摘要
我国是世界上煤炭生产量和消耗量最大的国家,但由于水文地质条件复杂,在煤矿生产过程中煤层顶底板突水事故频发,常常造成严重的经济损失和人员伤亡,快速精准地判别水害来源是矿井突水水害防治的关键步骤。基于河北开滦赵各庄矿的67个水样实测数据,将Na^(+),Ca^(2+),Mg^(2+),Cl^(-),SO_(4)^(2-),HCO3^(-)六种离子的物质的量浓度作为输入项,突水水源类型为其输出项,应用鲸鱼优化算法(WOA)改进极限学习机(ELM)形成WOA-ELM判别模型实现突水水源判别。研究结果表明:以往的单一极限学习机具有稳定性差的缺点,采用鲸鱼算法对其权值和阈值进行迭代寻优,通过环形包围、气幕袭击、随机搜索3种方式的鲸鱼优化算法对最优参数进行搜索,收敛速度快、全局搜索能力强。根据座头鲸捕食行为建立的数学模型,由于猎物(突水)位置不确定,WOA算法首先假设当前的最佳候选解是目标猎物位置或最靠近猎物的位置,然后通过随机产生向量A和概率p来决定鲸鱼更新位置的方式。当|A|>1时随机搜索猎物;当|A|<1时,以0.5为分界点,p<0.5选择环形包围模式,p>0.5则通过螺旋运动来更新位置。依据最低适应度值得到最优个体的位置,最终将输出的42个最优权值和阈值赋给ELM模型,对待测样本进行判别。通过对比,WOA-ELM判别模型在矿井突水水源识别中的准确率高达95%以上,与单一ELM模型相比,准确率提升了15%左右。与支持向量机模型(SVW)、粒子群优化的极限学习机(PSO-ELM)模型以及灰狼优化算法改进的极限学习机(GWO-ELM)模型等相比,该模型具有更快的收敛速度与更高的精度,稳定性和泛化能力也均得到提升。
China is the country with the largest coal production and consumption in the world.However,due to the complex hydrogeological conditions,water inrush accidents occur frequently in the coal seam roof and floor during coal mining,causing serious economic losses and casualties.Identifying the source of water hazards rapidly and accurately is the key step in mine water inrush prevention and control.Based on the measured data of 67 water samples from Zhaogezhuang mine in Kailuan,the ion concentrations of six ions,including Na^(+),Ca^(2+),Mg^(2+),Cl^(-),SO_(4)^(2-)and HCO3^(-),were taken as input items,and the type of water inrush source was taken as output item.The WOA-ELM discriminant model was developed by using the Whale Optimization Algorithm(WOA)to improve ELM for achieving the water inrush source discrimination.The results show that the single extreme learning machine has the disadvantage of poor stability,and the whale algorithm is used to iteratively optimize its weight and threshold values.The whale optimization algorithm using three methods of ring encirclement,air curtain attack,and random search is used to search for optimal parameters,with fast convergence speed and strong global search ability.A mathematical model is established based on the predation behavior of humpback whales.Due to the uncertainty of prey position(water inrush),the WOA algorithm first assumes that the current best candidate solution is the target prey position or the closest position to the prey,and then randomly generates the vector A and the probability p to determine the way that the whales update the position.When|A|>1,random search prey is chosen.When the|A|<1,the probability of 0.5 is taken as the cut-off point.When p is less than 0.5,the enveloping mode is selected.Otherwise,the position is updated by spiral motion.According to the lowest fitness value,the position of the optimal individual is obtained.Finally,the output 42 optimal weights and thresholds are assigned to the ELM model to identify the samples to be tested.By comparison,the accuracy of the WOA-ELM discriminant model in identifying the mine water inrush source is up to 95%,about 15%higher than that of the single ELM model.Compared with the Support Vector Machine(SVM)model,the Particle Swarm OptimizationExtreme Learning Machine(PSO-ELM)model and the Gray Wolf Optimization-Extreme Learning Machine(GWOELM)model,the WOA-ELM model has a faster convergence speed and higher precision.The stability and generalization ability of the model are also improved.
作者
董东林
陈昱吟
倪林根
李源
覃华清
韦仙宇
DONG Donglin;CHEN Yuyin;NI Lingen;LI Yuan;QIN Huaqing;WEI Xianyu(School of Geosciences&Surveying Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China)
出处
《煤炭学报》
EI
CAS
CSCD
北大核心
2021年第3期984-993,共10页
Journal of China Coal Society
基金
国家自然科学基金资助项目(41972255)
国家自然科学基金联合基金资助项目(U1710258)
国家重点研发计划资助项目(2017YFC0804104)。
