.
[3]蒋国安,李兴华,刘帮国.矿井采掘计划的最优控制和智能决策支持系统[J].山东科技大学学报,2000,19(4):3-7.
[4]玛伦霍尔茨,张小平,韩可琦.矿井生产技术经营决策模拟系统—SIMBERG[J].中国矿业大学学报,1998,27(2):1-4.
[5]刘涛.基于产销平衡的矿井生产计划编制优化研究[D].辽宁工程技术大学,2004.
[6] Ivanov N.I., Suslov O. P., and Efremov A. V., et al. Optimization of mining work scheduling[J].Cybernetics and Systems Analysis,1971,3(7):317-321.
[7]韩可琦,蔡恒兵.基于群体AHP的采掘计划灰色关联分析评价[J].中国矿业大学学报,1999,(3):208-211.
[8]余忠林,谢广祥.基于神经网络的采掘接替优化研究[J].西安科技学院学报,1999,(4):298-301.
[9]胡清淮.线性规划在矿山配矿问题中的应用[J].系统工程,1983,1(2):57-62.
[10]罗周全.采掘作业计划的线性目标规划模型[J].有色金属(矿山部分),1993,(3):3-8.
[11]韩可琦,胡吉锋,蔡恒兵等.煤矿采掘接替计划计算机仿真系统[J].中国矿业大学学报,2000,29(1):30-33.
[12]孟祥瑞,殷大发,詹林.矿井采掘计划辅助编制系统的开发[J].河南理工大学学报(自然科学版),2005,24(1):25-29.
[13]赵鹏,毛善君,黄群英等.矿井采掘衔接智能决策支持系统设计[J].煤炭科学技术,2007,35(3):51-56.
[14]蒋国安,刘帮国,李兴华.马尔可夫决策规划与采掘计划优化[J].山东科技大学学报(自然科学版),2001,20(1):20-33.
[15]蒋国安.矿井采掘计划的编制与检验[M].北京:煤炭工业出版,1992.7.
[16]刘晓峰.矿井采掘比例关系研究与应用[J].煤炭企业管理,2001,(4):51-52.
[17]蒋国安,李兴华.生产矿井采掘计划的预测检验[J].煤炭学报,1991,(3):29-35.
[18]刘华东,蔡恒兵,韩可琦.利用时间序列分析检验矿井采掘关系[J].江苏煤炭,1998,(4):9-10.
[19]王学明,郭中强.矿井采掘接替决策支持系统的研制[J].焦作工学院学报,1994,(4):87-91.
[20]蒋国安,王新华.用BAYES预测方法编制矿井年度采掘计划[J].煤炭学报,1994,(4):369-377.
[21]李江,陈开岩,李跃文.岩石巷道施工速度的计算机模拟[J].能源技术与管理,2004,(1):82-84.
[22]司俊鸿,刘结高,陈开岩等.综采面瓦斯绝对涌出量主要影响因素分析[J].煤矿安全,2009,(5):96-99.
[23]何利文,施式亮,宋译等.回采工作面瓦斯涌出量的复杂性及其度量[J].煤炭学报,2008,33(5):547-550.
[24] C.O.. gen Karacan, Gerrit V.R. Goodman. A CART technique to adjust production from longwall coaloperations under ventilation constraints[J]. Safety Science,2012,50:510–522.
[25]李树刚,肖鹏,林海飞.乌兰煤矿5343综放工作面瓦斯涌出规律分析.煤矿安全,2011,(1):122-124.
[26]徐涛,郝彬彬,张华.分源预测法在新建矿井瓦斯涌出量预测中的应用[J].煤炭技术,2009(7):104-106.
[27]张铁岗.矿井瓦斯综合治理技术[M].北京:煤炭工业出版社,2001.
[28]蒋中承,陈开岩.综放工作面瓦斯涌出量预测统计模型及应用[J].煤矿安全,2011,(1):82-85.
[29]李宗翔,李舒伶,刘剑.综放面风量调节范围与安全评价的数值模拟分析[J].中国安全科学学报,2003,13(3):4-57.
[30]邓聚龙.灰色控制系统（第2版）[M].武汉:华中理工大学出版社,1985.8.
[31]伍爱友,田云丽,宋译等.灰色系统理论在矿井瓦斯涌出量预测中的应用[J].煤炭学报,2005,(5):589-592.
[32]白福臣,李舒伶.灰色理论在矿井绝对瓦斯涌出量预测中的应用[J].辽宁工程技术大学学报,1991,(3):29-32.
[33]孙忠强,张嘉勇,朱令起等.人工神经网络模型在瓦斯预测中的应用[J].河北理工大学学报,2007,(1):4-7.
[34]周革忠.回采工作面瓦斯涌出量预测的神经网络方法[J].中国安全科学学报,2004,(10):18-21.
[35]郁云,杜杰,陆金桂.基于人工神经网络的瓦斯涌出量预测[J].计算机仿真,2006,(8):146-148.
[36]朱川曲.采煤工作面瓦斯涌出量预测的神经网络模型[J].中国安全科学学报,1999,(2):42-45.
[37]白庆升,范正伟,王为举.灰关联分析与BP神经网络方法在矿井瓦斯涌出量预测中的应用[J].煤,2009(4):21-24.
[38]白庆升,敬彦宁,张博.基于灰色-神经网络组合模型的瓦斯涌出量预测[J].煤炭技术,2009(8):70-72.
[39]胡永梅.灰色系统GM(1,1)模型在煤矿瓦斯涌出量预测中的应用[J].能源与环境,2008(4):45-46.
[40]唐亮,李春生,许虎等.基于BP和RBFNN的神经网络算法在瓦斯预测中的应用及比较[J].中国安全生产科学技术,2007(6):31-34.
[41] Qu Z.M.. Application of grey neural network in analyzing disaster prevention and control in coal minebased on CC and RBF-DDA algorithms[C].2009International Conference on Innovation Management,2009:55-58.
[42]薛鹏骞,吴立锋,李海军.基于小波神经网络的瓦斯涌出量预测研究[J].中国安全科学学报,2006,(2):22-25.
[43]谷松,崔洪庆,冯文丽.基于灰色理论的小波神经网络对瓦斯涌出量的预测[J].煤炭学报,2007(9):964-965.
[44]曾勇,吴财芳.矿井瓦斯涌出量预测的模糊分形神经网络研究[J].煤炭科学技术,2004,(2):62-65.
[45] Liu Y.J., Zhang Y.Q., Zhu Y., et al. A constructive neural network learning method based on quotientspace and its application in coal mine gas prediction[C].2010International Conference on IntelligentComputing and Cognitive Informatics,2010:172-174.
[46] Hong J., Zhao Y.. The study of the gas emission prediction model based on fuzzy-rough set neuralnetwork[C]. Proceedings of the2nd International Conference on Machine Learning and Computing,2010:313-316.
[47] Vapnik V.N.. The statistical learning theory[M].NewYork: Jonn Wiley,1998:121-132.
[48]孙林,杨世元.基于LS-SVM的回采工作面瓦斯涌出量预测[J].煤炭学报,2008(12):1377-1380.
[49]孙林,杨世元.基于最小二乘支持向量机的煤层瓦斯含量预测[J].煤矿安全,2009(2):10-13.
[50] Cao S.G., Liu, Y.B., Wang, Y.P.. A forecasting and forewarning model for methane hazard in workingface of coal mine based on LS-SVM[J]. Journal of China University of Mining and Technology,2008,18(2):172-176.
[51]马文涛.基于WT与GALSSVM的瓦斯涌出量预测[J].采矿与安全工程学报,2009(4):524-528.
[52]戴宏亮.小波支持向量回归在瓦斯涌出量预测中的应用[J].计算机工程与应用,2010,(7):15-17.
[53]李云玮,张涛.基于分形和支持向量机的瓦斯涌出量预测研究[J].中国科技论文在线,2006(3):203-207.
[54]廖建朝，谢正文.灰色支持向量机在瓦斯流量预测中的应用[J].工业安全与环保,2008(8):27-29.
[55]姜谙男,梁冰,张娇.基于粒子群最小二乘支持向量机的瓦斯含量预测[J].辽宁工程技术大学学报,2009(3):363-366.
[56]何利文,施式亮,宋译等.基于支持向量机(SVM)的回采工作面瓦斯涌出混沌预测方法研究[J].中国安全科学学报,2009(9):42-46.
[57]马文涛,黄凌霄.基于相空间重构与GA-LSSVM的瓦斯涌出量预测[J].煤炭工程,2009(6):78-80.
[58]戴宏亮.基于智能支持向量回归的瓦斯涌出量预测[J].计算机工程与应用,2009,(1):242-244.
[59]吴玉国,王俊峰,周春山等.综放工作面瓦斯涌出量与生产能力的关系研究[J].煤炭科学技术,2011,(2):43-45.
[60]刘剑,贾进章,赵千里.金川二矿区矿井通风仿真系统数学模型研究与应用[J].矿业快报,2001,(12):8-19.
[61]王从陆.复杂矿井通风网络解算及参数可调度研究[D].中南大学,2003.
[62]苏清政,刘剑.矿井通风仿真理论与实践[M].北京:煤炭工业出版社,2006.
[63] Hartman H.L., Mutmansky J.M., Wang Y.J..Mine ventilation and air conditioning[M].New York:JohnWiley&Sons,1982:483-516.
[64]王树刚,刘剑,周西华.金川公司二矿区通风系统风流非定常流动规律研究[J].中国安全科学学报,2001,11(6):73-77.
[65]阳昌明.矿井通风网路的风流状态与控制[M].北京:煤炭工业出版社,1982.36-51.
[66] Waclawik J.,Bialas S.,Branny M.,et al.Algorytmy I programy wentylacji I klimatyzacjikopaln[M].Wydawnictwo slask Katowice,(in Polish),1983.
[67]王树刚,王继仁,洪林.矿井正常和灾变时期通风网络解算的数学模型[J].辽宁工程技术大学学报,2003,(4):436-438.
[68] Greuer R.E..Modeling the movement of smoke and the effect of ventilation systems in mine shaftfires[J]. Fire Safety Journal,1985,9(1):81-87.
[69]姬长发,常心坦.机械扰动下风流非稳定流动的数学模型[J]．西安矿业学院学报，1997,(2):13-17.
[70]王省身张国枢.矿井火灾防治[M].徐州:中国矿业大学出版社,1990.
[71]郑丙建,王彦凯,王建林等.调压技术在防治瓦斯中的应用[J].煤矿安全,2001,(7):15-16.
[72]贺艳涛.采用调压室联合抽放采空区瓦斯技术[J].煤矿开采,2005,(3):67-69.
[73] Morley T.A.. The minimization of ventilation costs for controlled flow networks[C]. SME_AIME FallMeeting and Exhibit,1983,10.
[74]李恕和,王义章.矿井通风网络图论[M].北京:煤炭工业出版社,1984.
[75]王惠宾,胡卫民,李湖生.矿井通风网络理论与算法[M].徐州:中国矿业大学出版社，1996.
[76]黄元平,李湖生.矿井通风网络优化调节问题的非线性规划解法[J].煤炭学报,1995,(1):14-20.
[77]卢新明.非线性管道网络中的数学规划问题及解法[J].应用数学学报,1989,(3):281-291.
[78]徐竹云.矿井通风系统优化原理与设计方法[M].北京:冶金工业出版社,1995.
[79]黄翰文,聂义勇.矿井风压和风量调节的数学模型、计算方法和《KTM》程序[J].煤矿安全,1982,(9):15-24.
[80]李湖生.矿井按需分风优化调节的研究与进展[J].煤炭工程师,1997,(1):8-11.
[81]张绍峰.用线性规划法解算矿井通风网络中风量调节的参数[J].煤矿安全,1979,(5):10-16.
[82] Wang Y.J.. Ventilation network theory[M]. Mine Ventilation and Air Conditioning,2nd edit. NewYork:Wiley-Interscience,1982:483-516.
[83] Bhamidpati S.. A critical path crashing technique to optimize multiple fan ventilation systems[C].SME_AIME Annual Meeting,1983,3.
[84]徐瑞龙,施圣荣.矿井通风按需调节的通路法[J].辽宁工程技术大学学报,1984,(3):21-30.
[85]谭国运.通路法在风量调节电算中的应用[J].煤炭工程,1988,(2):8-10.
[86]王明生.按需分风网风量调节的通路公共分支法[J].煤炭工程师,1993,(5):33-36.
[87]希梅尔劳D. M..实用非线性规划[M].北京:北京大学出版社,1981,2.
[88]周济.机械设计优化方法及其应用[M].北京:高等教育出版社,1989.
[89]黄争鸣.一种改进的牛顿法[J].华中科技大学学报,1986,(6):865-870.
[90]张静.无约束非线性规划的共轭梯度法研究综述[J].北京联合大学学报,2008,(2):72-76.
[91] David M H. Applied nonlinear programming [M]. McGraw-Hill,1972.
[92]邓乃扬.无约束最优化计算方法[M].北京:科学出版社,1982.
[93] Powell M.D.. An efficient method for finding minimum of function of several variables withoutcalculating derivatives [J], Comput.,1964,1(7):155-162.
[94]高自友,贺国平,卢新明.非线性规划中的约束变尺度法[J].山东科技大学学报(自然科学版),1993,(3):305-310.
[95]程志毅,余俊,周济.广义简约梯度法程序GRG-2的开发及GRG-C的研制[J].华中科技大学学报,1987,(3):149-154.
[96]陈国良.遗传算法及其应用[M].北京:人民邮电出版社,1996.
[97] Rechenberg I. Cybernetic solution path of an experimental Problem[M]. Roy Airer Establ, libr trans l122Hants. UK:Farnborough,1965.
[98] Schwefel H.P.. Numerical optimization of computer models[M]. Chichester:Wiley,1981.
[99] Fogel L.J., Owens A.J., and Walsh M.J.. Artificial intelligenee through simulated evolution[M].NewYork: John Wiley,1966.
[100] Holland J.H.. Adaptation in natural and artificial systems [M]. Ann Arbor:University of MichiganPress,1975.
[101] DeJong K.A.. An analysis of the behavior of a class at genetic adaptive system[D]. AnnArbor:University of Michigan,1975.
[102] Goldberg D.E.. Genetic algorithms in search[J]. Optimization and Machine Learning. Addison-Wesley,1989.
[103] Grefenstette J.J.. Optimization of control parameters for genetic algorithms[J]. IEEE Trans. Syst. Manand Cybern,1986,16:122-128.
[104] Davis. Hand book of genetic algorithms[M]. Van Nostrand Reinhold,1991.
[105] Kirkpatrick S., Gelatt C.D., and Vecchi M.P.. Optimization by simulated annealing[J]. Science,1983,220(4598):671-680.
[106] erny V.. Thermodynamical approach to the traveling salesman problem: An efficient simulationalgorithm[J]. Journal of Optimization Theory and Applications,1985,45:41-51.
[107] Hoppfifld J.J.. Neural networks and physical systems with emergent collective computationalabilities[J]. Proc. NatL Acad. Sci.1982,79:2554-2558
[108] Price K.V., Storn R.M., and Lampinen J.A.. Differential evolution: A practical approach to globaloptimization[M]. Springer,2004:135-139.
[109] Kaelo P. and Ali M.M.. A numerical study of some modified differential evolution algorithms[J].European Journal of Operational Research,2006,169(3):1176-1184.
[110] Storn R. and Price K.. DE-A simple and efficient adaptive scheme for global optimization overcontinuous space [R], International Computer Science Institute (ICSI), Technical Report TR-95-012,1995.
[111] Storn R. and Price K.. Differential Evolution–A simple and efficient Heuristic for globaloptimization over continuous spaces[J]. Journal of Global Optimization,1997,11(4):341-359.
[112] Back T. and Schwefel H.P.. An overview of evolutionary algorithms for parameter optimization[J].Evolutionary Computation,1993,1(1):1-23.
[113] Lampinen J. and Zelinka I.. Mixed variable non-linear optimization by differential evolution [J/OL].http://www.lut.fi./jlampine.debiblio.htm.
[114] Lampinen J.. A bibliography of differential evolution algorithm[T/OL],Technical Report,Lappeenranta University of Technology, Department of Information Technology, Laboratory ofInformation Processing. http://www.lut.fi/jlampine/debiblio.htm.
[115] Abbass H., Sarker R., and Newton C.. A pareto frontier differential evolution approach formulti-objective optimization problems[C].In Proceedings of the Congress on EvolutionaryComputation, IEEE Service Centre.2001,2:971-978.
[116] Krink T., Filipic B., and Fogel G.B.. Noisy optimization problems-a particular challenge fordifferential evolution[C].In Proceedings of Congress on Evolutionary Computation,Poland,2004.
[117] Ricardo L.,Becerra D., and Carlos A.. A cultural algorithm with differential evolution to solveconstrained optimization problems[J].Springer-Verlag,Lecture Notes in Artificial IntelligenceVol.3315,Puebla,México,November2004.881-890.
[118] Mezura M.. Simple feasibility rules and differential evolution for constrained optimization[C].InProceedings of the Third Mexican International Conference on Artificial Intelligence, SpringerVerlag,Lecture Notes in Artificial Intelligence,2004,2972:707-716.
[119]张晓丽.基于微分进化算法的机器人路径规划方法[D].大连:大连理工大学,2006.
[120]李颖,徐桂芝,饶利云.微分进化算法在头部电阻抗成像中的应用[J].中国生物医学工程学报,2005,24(6):672-675.
[121]谢锦程.基于微分进化算法的智能人工腿的研究与开发[D].中南大学,2005.
[122]牛卫平.基于GIS和微分进化算法的变电站选址及定容[D].华北电力大学，2007.
[123]程功林.瓦斯地质[M].北京:煤炭工业出版社,2009.
[124]苗六县.钻孔预抽本煤层瓦斯合理预抽期评价模型[J].北京工业职业技术学院学报,2006,(3):38-41.
[125]吕保民.煤层瓦斯预抽期评价及预抽效果分析[J].能源技术与管理,2011,(3):16-18.
[126]赵长春,屈庆栋,许家林等.超长综放面瓦斯涌出特征及其影响因素[J].采矿与安全工程学报,2007(3):335-339.
[127]杜志军.采煤工作面生产能力与瓦斯涌出量的关系探讨[J].中国科技信息,2006,(10):87.
[128]谭继兴.高沼气矿的瓦斯涌出及风量计算[J].矿业安全与环保,1986,(3):39-43.
[129]朱川曲,罗键,王卫军.矿井采掘接替模拟模型[J].湘潭矿业学院学报,1995,10(1):9-14.
[130]张国枢,谭允祯,陈开岩等.通风安全学[M].徐州：中国矿业大学出版社,2000.
[2]朱贵芳.新三维动态规划法确定露天矿最优境界[J].武汉理工大学学报,1984,(1):71-82.
[3]蒋国安,李兴华,刘帮国.矿井采掘计划的最优控制和智能决策支持系统[J].山东科技大学学报,2000,19(4):3-7.
[4]玛伦霍尔茨,张小平,韩可琦.矿井生产技术经营决策模拟系统—SIMBERG[J].中国矿业大学学报,1998,27(2):1-4.
[5]刘涛.基于产销平衡的矿井生产计划编制优化研究[D].辽宁工程技术大学,2004.
[6] Ivanov N.I., Suslov O. P., and Efremov A. V., et al. Optimization of mining work scheduling[J].Cybernetics and Systems Analysis,1971,3(7):317-321.
[7]韩可琦,蔡恒兵.基于群体AHP的采掘计划灰色关联分析评价[J].中国矿业大学学报,1999,(3):208-211.
[8]余忠林,谢广祥.基于神经网络的采掘接替优化研究[J].西安科技学院学报,1999,(4):298-301.
[9]胡清淮.线性规划在矿山配矿问题中的应用[J].系统工程,1983,1(2):57-62.
[10]罗周全.采掘作业计划的线性目标规划模型[J].有色金属(矿山部分),1993,(3):3-8.
[11]韩可琦,胡吉锋,蔡恒兵等.煤矿采掘接替计划计算机仿真系统[J].中国矿业大学学报,2000,29(1):30-33.
[12]孟祥瑞,殷大发,詹林.矿井采掘计划辅助编制系统的开发[J].河南理工大学学报(自然科学版),2005,24(1):25-29.
[13]赵鹏,毛善君,黄群英等.矿井采掘衔接智能决策支持系统设计[J].煤炭科学技术,2007,35(3):51-56.
[14]蒋国安,刘帮国,李兴华.马尔可夫决策规划与采掘计划优化[J].山东科技大学学报(自然科学版),2001,20(1):20-33.
[15]蒋国安.矿井采掘计划的编制与检验[M].北京:煤炭工业出版,1992.7.
[16]刘晓峰.矿井采掘比例关系研究与应用[J].煤炭企业管理,2001,(4):51-52.
[17]蒋国安,李兴华.生产矿井采掘计划的预测检验[J].煤炭学报,1991,(3):29-35.
[18]刘华东,蔡恒兵,韩可琦.利用时间序列分析检验矿井采掘关系[J].江苏煤炭,1998,(4):9-10.
[19]王学明,郭中强.矿井采掘接替决策支持系统的研制[J].焦作工学院学报,1994,(4):87-91.
[20]蒋国安,王新华.用BAYES预测方法编制矿井年度采掘计划[J].煤炭学报,1994,(4):369-377.
[21]李江,陈开岩,李跃文.岩石巷道施工速度的计算机模拟[J].能源技术与管理,2004,(1):82-84.
[22]司俊鸿,刘结高,陈开岩等.综采面瓦斯绝对涌出量主要影响因素分析[J].煤矿安全,2009,(5):96-99.
[23]何利文,施式亮,宋译等.回采工作面瓦斯涌出量的复杂性及其度量[J].煤炭学报,2008,33(5):547-550.
[24] C.O.. gen Karacan, Gerrit V.R. Goodman. A CART technique to adjust production from longwall coaloperations under ventilation constraints[J]. Safety Science,2012,50:510–522.
[25]李树刚,肖鹏,林海飞.乌兰煤矿5343综放工作面瓦斯涌出规律分析.煤矿安全,2011,(1):122-124.
[26]徐涛,郝彬彬,张华.分源预测法在新建矿井瓦斯涌出量预测中的应用[J].煤炭技术,2009(7):104-106.
[27]张铁岗.矿井瓦斯综合治理技术[M].北京:煤炭工业出版社,2001.
[28]蒋中承,陈开岩.综放工作面瓦斯涌出量预测统计模型及应用[J].煤矿安全,2011,(1):82-85.
[29]李宗翔,李舒伶,刘剑.综放面风量调节范围与安全评价的数值模拟分析[J].中国安全科学学报,2003,13(3):4-57.
[30]邓聚龙.灰色控制系统（第2版）[M].武汉:华中理工大学出版社,1985.8.
[31]伍爱友,田云丽,宋译等.灰色系统理论在矿井瓦斯涌出量预测中的应用[J].煤炭学报,2005,(5):589-592.
[32]白福臣,李舒伶.灰色理论在矿井绝对瓦斯涌出量预测中的应用[J].辽宁工程技术大学学报,1991,(3):29-32.
[33]孙忠强,张嘉勇,朱令起等.人工神经网络模型在瓦斯预测中的应用[J].河北理工大学学报,2007,(1):4-7.
[34]周革忠.回采工作面瓦斯涌出量预测的神经网络方法[J].中国安全科学学报,2004,(10):18-21.
[35]郁云,杜杰,陆金桂.基于人工神经网络的瓦斯涌出量预测[J].计算机仿真,2006,(8):146-148.
[36]朱川曲.采煤工作面瓦斯涌出量预测的神经网络模型[J].中国安全科学学报,1999,(2):42-45.
[37]白庆升,范正伟,王为举.灰关联分析与BP神经网络方法在矿井瓦斯涌出量预测中的应用[J].煤,2009(4):21-24.
[38]白庆升,敬彦宁,张博.基于灰色-神经网络组合模型的瓦斯涌出量预测[J].煤炭技术,2009(8):70-72.
[39]胡永梅.灰色系统GM(1,1)模型在煤矿瓦斯涌出量预测中的应用[J].能源与环境,2008(4):45-46.
[40]唐亮,李春生,许虎等.基于BP和RBFNN的神经网络算法在瓦斯预测中的应用及比较[J].中国安全生产科学技术,2007(6):31-34.
[41] Qu Z.M.. Application of grey neural network in analyzing disaster prevention and control in coal minebased on CC and RBF-DDA algorithms[C].2009International Conference on Innovation Management,2009:55-58.
[42]薛鹏骞,吴立锋,李海军.基于小波神经网络的瓦斯涌出量预测研究[J].中国安全科学学报,2006,(2):22-25.
[43]谷松,崔洪庆,冯文丽.基于灰色理论的小波神经网络对瓦斯涌出量的预测[J].煤炭学报,2007(9):964-965.
[44]曾勇,吴财芳.矿井瓦斯涌出量预测的模糊分形神经网络研究[J].煤炭科学技术,2004,(2):62-65.
[45] Liu Y.J., Zhang Y.Q., Zhu Y., et al. A constructive neural network learning method based on quotientspace and its application in coal mine gas prediction[C].2010International Conference on IntelligentComputing and Cognitive Informatics,2010:172-174.
[46] Hong J., Zhao Y.. The study of the gas emission prediction model based on fuzzy-rough set neuralnetwork[C]. Proceedings of the2nd International Conference on Machine Learning and Computing,2010:313-316.
[47] Vapnik V.N.. The statistical learning theory[M].NewYork: Jonn Wiley,1998:121-132.
[48]孙林,杨世元.基于LS-SVM的回采工作面瓦斯涌出量预测[J].煤炭学报,2008(12):1377-1380.
[49]孙林,杨世元.基于最小二乘支持向量机的煤层瓦斯含量预测[J].煤矿安全,2009(2):10-13.
[50] Cao S.G., Liu, Y.B., Wang, Y.P.. A forecasting and forewarning model for methane hazard in workingface of coal mine based on LS-SVM[J]. Journal of China University of Mining and Technology,2008,18(2):172-176.
[51]马文涛.基于WT与GALSSVM的瓦斯涌出量预测[J].采矿与安全工程学报,2009(4):524-528.
[52]戴宏亮.小波支持向量回归在瓦斯涌出量预测中的应用[J].计算机工程与应用,2010,(7):15-17.
[53]李云玮,张涛.基于分形和支持向量机的瓦斯涌出量预测研究[J].中国科技论文在线,2006(3):203-207.
[54]廖建朝，谢正文.灰色支持向量机在瓦斯流量预测中的应用[J].工业安全与环保,2008(8):27-29.
[55]姜谙男,梁冰,张娇.基于粒子群最小二乘支持向量机的瓦斯含量预测[J].辽宁工程技术大学学报,2009(3):363-366.
[56]何利文,施式亮,宋译等.基于支持向量机(SVM)的回采工作面瓦斯涌出混沌预测方法研究[J].中国安全科学学报,2009(9):42-46.
[57]马文涛,黄凌霄.基于相空间重构与GA-LSSVM的瓦斯涌出量预测[J].煤炭工程,2009(6):78-80.
[58]戴宏亮.基于智能支持向量回归的瓦斯涌出量预测[J].计算机工程与应用,2009,(1):242-244.
[59]吴玉国,王俊峰,周春山等.综放工作面瓦斯涌出量与生产能力的关系研究[J].煤炭科学技术,2011,(2):43-45.
[60]刘剑,贾进章,赵千里.金川二矿区矿井通风仿真系统数学模型研究与应用[J].矿业快报,2001,(12):8-19.
[61]王从陆.复杂矿井通风网络解算及参数可调度研究[D].中南大学,2003.
[62]苏清政,刘剑.矿井通风仿真理论与实践[M].北京:煤炭工业出版社,2006.
[63] Hartman H.L., Mutmansky J.M., Wang Y.J..Mine ventilation and air conditioning[M].New York:JohnWiley&Sons,1982:483-516.
[64]王树刚,刘剑,周西华.金川公司二矿区通风系统风流非定常流动规律研究[J].中国安全科学学报,2001,11(6):73-77.
[65]阳昌明.矿井通风网路的风流状态与控制[M].北京:煤炭工业出版社,1982.36-51.
[66] Waclawik J.,Bialas S.,Branny M.,et al.Algorytmy I programy wentylacji I klimatyzacjikopaln[M].Wydawnictwo slask Katowice,(in Polish),1983.
[67]王树刚,王继仁,洪林.矿井正常和灾变时期通风网络解算的数学模型[J].辽宁工程技术大学学报,2003,(4):436-438.
[68] Greuer R.E..Modeling the movement of smoke and the effect of ventilation systems in mine shaftfires[J]. Fire Safety Journal,1985,9(1):81-87.
[69]姬长发,常心坦.机械扰动下风流非稳定流动的数学模型[J]．西安矿业学院学报，1997,(2):13-17.
[70]王省身张国枢.矿井火灾防治[M].徐州:中国矿业大学出版社,1990.
[71]郑丙建,王彦凯,王建林等.调压技术在防治瓦斯中的应用[J].煤矿安全,2001,(7):15-16.
[72]贺艳涛.采用调压室联合抽放采空区瓦斯技术[J].煤矿开采,2005,(3):67-69.
[73] Morley T.A.. The minimization of ventilation costs for controlled flow networks[C]. SME_AIME FallMeeting and Exhibit,1983,10.
[74]李恕和,王义章.矿井通风网络图论[M].北京:煤炭工业出版社,1984.
[75]王惠宾,胡卫民,李湖生.矿井通风网络理论与算法[M].徐州:中国矿业大学出版社，1996.
[76]黄元平,李湖生.矿井通风网络优化调节问题的非线性规划解法[J].煤炭学报,1995,(1):14-20.
[77]卢新明.非线性管道网络中的数学规划问题及解法[J].应用数学学报,1989,(3):281-291.
[78]徐竹云.矿井通风系统优化原理与设计方法[M].北京:冶金工业出版社,1995.
[79]黄翰文,聂义勇.矿井风压和风量调节的数学模型、计算方法和《KTM》程序[J].煤矿安全,1982,(9):15-24.
[80]李湖生.矿井按需分风优化调节的研究与进展[J].煤炭工程师,1997,(1):8-11.
[81]张绍峰.用线性规划法解算矿井通风网络中风量调节的参数[J].煤矿安全,1979,(5):10-16.
[82] Wang Y.J.. Ventilation network theory[M]. Mine Ventilation and Air Conditioning,2nd edit. NewYork:Wiley-Interscience,1982:483-516.
[83] Bhamidpati S.. A critical path crashing technique to optimize multiple fan ventilation systems[C].SME_AIME Annual Meeting,1983,3.
[84]徐瑞龙,施圣荣.矿井通风按需调节的通路法[J].辽宁工程技术大学学报,1984,(3):21-30.
[85]谭国运.通路法在风量调节电算中的应用[J].煤炭工程,1988,(2):8-10.
[86]王明生.按需分风网风量调节的通路公共分支法[J].煤炭工程师,1993,(5):33-36.
[87]希梅尔劳D. M..实用非线性规划[M].北京:北京大学出版社,1981,2.
[88]周济.机械设计优化方法及其应用[M].北京:高等教育出版社,1989.
[89]黄争鸣.一种改进的牛顿法[J].华中科技大学学报,1986,(6):865-870.
[90]张静.无约束非线性规划的共轭梯度法研究综述[J].北京联合大学学报,2008,(2):72-76.
[91] David M H. Applied nonlinear programming [M]. McGraw-Hill,1972.
[92]邓乃扬.无约束最优化计算方法[M].北京:科学出版社,1982.
[93] Powell M.D.. An efficient method for finding minimum of function of several variables withoutcalculating derivatives [J], Comput.,1964,1(7):155-162.
[94]高自友,贺国平,卢新明.非线性规划中的约束变尺度法[J].山东科技大学学报(自然科学版),1993,(3):305-310.
[95]程志毅,余俊,周济.广义简约梯度法程序GRG-2的开发及GRG-C的研制[J].华中科技大学学报,1987,(3):149-154.
[96]陈国良.遗传算法及其应用[M].北京:人民邮电出版社,1996.
[97] Rechenberg I. Cybernetic solution path of an experimental Problem[M]. Roy Airer Establ, libr trans l122Hants. UK:Farnborough,1965.
[98] Schwefel H.P.. Numerical optimization of computer models[M]. Chichester:Wiley,1981.
[99] Fogel L.J., Owens A.J., and Walsh M.J.. Artificial intelligenee through simulated evolution[M].NewYork: John Wiley,1966.
[100] Holland J.H.. Adaptation in natural and artificial systems [M]. Ann Arbor:University of MichiganPress,1975.
[101] DeJong K.A.. An analysis of the behavior of a class at genetic adaptive system[D]. AnnArbor:University of Michigan,1975.
[102] Goldberg D.E.. Genetic algorithms in search[J]. Optimization and Machine Learning. Addison-Wesley,1989.
[103] Grefenstette J.J.. Optimization of control parameters for genetic algorithms[J]. IEEE Trans. Syst. Manand Cybern,1986,16:122-128.
[104] Davis. Hand book of genetic algorithms[M]. Van Nostrand Reinhold,1991.
[105] Kirkpatrick S., Gelatt C.D., and Vecchi M.P.. Optimization by simulated annealing[J]. Science,1983,220(4598):671-680.
[106] erny V.. Thermodynamical approach to the traveling salesman problem: An efficient simulationalgorithm[J]. Journal of Optimization Theory and Applications,1985,45:41-51.
[107] Hoppfifld J.J.. Neural networks and physical systems with emergent collective computationalabilities[J]. Proc. NatL Acad. Sci.1982,79:2554-2558
[108] Price K.V., Storn R.M., and Lampinen J.A.. Differential evolution: A practical approach to globaloptimization[M]. Springer,2004:135-139.
[109] Kaelo P. and Ali M.M.. A numerical study of some modified differential evolution algorithms[J].European Journal of Operational Research,2006,169(3):1176-1184.
[110] Storn R. and Price K.. DE-A simple and efficient adaptive scheme for global optimization overcontinuous space [R], International Computer Science Institute (ICSI), Technical Report TR-95-012,1995.
[111] Storn R. and Price K.. Differential Evolution–A simple and efficient Heuristic for globaloptimization over continuous spaces[J]. Journal of Global Optimization,1997,11(4):341-359.
[112] Back T. and Schwefel H.P.. An overview of evolutionary algorithms for parameter optimization[J].Evolutionary Computation,1993,1(1):1-23.
[113] Lampinen J. and Zelinka I.. Mixed variable non-linear optimization by differential evolution [J/OL].http://www.lut.fi./jlampine.debiblio.htm.
[114] Lampinen J.. A bibliography of differential evolution algorithm[T/OL],Technical Report,Lappeenranta University of Technology, Department of Information Technology, Laboratory ofInformation Processing. http://www.lut.fi/jlampine/debiblio.htm.
[115] Abbass H., Sarker R., and Newton C.. A pareto frontier differential evolution approach formulti-objective optimization problems[C].In Proceedings of the Congress on EvolutionaryComputation, IEEE Service Centre.2001,2:971-978.
[116] Krink T., Filipic B., and Fogel G.B.. Noisy optimization problems-a particular challenge fordifferential evolution[C].In Proceedings of Congress on Evolutionary Computation,Poland,2004.
[117] Ricardo L.,Becerra D., and Carlos A.. A cultural algorithm with differential evolution to solveconstrained optimization problems[J].Springer-Verlag,Lecture Notes in Artificial IntelligenceVol.3315,Puebla,México,November2004.881-890.
[118] Mezura M.. Simple feasibility rules and differential evolution for constrained optimization[C].InProceedings of the Third Mexican International Conference on Artificial Intelligence, SpringerVerlag,Lecture Notes in Artificial Intelligence,2004,2972:707-716.
[119]张晓丽.基于微分进化算法的机器人路径规划方法[D].大连:大连理工大学,2006.
[120]李颖,徐桂芝,饶利云.微分进化算法在头部电阻抗成像中的应用[J].中国生物医学工程学报,2005,24(6):672-675.
[121]谢锦程.基于微分进化算法的智能人工腿的研究与开发[D].中南大学,2005.
[122]牛卫平.基于GIS和微分进化算法的变电站选址及定容[D].华北电力大学，2007.
[123]程功林.瓦斯地质[M].北京:煤炭工业出版社,2009.
[124]苗六县.钻孔预抽本煤层瓦斯合理预抽期评价模型[J].北京工业职业技术学院学报,2006,(3):38-41.
[125]吕保民.煤层瓦斯预抽期评价及预抽效果分析[J].能源技术与管理,2011,(3):16-18.
[126]赵长春,屈庆栋,许家林等.超长综放面瓦斯涌出特征及其影响因素[J].采矿与安全工程学报,2007(3):335-339.
[127]杜志军.采煤工作面生产能力与瓦斯涌出量的关系探讨[J].中国科技信息,2006,(10):87.
[128]谭继兴.高沼气矿的瓦斯涌出及风量计算[J].矿业安全与环保,1986,(3):39-43.
[129]朱川曲,罗键,王卫军.矿井采掘接替模拟模型[J].湘潭矿业学院学报,1995,10(1):9-14.
[130]张国枢,谭允祯,陈开岩等.通风安全学[M].徐州：中国矿业大学出版社,2000.