Mining Safety and Sustainability I
Safety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of developmen...
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| Year of Publication: | 2022 |
| Language: | English |
| Physical Description: | 1 electronic resource (348 p.) |
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| 100 | 1 | |a Dong, Longjun |4 edt | |
| 245 | 1 | 0 | |a Mining Safety and Sustainability I |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2022 | ||
| 300 | |a 1 electronic resource (348 p.) | ||
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| 520 | |a Safety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of development of mineral resource. This book provides a platform to present new research and recent advances in the safety and sustainability of mining. More specifically, Mining Safety and Sustainability presents recent theoretical and experimental studies with a focus on safety mining, green mining, intelligent mining and mines, sustainable development, risk management of mines, ecological restoration of mines, mining methods and technologies, and damage monitoring and prediction. It will be further helpful to provide theoretical support and technical support for guiding the normative, green, safe, and sustainable development of the mining industry. | ||
| 546 | |a English | ||
| 650 | 7 | |a Technology: general issues |2 bicssc | |
| 650 | 7 | |a History of engineering & technology |2 bicssc | |
| 653 | |a top-coal caving mining | ||
| 653 | |a process parameters | ||
| 653 | |a decision model | ||
| 653 | |a BP neural network | ||
| 653 | |a similaritysimulation test | ||
| 653 | |a time-dependent cohesion | ||
| 653 | |a traction force | ||
| 653 | |a deep-sea sediment | ||
| 653 | |a tracked miner | ||
| 653 | |a rheology | ||
| 653 | |a cemented paste backfill | ||
| 653 | |a curing conditions | ||
| 653 | |a mechanical properties | ||
| 653 | |a mathematical strength model | ||
| 653 | |a AMD | ||
| 653 | |a phytoremediation | ||
| 653 | |a sulfate | ||
| 653 | |a hydroponic experiment | ||
| 653 | |a wetland plants | ||
| 653 | |a ecological pollution | ||
| 653 | |a tailings dam | ||
| 653 | |a safety factor | ||
| 653 | |a quantitative evaluation | ||
| 653 | |a dynamic weight | ||
| 653 | |a comprehensivediagnosis of health | ||
| 653 | |a : rock formations | ||
| 653 | |a surface subsidence law | ||
| 653 | |a surface subsidence process | ||
| 653 | |a 3D test device | ||
| 653 | |a 3Dlaser scanning | ||
| 653 | |a mine ventilation network | ||
| 653 | |a wind speed sensors distribution | ||
| 653 | |a air volume reconstruction | ||
| 653 | |a independent cut set | ||
| 653 | |a surface subsidence | ||
| 653 | |a probability integration | ||
| 653 | |a loess donga | ||
| 653 | |a superimposed calculation | ||
| 653 | |a additional displacement of slope mining slip | ||
| 653 | |a : mining water hazard | ||
| 653 | |a microseismic monitoring | ||
| 653 | |a intelligent recognition | ||
| 653 | |a feature extraction | ||
| 653 | |a support vector machine | ||
| 653 | |a classification model | ||
| 653 | |a freeze–thaw cycles | ||
| 653 | |a tailings | ||
| 653 | |a mechanical behavior | ||
| 653 | |a SEM | ||
| 653 | |a MIP | ||
| 653 | |a thick aeolian sand | ||
| 653 | |a shallow buried thick seam | ||
| 653 | |a overburden failure | ||
| 653 | |a ground damage | ||
| 653 | |a numerical simulation | ||
| 653 | |a rock mechanics | ||
| 653 | |a cyclic impact | ||
| 653 | |a chemical corrosion | ||
| 653 | |a axial compression | ||
| 653 | |a strength degradation | ||
| 653 | |a pipe transportation system test | ||
| 653 | |a pressure loss | ||
| 653 | |a random forest algorithm | ||
| 653 | |a filling-aided design | ||
| 653 | |a vibration signals | ||
| 653 | |a neural network | ||
| 653 | |a drilling state identification algorithm | ||
| 653 | |a drilling depth | ||
| 653 | |a monitoring-while-drilling method | ||
| 653 | |a | ||
| 776 | |z 3-0365-4687-1 | ||
| 700 | 1 | |a Zhao, Yanlin |4 edt | |
| 700 | 1 | |a Chen, Wenxue |4 edt | |
| 700 | 1 | |a Dong, Longjun |4 oth | |
| 700 | 1 | |a Zhao, Yanlin |4 oth | |
| 700 | 1 | |a Chen, Wenxue |4 oth | |
| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 05:38:08 Europe/Vienna |f system |c marc21 |a 2022-11-05 21:33:14 Europe/Vienna |g false | ||
| AVE | |i DOAB Directory of Open Access Books |P DOAB Directory of Open Access Books |x https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5340570130004498&Force_direct=true |Z 5340570130004498 |b Available |8 5340570130004498 | ||