薄煤层煤岩刨削比能耗优化模型
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摘要
合理选择刨削工艺参数可有效节省薄煤层综采过程中的能量损耗,是实现煤岩绿色、可持续开采的关键。鉴于此,对刨煤机刨削工艺参数优化进行了研究,综合考虑刨头刨削煤岩、刨刀刨削煤岩温升、机组沿煤岩采面移动、刨落煤岩推移与运输、辅助系统损耗等过程,构建了刨煤机能量损耗模型;建立了以煤岩刨削比能耗最小为目标的煤岩刨削工艺参数优化模型;基于自适应自然选择粒子群算法完成了优化模型寻优求解;结合薄煤层井下开采实例验证了所建立比能耗优化模型有效性;比较分析了刨削速度、截深、刀间距3个优化变量对刨削比能耗的影响规律。结果表明,自适应自然选择粒子群算法具有较好的收敛速度与求解精度,所建模型可为煤岩刨削工艺参数设计提供理论依据;该方法能够有效降低薄煤层综采过程能量损耗,对工程应用具有很好的指导意义。
Correct selection of plowing parameters is an effective method to reduce energy consumption for thin coal seam mining process,and it is the key to achieve green and sustainable coal mining.For this reason,the plowing process parameters optimization of plough was researched.An energy consumption model of plough was proposed by considering the plowing processes for plow body force of coal cutting,plough bit temperature rising of coal cutting,plough unit moving along with coal seam mining face,plowed coal particles moving and transport and the auxiliary systems consumption.The process parameters optimization model with minimum specific energy consumption objective of thin coal seam plowing was established,and the adaptive natural selection particle swarm optimization method was applied to solve the optimal solution.A thin coal seam mining experiment case was performed to verify the effectiveness of the specific energy consumption optimization model,and the influence rules of three optimize variables which were cutting speed,cutting depth,bits interval on energy consumption were compared and analyzed.The results showed that the adaptive natural selection particle swarm optimization algorithm had good convergence speed and solving accuracy.The proposed optimization model not only could provide some theoretical basis for plowing process parameters design,but also reduce energy consumption for the thin coal seam mining process effectively,which provided a good guiding significance for engineering application.
Correct selection of plowing parameters is an effective method to reduce energy consumption for thin coal seam mining process,and it is the key to achieve green and sustainable coal mining.For this reason,the plowing process parameters optimization of plough was researched.An energy consumption model of plough was proposed by considering the plowing processes for plow body force of coal cutting,plough bit temperature rising of coal cutting,plough unit moving along with coal seam mining face,plowed coal particles moving and transport and the auxiliary systems consumption.The process parameters optimization model with minimum specific energy consumption objective of thin coal seam plowing was established,and the adaptive natural selection particle swarm optimization method was applied to solve the optimal solution.A thin coal seam mining experiment case was performed to verify the effectiveness of the specific energy consumption optimization model,and the influence rules of three optimize variables which were cutting speed,cutting depth,bits interval on energy consumption were compared and analyzed.The results showed that the adaptive natural selection particle swarm optimization algorithm had good convergence speed and solving accuracy.The proposed optimization model not only could provide some theoretical basis for plowing process parameters design,but also reduce energy consumption for the thin coal seam mining process effectively,which provided a good guiding significance for engineering application.
引文
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[5]TAN Qing,YI Nianen,XIA Yimin,et al.Research on rock dynamic fragmentation characteristics by TBM cutters and cutter spacing optimization[J].Chinese Journal of Rock Mechanics and Engineering,2012,31(12):2453-2464(in Chinese).[谭青,易念恩,夏毅敏,等.TBM滚刀破岩动态特性与最优刀间距研究[J].岩石力学与工程学报,2012,31(12):2453-2464.]
[6]KANG Xiaomin,LI Guixuan,HAO Zhiyong.Optimization for cutting depth with minimum unit energy[J].Coal Mine Machinery,2004,25(9):42-43(in Chinese).[康晓敏,李贵轩,郝志勇.以极小化单位能耗为目标优化刨削深度[J].煤矿机械,2004,25(9):42-43.]
[7]ZHANG Qiang,FU Yunfei,SONG Qiushuang,et al.Parameter optimization of lowest energy consumption for plow based on artificial fish school algorithm[J].Journal of Guangxi University:Natural Science Edition,2012,37(2):241-246(in Chinese).[张强,付云飞,宋秋爽,等.基于人工鱼群算法刨煤机比能耗最低参数优化[J].广西大学学报:自然科学版,2012,37(2):241-246.]
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[9]MAO Jun,YANG Xingwei,PAN Dewen,et al.Muti-objection optimal design for plough based on improved particle swarm optimization algorithm[J].Machine Design and Research,2016,32(1):168-170(in Chinese).[毛君,杨辛未,潘德文,等.基于改进粒子群算法的刨煤机多目标优化设计[J].机械设计与研究,2016,32(1):168-170.]
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[12]HAO Zhiyong.Research on mechanical characteristics of cutting coal and optimization design for coal plow bits[M].Liaoning:Liaoning Science and Technology Publishing House,2015(in Chinese).[郝志勇.刨煤机刨刀刨削煤岩力学特性研究及其优化设计[M].辽宁:辽宁科学技术出版社,2015.]
[13]TAN Qing,LYU Dan,XIA Yimin,et al.Thermo-mechanical coupling analysis of shield cutter head under mud cake condition[J].Journal of Chongqing University,2013,36(10):61-66(in Chinese).[谭青,吕丹,夏毅敏,等.泥饼工况下盾构刀盘热-力耦合分析[J].重庆大学学报,2013,36(10):61-66.]
[14]CHE Demeng,GUO Ping,EHMANN Kornel.Issues in polycrystalline diamond compact cutter-rock interaction from a metal machining point of view-part I:temperature,stresses,and forces[J].Journal of Manufacturing Science and Engineering,2012,134(6):114-122.
[15]GLOWKA D.A.The thermal response of rock to friction in the drag cutting process[J].Journal of Structural Geology,1989,11(7):919-931.
[16]QIU Kun,WANG Xinyong,PANG Siqin.Cast nickel-base superalloy K24study of cutting temperature[J].Journal of Functional Materials,2012,43(6):692-695(in Chinese).[邱坤,王新永,庞思勤.镍基铸造高温合金K24的切削温度实验研究[J].功能材料,2012,43(6):692-695.]
[17]Standardization Administration.GB-T 12718-2009 Mine high-intensity ring chain[S]Beijing:Standards Press of China,2009(in Chinese).[中国国家标准化管理委员会.GB-T12718-2009矿用高强度圆环链[S]北京:中国标准出版社,2009.]
[18]ПОЗИНЕЗ,МЕЛАМЕДВЗ,ТОНВВ.Coal breaking theory of shearer[M].Beijing:China Coal Industry Publishing House,1992(in Chinese).[E.З.保晋,B.З.乜拉麦德,B.B.顿.采煤机破煤理论[M].北京:煤炭工业出版社,1992.]
[19]CHENG Hang,ZHANG Shuiming,QUAN Long.Optimization method for wind turbine blade based on dominanted-constraint hybrid particle swarm[J].Journal of Mechanical Engineering,2015,51(1):176-181(in Chinese).[程珩,张水明,权龙.基于约束主导混合粒子群算法的风力机叶片优化方法研究[J],机械工程学报,2015,51(1):176-181.]
[20]KARANKI S B,MISHRA M K,KUMAR B K.Particle swarm optimization based feedback controller for unified power quality conditioner[J].IEEE Transactions on Power Delivery,2010,25(4):2814-2824.
[21]WU Yongming,DAI Longzhou,LI Shaobo,et al.Mixed assembly line evolution balancing based on improved particle swarm algorithm[J].Computer Integrated Manufacturing Systems,2017,23(4):781-790(in Chinese).[吴永明,戴隆州,李少波,等.基于改进粒子群优化算法的混流装配线演进平衡[J].计算机集成制造系统,2017,23(4):781-790.]
[22]WANG Yahui,YU Suihuai.Car styling design decision on multi-objective particle swarm optimization[J].Computer Integrated Manufacturing Systems,2017,23(4):681-688(in Chinese).[王亚辉,余隋怀.基于多目标粒子群优化算法的汽车造型设计决策模型[J].计算机集成制造系统,2017,23(4):681-688.]
[23]GUO Chenguang,HAN Xue,LI Yuan,et al.Thermal error modeling for spindle system of precision CNC lathe[J].Optics and Precision Engineering,2013,24(7):1731-1742(in Chinese).[郭辰光,韩雪,李源,等.精密数控车床主轴热误差建模[J].光学精密工程,2013,24(7):1731-1742.]
[24]LOPEZ G P,ONIEVA E,OSABA E,et al.A hybrid method for short-term traffic congestion forecasting using genetic algorithms and cross entropy[J].IEEE Transactions on Intelligent Transportation Systems,2016,17(2):557-569.
[25]DIABY A L,MIKLAVCIC S J,ADDAI M J.Optimization of scheduled cleaning of fouled heat exchanger network under aging using genetic algorithm[J].Chemical Engineering Research&Design:Transactions of the Institution of Chemical Engineers Part A,2016,113(9):223-240.
[26]LI Jing,GAO Zhenghong,HUANG Jiangtao,et al.Airfoil optimization based on distributed particle swarm algorithm[J].Acta Aero Dynamica Sinica,2011,29(4):464-469(in Chinese).[李静,高正红,黄江涛,等.基于分布式粒子群算法的翼型优化设计[J].空气动力学学报,2011,29(4):464-469.]
[27]LIU Jianhua,LIU Guomai,YANG Ronghua,et al.Analysis of interactivity and randomness in particle swarm optimization[J].Acta Automatica Sinica,2012,38(9):1471-1481(in Chinese).[刘建华,刘国买,杨荣华,等.粒子群算法的交互性与随机性分析[J].自动化学报,2012,38(9):1471-1484.]
[28]DING K,TAN Y.Comparison of random number generators in particle swarm optimization algorithm[C]//Proceedings of2014IEEE Congress on Evolutionary Computation Evolutionary Computation.Washington,D.C.,USA:IEEE,2014:2664-2671.
[2]YUAN Shulai,ZHANG Liming,WANG Kewu,et al.Highefficient and high-yield mining technology of thin coal seam[M].Beijing:China Coal Industry Publishing House,2011(in Chinese).[袁树来,张立明,王克武,等.薄煤层高产高效开采技术[M],北京:煤炭工业出版社,2011.]
[3]JIANG Jinquan,DAI Jin,LI Hong,et al.Failure law and application of complex structure thin coal seam mining face[J].Journal of China Coal Society,2013,38(11):1912-1916(in Chinese).[蒋金泉,代进,李洪,等.复杂结构薄煤层工作面煤壁破坏规律及应用[J].煤炭学报,2013,38(11):1912-1916.]
[4]ZHAO Lijuan,LI Minghao,TIAN Zhen,et al.Coal plough's traction block reliability analysis and optimization design[J].Journal of China Coal Society,2016,41(3):776-781(in Chinese).[赵丽娟,李明昊,田震,等.刨煤机牵引块疲劳寿命分析与优化设计[J].煤炭学报,2016,41(3):776-781.]
[5]TAN Qing,YI Nianen,XIA Yimin,et al.Research on rock dynamic fragmentation characteristics by TBM cutters and cutter spacing optimization[J].Chinese Journal of Rock Mechanics and Engineering,2012,31(12):2453-2464(in Chinese).[谭青,易念恩,夏毅敏,等.TBM滚刀破岩动态特性与最优刀间距研究[J].岩石力学与工程学报,2012,31(12):2453-2464.]
[6]KANG Xiaomin,LI Guixuan,HAO Zhiyong.Optimization for cutting depth with minimum unit energy[J].Coal Mine Machinery,2004,25(9):42-43(in Chinese).[康晓敏,李贵轩,郝志勇.以极小化单位能耗为目标优化刨削深度[J].煤矿机械,2004,25(9):42-43.]
[7]ZHANG Qiang,FU Yunfei,SONG Qiushuang,et al.Parameter optimization of lowest energy consumption for plow based on artificial fish school algorithm[J].Journal of Guangxi University:Natural Science Edition,2012,37(2):241-246(in Chinese).[张强,付云飞,宋秋爽,等.基于人工鱼群算法刨煤机比能耗最低参数优化[J].广西大学学报:自然科学版,2012,37(2):241-246.]
[8]ZHANG Qiang,FU Yunfei,SONG Qiushuang,et al.Muitiobjective optimization reliability design for incomplete probability information plow head base on NSGA-Ⅱ[J].Chinese Journal of Engineering Design,2012,19(1):25-29(in Chinese).[张强,付云飞,宋秋爽,等.基于NSGA-Ⅱ算法的不完全概率信息刨头多目标模糊可靠性优化[J].工程设计学报,2012,19(1):25-29.]
[9]MAO Jun,YANG Xingwei,PAN Dewen,et al.Muti-objection optimal design for plough based on improved particle swarm optimization algorithm[J].Machine Design and Research,2016,32(1):168-170(in Chinese).[毛君,杨辛未,潘德文,等.基于改进粒子群算法的刨煤机多目标优化设计[J].机械设计与研究,2016,32(1):168-170.]
[10]CHEN Yinliang.Coal mining technology of coal plow in China[M].Beijing:China Coal Industry Publishing House,2000(in Chinese).[陈引亮.中国刨煤机采煤技术[M].北京:煤炭工业出版社,2000.]
[11]CОЛОЛВИ,ГЕТОЛАНОВВН,РАЧЕКВМ.Design and calculation of mining machinery and integrated units[M].Beijing:China Coal Industry Publishing House,1989(in Chinese).[В.И.索洛德,В.Н.格托帕诺夫,В.М.拉切克.采矿机械与综合机组的设计计算[M].北京:煤炭工业出版社,1989.]
[12]HAO Zhiyong.Research on mechanical characteristics of cutting coal and optimization design for coal plow bits[M].Liaoning:Liaoning Science and Technology Publishing House,2015(in Chinese).[郝志勇.刨煤机刨刀刨削煤岩力学特性研究及其优化设计[M].辽宁:辽宁科学技术出版社,2015.]
[13]TAN Qing,LYU Dan,XIA Yimin,et al.Thermo-mechanical coupling analysis of shield cutter head under mud cake condition[J].Journal of Chongqing University,2013,36(10):61-66(in Chinese).[谭青,吕丹,夏毅敏,等.泥饼工况下盾构刀盘热-力耦合分析[J].重庆大学学报,2013,36(10):61-66.]
[14]CHE Demeng,GUO Ping,EHMANN Kornel.Issues in polycrystalline diamond compact cutter-rock interaction from a metal machining point of view-part I:temperature,stresses,and forces[J].Journal of Manufacturing Science and Engineering,2012,134(6):114-122.
[15]GLOWKA D.A.The thermal response of rock to friction in the drag cutting process[J].Journal of Structural Geology,1989,11(7):919-931.
[16]QIU Kun,WANG Xinyong,PANG Siqin.Cast nickel-base superalloy K24study of cutting temperature[J].Journal of Functional Materials,2012,43(6):692-695(in Chinese).[邱坤,王新永,庞思勤.镍基铸造高温合金K24的切削温度实验研究[J].功能材料,2012,43(6):692-695.]
[17]Standardization Administration.GB-T 12718-2009 Mine high-intensity ring chain[S]Beijing:Standards Press of China,2009(in Chinese).[中国国家标准化管理委员会.GB-T12718-2009矿用高强度圆环链[S]北京:中国标准出版社,2009.]
[18]ПОЗИНЕЗ,МЕЛАМЕДВЗ,ТОНВВ.Coal breaking theory of shearer[M].Beijing:China Coal Industry Publishing House,1992(in Chinese).[E.З.保晋,B.З.乜拉麦德,B.B.顿.采煤机破煤理论[M].北京:煤炭工业出版社,1992.]
[19]CHENG Hang,ZHANG Shuiming,QUAN Long.Optimization method for wind turbine blade based on dominanted-constraint hybrid particle swarm[J].Journal of Mechanical Engineering,2015,51(1):176-181(in Chinese).[程珩,张水明,权龙.基于约束主导混合粒子群算法的风力机叶片优化方法研究[J],机械工程学报,2015,51(1):176-181.]
[20]KARANKI S B,MISHRA M K,KUMAR B K.Particle swarm optimization based feedback controller for unified power quality conditioner[J].IEEE Transactions on Power Delivery,2010,25(4):2814-2824.
[21]WU Yongming,DAI Longzhou,LI Shaobo,et al.Mixed assembly line evolution balancing based on improved particle swarm algorithm[J].Computer Integrated Manufacturing Systems,2017,23(4):781-790(in Chinese).[吴永明,戴隆州,李少波,等.基于改进粒子群优化算法的混流装配线演进平衡[J].计算机集成制造系统,2017,23(4):781-790.]
[22]WANG Yahui,YU Suihuai.Car styling design decision on multi-objective particle swarm optimization[J].Computer Integrated Manufacturing Systems,2017,23(4):681-688(in Chinese).[王亚辉,余隋怀.基于多目标粒子群优化算法的汽车造型设计决策模型[J].计算机集成制造系统,2017,23(4):681-688.]
[23]GUO Chenguang,HAN Xue,LI Yuan,et al.Thermal error modeling for spindle system of precision CNC lathe[J].Optics and Precision Engineering,2013,24(7):1731-1742(in Chinese).[郭辰光,韩雪,李源,等.精密数控车床主轴热误差建模[J].光学精密工程,2013,24(7):1731-1742.]
[24]LOPEZ G P,ONIEVA E,OSABA E,et al.A hybrid method for short-term traffic congestion forecasting using genetic algorithms and cross entropy[J].IEEE Transactions on Intelligent Transportation Systems,2016,17(2):557-569.
[25]DIABY A L,MIKLAVCIC S J,ADDAI M J.Optimization of scheduled cleaning of fouled heat exchanger network under aging using genetic algorithm[J].Chemical Engineering Research&Design:Transactions of the Institution of Chemical Engineers Part A,2016,113(9):223-240.
[26]LI Jing,GAO Zhenghong,HUANG Jiangtao,et al.Airfoil optimization based on distributed particle swarm algorithm[J].Acta Aero Dynamica Sinica,2011,29(4):464-469(in Chinese).[李静,高正红,黄江涛,等.基于分布式粒子群算法的翼型优化设计[J].空气动力学学报,2011,29(4):464-469.]
[27]LIU Jianhua,LIU Guomai,YANG Ronghua,et al.Analysis of interactivity and randomness in particle swarm optimization[J].Acta Automatica Sinica,2012,38(9):1471-1481(in Chinese).[刘建华,刘国买,杨荣华,等.粒子群算法的交互性与随机性分析[J].自动化学报,2012,38(9):1471-1484.]
[28]DING K,TAN Y.Comparison of random number generators in particle swarm optimization algorithm[C]//Proceedings of2014IEEE Congress on Evolutionary Computation Evolutionary Computation.Washington,D.C.,USA:IEEE,2014:2664-2671.