China is a significant atmospheric carbon dioxide producer.Burning of agricultural waste in China is also a problematic issue,raising environmental and carbon-emission-related concerns.Furthermore,the coal-dependent e...China is a significant atmospheric carbon dioxide producer.Burning of agricultural waste in China is also a problematic issue,raising environmental and carbon-emission-related concerns.Furthermore,the coal-dependent economy is accompanied by the formation of large coal mine goaf areas,particularly in Shanxi Province.In this context,the idea of filling crop residues into the coal mine goafs is proposed.This concept addresses multi-functions:(1)carbon sequestration,(2)an alternative disposal method of crop residue in rural areas,and(3)coal mine goaf remedy.展开更多
An approach which combines particle swarm optimization and support vector machine(PSO–SVM)is proposed to forecast large-scale goaf instability(LSGI).Firstly,influencing factors of goaf safety are analyzed,and followi...An approach which combines particle swarm optimization and support vector machine(PSO–SVM)is proposed to forecast large-scale goaf instability(LSGI).Firstly,influencing factors of goaf safety are analyzed,and following parameters were selected as evaluation indexes in the LSGI:uniaxial compressive strength(UCS)of rock,elastic modulus(E)of rock,rock quality designation(RQD),area ration of pillar(Sp),the ratio of width to height of the pillar(w/h),depth of ore body(H),volume of goaf(V),dip of ore body(a)and area of goaf(Sg).Then LSGI forecasting model by PSO-SVM was established according to the influencing factors.The performance of hybrid model(PSO+SVM=PSO–SVM)has been compared with the grid search method of support vector machine(GSM–SVM)model.The actual data of 40 goafs are applied to research the forecasting ability of the proposed method,and two cases of underground mine are also validated by the proposed model.The results indicated that the heuristic algorithm of PSO can speed up the SVM parameter optimization search,and the predictive ability of the PSO–SVM model with the RBF kernel function is acceptable and robust,which might hold a high potential to become a useful tool in goaf risky prediction research.展开更多
文摘China is a significant atmospheric carbon dioxide producer.Burning of agricultural waste in China is also a problematic issue,raising environmental and carbon-emission-related concerns.Furthermore,the coal-dependent economy is accompanied by the formation of large coal mine goaf areas,particularly in Shanxi Province.In this context,the idea of filling crop residues into the coal mine goafs is proposed.This concept addresses multi-functions:(1)carbon sequestration,(2)an alternative disposal method of crop residue in rural areas,and(3)coal mine goaf remedy.
基金supported by the National Basic Research Program Project of China(No.2010CB732004)the National Natural Science Foundation Project of China(Nos.50934006 and41272304)+2 种基金the Graduated Students’ResearchInnovation Fund Project of Hunan Province of China(No.CX2011B119)the Scholarship Award for Excellent Doctoral Student of Ministry of Education of China and the Valuable Equipment Open Sharing Fund of Central South University(No.1343-76140000022)
文摘An approach which combines particle swarm optimization and support vector machine(PSO–SVM)is proposed to forecast large-scale goaf instability(LSGI).Firstly,influencing factors of goaf safety are analyzed,and following parameters were selected as evaluation indexes in the LSGI:uniaxial compressive strength(UCS)of rock,elastic modulus(E)of rock,rock quality designation(RQD),area ration of pillar(Sp),the ratio of width to height of the pillar(w/h),depth of ore body(H),volume of goaf(V),dip of ore body(a)and area of goaf(Sg).Then LSGI forecasting model by PSO-SVM was established according to the influencing factors.The performance of hybrid model(PSO+SVM=PSO–SVM)has been compared with the grid search method of support vector machine(GSM–SVM)model.The actual data of 40 goafs are applied to research the forecasting ability of the proposed method,and two cases of underground mine are also validated by the proposed model.The results indicated that the heuristic algorithm of PSO can speed up the SVM parameter optimization search,and the predictive ability of the PSO–SVM model with the RBF kernel function is acceptable and robust,which might hold a high potential to become a useful tool in goaf risky prediction research.
