According to the strategic goal of sustainable development,construction and management for the construction of Shendong mining area with ecological safety,the ecological restoration principle of ＂ control protects de...According to the strategic goal of sustainable development,construction and management for the construction of Shendong mining area with ecological safety,the ecological restoration principle of ＂ control protects development and development promotes control＂ for desertification prevention and control was adopted,and engineering measures,plant greening measures,and enclosure management and protection measures were taken to prevent and control desertification in the mining area based on careful detailed investigation and accurate planning and design in the early period. After 32 years,the desertification ecological landscape environment of the mining area has undergone a qualitative change,and the vegetation coverage has increased from 3%-8% to above 60% after the development. The former desertification land has become a modern green energy base that has produced 200 million tons of coal every year. The construction and management mode of an ecologically safe and modernized green coal mining area built by Shendong in the desertification region of northwestern China shows that taking appropriate comprehensive ecological restoration construction technology and management measures that integrate engineering,plants and enclosure management and protection is an effective technical and management paradigm for the construction of a modernized green large-scale coal mining area in China＇s arid and semi-arid regions.展开更多
针对煤矿生产过程庞杂、各类工程项目繁多、缺乏便捷的信息化管理手段的问题,深入分析了煤矿工程项目的主要建设内容、管理过程和功能需求,设计了面向电脑Web终端和移动APP软件相结合的煤矿工程项目信息管理系统。基于Java开发语言,Web...针对煤矿生产过程庞杂、各类工程项目繁多、缺乏便捷的信息化管理手段的问题,深入分析了煤矿工程项目的主要建设内容、管理过程和功能需求,设计了面向电脑Web终端和移动APP软件相结合的煤矿工程项目信息管理系统。基于Java开发语言,Web终端采用MySQL数据库和Spring boot框架,APP软件利用Android平台和SQLite数据库,通过统一的Java Web API接口实现业务数据交互共享。经现场应用表明,系统为工程项目信息化管理提供了有效支撑,提高了项目的施工和管理效率。展开更多
A pplication o f m echanical excavators is one o f th e m o st com m only used excavation m eth o d s because itcan bring th e p ro ject m ore productivity, accuracy and safety. A m ong th e m echanical excavators, ro...A pplication o f m echanical excavators is one o f th e m o st com m only used excavation m eth o d s because itcan bring th e p ro ject m ore productivity, accuracy and safety. A m ong th e m echanical excavators, roadhead ers are m echanical m iners w h ich have b een extensively u se d in tu n n elin g , m ining an d civil indu stries. Perform ance pred ictio n is an im p o rta n t issue for successful ro a d h e a d e r application andgenerally deals w ith m achine selection, p ro d u ctio n rate an d b it consu m p tio n . The m ain aim o f thisresearch is to investigate th e c u ttin g p erfo rm an ce （in stan tan eo u s c u ttin g rates （ICRs）） o f m ed iu m -d u tyro ad h ead ers by using artificial neural n etw o rk （ANN） approach. T here are d ifferent categories forANNs, b u t based o n train in g alg o rith m th e re are tw o m ain k in d s： supervised and u n su p erv ised . Them u lti-lay er p ercep tro n （MLP） an d K ohonen self-organizing feature m ap （KSOFM） are th e m o st w idelyused neu ral netw o rk s for supervised an d u n su p erv ised ones, respectively. For gaining this goal, ad atab ase w as prim arily provided from ro ad h e a d e rs＇ p erfo rm an ce an d geom echanical characteristics o frock form ations in tu n n els and d rift galleries in Tabas coal m ine, th e larg est an d th e only fullymech an ized coal m ine in Iran. T hen th e datab ase w as analyzed in o rd e r to yield th e m ost im p o rtan tfactor for ICR by using relatively im p o rta n t factor in w hich G arson eq u atio n w as utilized. The MLPn etw o rk w as train ed by 3 in p u t p ara m e te rs including rock m ass pro p erties, rock quality d esignation（RQD）, in tact rock p ro p erties such as uniaxial com pressive stre n g th （UCS） an d Brazilian ten sile stren g th（BTS）, and o n e o u tp u t p a ra m e te r （ICR）. In o rd e r to have m ore v alidation o n MLP o u tp u ts, KSOFM visualizationw as applied. The m ean square e rro r （MSE） an d regression coefficient （R ） o f MLP w e re found tobe 5.49 an d 0.97, respectively. M oreover, KSOFM n etw o rk has a m ap size o f 8 x 5 and final qu an tizatio nan d topographic erro rs w e re 0.383 an d 0.032, respectively. The results show th a t MLP neural n etw orkshave a strong capability to p red ict an d ev alu ate th e perfo rm an ce o f m ed iu m -d u ty ro ad h ead ers in coalm easu re rocks. Furtherm ore, it is concluded th a t KSOFM neural n etw o rk is an efficient w ay for u n d e rstand in g system beh av io r an d know ledge extraction. Finally, it is indicated th a t UCS has m ore influenceo n ICR b y applying th e b e st train ed MLP n etw o rk w eig h ts in G arson eq u atio n w h ich is also confirm ed byKSOFM.展开更多
文摘According to the strategic goal of sustainable development,construction and management for the construction of Shendong mining area with ecological safety,the ecological restoration principle of ＂ control protects development and development promotes control＂ for desertification prevention and control was adopted,and engineering measures,plant greening measures,and enclosure management and protection measures were taken to prevent and control desertification in the mining area based on careful detailed investigation and accurate planning and design in the early period. After 32 years,the desertification ecological landscape environment of the mining area has undergone a qualitative change,and the vegetation coverage has increased from 3%-8% to above 60% after the development. The former desertification land has become a modern green energy base that has produced 200 million tons of coal every year. The construction and management mode of an ecologically safe and modernized green coal mining area built by Shendong in the desertification region of northwestern China shows that taking appropriate comprehensive ecological restoration construction technology and management measures that integrate engineering,plants and enclosure management and protection is an effective technical and management paradigm for the construction of a modernized green large-scale coal mining area in China＇s arid and semi-arid regions.
文摘针对煤矿生产过程庞杂、各类工程项目繁多、缺乏便捷的信息化管理手段的问题,深入分析了煤矿工程项目的主要建设内容、管理过程和功能需求,设计了面向电脑Web终端和移动APP软件相结合的煤矿工程项目信息管理系统。基于Java开发语言,Web终端采用MySQL数据库和Spring boot框架,APP软件利用Android平台和SQLite数据库,通过统一的Java Web API接口实现业务数据交互共享。经现场应用表明,系统为工程项目信息化管理提供了有效支撑,提高了项目的施工和管理效率。
文摘A pplication o f m echanical excavators is one o f th e m o st com m only used excavation m eth o d s because itcan bring th e p ro ject m ore productivity, accuracy and safety. A m ong th e m echanical excavators, roadhead ers are m echanical m iners w h ich have b een extensively u se d in tu n n elin g , m ining an d civil indu stries. Perform ance pred ictio n is an im p o rta n t issue for successful ro a d h e a d e r application andgenerally deals w ith m achine selection, p ro d u ctio n rate an d b it consu m p tio n . The m ain aim o f thisresearch is to investigate th e c u ttin g p erfo rm an ce （in stan tan eo u s c u ttin g rates （ICRs）） o f m ed iu m -d u tyro ad h ead ers by using artificial neural n etw o rk （ANN） approach. T here are d ifferent categories forANNs, b u t based o n train in g alg o rith m th e re are tw o m ain k in d s： supervised and u n su p erv ised . Them u lti-lay er p ercep tro n （MLP） an d K ohonen self-organizing feature m ap （KSOFM） are th e m o st w idelyused neu ral netw o rk s for supervised an d u n su p erv ised ones, respectively. For gaining this goal, ad atab ase w as prim arily provided from ro ad h e a d e rs＇ p erfo rm an ce an d geom echanical characteristics o frock form ations in tu n n els and d rift galleries in Tabas coal m ine, th e larg est an d th e only fullymech an ized coal m ine in Iran. T hen th e datab ase w as analyzed in o rd e r to yield th e m ost im p o rtan tfactor for ICR by using relatively im p o rta n t factor in w hich G arson eq u atio n w as utilized. The MLPn etw o rk w as train ed by 3 in p u t p ara m e te rs including rock m ass pro p erties, rock quality d esignation（RQD）, in tact rock p ro p erties such as uniaxial com pressive stre n g th （UCS） an d Brazilian ten sile stren g th（BTS）, and o n e o u tp u t p a ra m e te r （ICR）. In o rd e r to have m ore v alidation o n MLP o u tp u ts, KSOFM visualizationw as applied. The m ean square e rro r （MSE） an d regression coefficient （R ） o f MLP w e re found tobe 5.49 an d 0.97, respectively. M oreover, KSOFM n etw o rk has a m ap size o f 8 x 5 and final qu an tizatio nan d topographic erro rs w e re 0.383 an d 0.032, respectively. The results show th a t MLP neural n etw orkshave a strong capability to p red ict an d ev alu ate th e perfo rm an ce o f m ed iu m -d u ty ro ad h ead ers in coalm easu re rocks. Furtherm ore, it is concluded th a t KSOFM neural n etw o rk is an efficient w ay for u n d e rstand in g system beh av io r an d know ledge extraction. Finally, it is indicated th a t UCS has m ore influenceo n ICR b y applying th e b e st train ed MLP n etw o rk w eig h ts in G arson eq u atio n w h ich is also confirm ed byKSOFM.
