基于SD的采煤面瓦斯浓度综合管控优化研究
|
摘要
为实现采煤面瓦斯浓度管控措施的组合优化,基于系统动力学理论(SD),构建了采煤面瓦斯浓度变化的因果关系结构和综合管控系统结构。分析采煤面瓦斯浓度的系统动力学行为,阐明瓦斯浓度随生产活动调整的变化规律,提出瓦斯浓度综合管控措施的优化方法。研究表明:影响采煤面瓦斯浓度的活动因素主要为单产、通风和瓦斯抽采,影响次序为单产、瓦斯抽采、通风;瓦斯浓度的动力学行为呈指数变化的振荡模式;利用非线性规划可实现瓦斯浓度管控措施的组合优化。将研究成果应用于矿井工作面瓦斯浓度的管控中,通过降低单产87 t/d,提高瓦斯抽采量8 m3/min,加大回风巷风量93 m3/min的组合优化策略,使工作面瓦斯浓度从0.44%～0.76%降低为0.22%～0.51%,有效预防了瓦斯事故的发生。
In order to realize the combination optimization of coal mining face gas concentration control measures, based on the system dynamics, the causal relationship structure of gas concentration change and the structure of gas concentration control system were established. The system dynamic behavior of coal seam gas concentration was analyzed, the change law of the gas concentration in the coal mining face with the adjustment of production activity was clarified, and a scientific prediction method of gas concentration and an optimization method of comprehensive control measures were proposed. Results showed that, the factors that affected the change of gas concentration in coal mining face mainly were: yield, ventilation and gas extraction, and the order of influence was yield, gas extraction, ventilation; the dynamic behavior of the gas concentration was exponential oscillatory mode;the combinatorial optimization of the comprehensive control measures of gas concentration could be realized by the nonlinear programming method. Applying the research results to the working face of coal mine, by the combination optimization strategy of reducing the yield per unit area of 87 t/d, increasing the gas extraction capacity of 8 m3/min, and increasing the air volume of return air lane of 93 m3/min, the gas concentration in the working face was reduced from range 0.44% to 0.76% to range 0.22%to 0.51%, which effectively prevented gas accidents.
In order to realize the combination optimization of coal mining face gas concentration control measures, based on the system dynamics, the causal relationship structure of gas concentration change and the structure of gas concentration control system were established. The system dynamic behavior of coal seam gas concentration was analyzed, the change law of the gas concentration in the coal mining face with the adjustment of production activity was clarified, and a scientific prediction method of gas concentration and an optimization method of comprehensive control measures were proposed. Results showed that, the factors that affected the change of gas concentration in coal mining face mainly were: yield, ventilation and gas extraction, and the order of influence was yield, gas extraction, ventilation; the dynamic behavior of the gas concentration was exponential oscillatory mode;the combinatorial optimization of the comprehensive control measures of gas concentration could be realized by the nonlinear programming method. Applying the research results to the working face of coal mine, by the combination optimization strategy of reducing the yield per unit area of 87 t/d, increasing the gas extraction capacity of 8 m3/min, and increasing the air volume of return air lane of 93 m3/min, the gas concentration in the working face was reduced from range 0.44% to 0.76% to range 0.22%to 0.51%, which effectively prevented gas accidents.
引文
[1]刘俊娥,安凤平,林大超,等.采煤工作面瓦斯涌出量的固有模态SVM建模预测[J].系统工程理论与实践,2013,33(2):505-511.
[2]王晓路,刘健,卢建军.基于小波变换和优化预测器的瓦斯浓度预测[J].应用基础与工程科学学报,2011,19(3):499-508.
[3]杨丽,刘晖,毛善君,等.基于多元分布滞后模型的瓦斯浓度动态预测[J].中国矿业大学学报,2016,45(3):455-461.
[4]魏林,白天亮,付华,等.基于EMD-LSSVM的瓦斯浓度动态预测模型[J].安全与环境学报,2016,16(2):119-123.
[5]冯占科.数字矿山瓦斯防治管理与分析决策系统的研究与实现[J].中国煤层气,2016,13(4):42-46.
[6]张津嘉,许开立,王贝贝,等.特别重大煤矿瓦斯爆炸事故致因分析及管理模式研究[J].中国安全科学学报,2016,26(2):73-78.
[7]孙继平.瓦斯综合防治方法研究[J].工矿自动化,2011,37(2):1-5.
[8]姚昕.基于PLC的矿井局部通风机瓦斯浓度智能控制系统研究[D].西安:西安科技大学,2014.
[9]张文华,梁志军.瓦斯防治技术与管理措施[J].煤炭科学技术,2014(S1):154-155.
[10]肖鹏,丁毅,李树刚.基于未确知测度的矿井瓦斯防治管理体系评价[J].中国安全科学学报,2017,27(1):98-103.
[11]田水承,张利华,王莉.煤矿瓦斯爆炸3类危险源系统的SD[J].西安科技大学学报,2011,31(6):689.
[12]田水承,张利华,许永刚.基于三类危险源的瓦斯爆炸SD仿真[J].煤矿安全,2012,43(7):213-216.
[13]张利华.煤矿瓦斯爆炸危险源SD模型构建及仿真研究[D].西安:西安科技大学,2012.
[14]金智新,王延生,邓存宝,等.广义安全结构理论研究[J].中国安全科学学报,2017,27(5):41-46.
[15]金智新,王延生,邓存宝.基于安全结构理论的安全事件内涵数学表达[J].中国安全科学学报,2017,27(10):38-43.
[16]俞启香,程远平.矿井瓦斯防治[M].徐州:中国矿业大学出版社,2012.
[2]王晓路,刘健,卢建军.基于小波变换和优化预测器的瓦斯浓度预测[J].应用基础与工程科学学报,2011,19(3):499-508.
[3]杨丽,刘晖,毛善君,等.基于多元分布滞后模型的瓦斯浓度动态预测[J].中国矿业大学学报,2016,45(3):455-461.
[4]魏林,白天亮,付华,等.基于EMD-LSSVM的瓦斯浓度动态预测模型[J].安全与环境学报,2016,16(2):119-123.
[5]冯占科.数字矿山瓦斯防治管理与分析决策系统的研究与实现[J].中国煤层气,2016,13(4):42-46.
[6]张津嘉,许开立,王贝贝,等.特别重大煤矿瓦斯爆炸事故致因分析及管理模式研究[J].中国安全科学学报,2016,26(2):73-78.
[7]孙继平.瓦斯综合防治方法研究[J].工矿自动化,2011,37(2):1-5.
[8]姚昕.基于PLC的矿井局部通风机瓦斯浓度智能控制系统研究[D].西安:西安科技大学,2014.
[9]张文华,梁志军.瓦斯防治技术与管理措施[J].煤炭科学技术,2014(S1):154-155.
[10]肖鹏,丁毅,李树刚.基于未确知测度的矿井瓦斯防治管理体系评价[J].中国安全科学学报,2017,27(1):98-103.
[11]田水承,张利华,王莉.煤矿瓦斯爆炸3类危险源系统的SD[J].西安科技大学学报,2011,31(6):689.
[12]田水承,张利华,许永刚.基于三类危险源的瓦斯爆炸SD仿真[J].煤矿安全,2012,43(7):213-216.
[13]张利华.煤矿瓦斯爆炸危险源SD模型构建及仿真研究[D].西安:西安科技大学,2012.
[14]金智新,王延生,邓存宝,等.广义安全结构理论研究[J].中国安全科学学报,2017,27(5):41-46.
[15]金智新,王延生,邓存宝.基于安全结构理论的安全事件内涵数学表达[J].中国安全科学学报,2017,27(10):38-43.
[16]俞启香,程远平.矿井瓦斯防治[M].徐州:中国矿业大学出版社,2012.