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none;">E55B5CF3F568463186A5B36F7AF52EF2</span> <div class="title">全尾砂充填体正交-BP 经|络强度预测</div> <div class="sub-title">2016-01-12</div> <!-- D start --> <div class="introduction"> 某矿山采用全胶结充填,需加入减水剂以保证充填料浆动性能,其充填体强度各媄响因素之?存在着更ؓ复杂的化学物理作用。ؓ了解各因素对充填体强度的影响规律,准确预测其强?建立了基于正交试?的BP 经|络全尾砂胶l充填体强度预测模型。预过E中,为提高样本可信度,以料浓度、灰砂比、减水剂比例 为媄响因?设计? 因素? 水^正交试验Ҏ作ؓ研究数据基础。采用灰色关联度理论分析了各因素对全?填体强度的媄响规?l果表明:对充填体早期强度(7 d)影响最大的是料质量浓?其次为灰砂比、减水剂比例,?充填体后期强?28 d)影响最大的是灰砂比,其次为料浓度、减水剂。以7 d?.. </div> <!-- D start --> <!-- 正文 --> <div class="mainbody"> <div style="display:none"> Series NoQ?475 w Januaryw 2016 金ꢀ w 属ꢀ w 矿ꢀ w 屘ˢ w w w w w w w w w w w w w w ȝ 475? METAL MINE 2016 q第 1 ? w w w w w w w w w w w w w w 全尾砂充填体正交wBP 经|络强度预测 1 2 2 2 刘恒?w 张钦C?w 王新?w 肖崇? ( 1. 新桥铁?安徽 铜陵 244132; 2. 中南大学资源与安全工E学?湖南 长沙 410083) 摘ꢀ 要ꢀ 某矿山采用全胶结充填,需加入减水剂以保证充填料浆动性能,其充填体强度各媄响因素之? 存在着更ؓ复杂的化学物理作用?Z解各因素对充填体强度的媄响规?准确预测其强?建立了基于正交试? ?BP 经|络全尾砂胶l充填体强度预测模型?预测q程?为提高样本可信度,以料浓度、灰砂比、减水剂比例 为媄响因?设计?3 因素? 水^正交试验Ҏ作ؓ研究数据基础?采用灰色兌度理论分析了各因素对全尾砂充 填体强度的媄响规?l果表明:对充填体早期强度(7 d)影响最大的是料质量浓?其次为灰砂比、减水剂比例,? 充填体后期强?28 d)影响最大的是灰砂比,其次为料浓度、减水剂??7 d?8 d 强度出因?q用 BP 经 |络对充填体强度q行预测,预测l果与试验结果最大误差ؓ 9. 98% ,q_误差?2. 71% ,_ֺ较高,预测可靠性强, h较好的工E应用h倹{? 关键词ꢀ 全尾砂ꢀ 减水剂ꢀ 正交试验w 胶结充填体ꢀ 强度预测 w w 中图分类号ꢀ TD853w w w 文献标志码ꢀ Aw w w 文章~号w 1001-1250(2016)-01-047-04 Strength Prediction of Unclassified Tailing Backfilling Based on Orthogonal-BP Neural Network 1 2 2 2 Liu Hengliang w Zhang Qinli w Wan Xinmin w Xiao Chongchun ( 1. Xinqiao Pyrite Mine,Tongling 244132,China; 2 . School of Resources and Safety Engineering,Central South University,Changsha 410083,China) Abstractw With the application of the unclassified tailing cemented filling in a mine,the water reducing agent is added to ensure the flow property of filling slurry. There exists more complex chemical-physical effect among various influencing factors of filling body strength. In order to grasp the effect law of various factors on the strength of filling body,and predict its strength accurately,the backfilling strength of cementing filling body prediction model is set up based on the BP neural network of or- thogonal experiment. In the forecasting process,three factors and four levels orthogonal experiment scheme is designed as the research data base,considering slurry concentration,cement-sand ratio,water reducing agent adding proportion as the influen- cing factors,to improve the sample scientific credibility. The grey relational theory is used to analyze the effect law of every fac- tors on the backfilling filling body:the biggest impact on the early strength of filling body (7 d) is slurry concentration,fol- lowed by cement-sand ratio,water reducing agent proportion. The late strength of filling body (28 d) is most affected by ce- ment-sand ratio,then followed by slurry concentration,water reducing agent proportion. For purpose of predicting the strength of the backfilling filling body exactly,it regards 7 d,28 d strength as the output factor,and adopts BP neural network,and as a re- sult the maximum relative error is 9. 98% ,the average relative error is 2. 71% ,which meets the requirements of filling body strength prediction,and has a good application value to the project. Keywordsw Unclassified tailing,Water reducing agent,Orthogonal experiment,Backfilling filling body,Srength prediction [1] 长远的破??对于q两大难?业内学者如于润 [2] [3] 沧院?,焦华喆等 普遍认ؓ对井下采I区q行 全尾砂胶l充填是理想的解x?是采矿技术未? 的发展趋ѝ? w w 随着我国l济的高速发展和资源的大量消?? 业主要面临着资源开采的深部化和地表的灾? 化两大难题?深部开采面临地压增大的情况,严重? 甚至产生破坏性极大的岩爆;而全直接排放到地 表不仅会占用大量的地表空?更会l生态环境造成 对于全尾砂胶l充填来?充填体强度是评判? 填质量好坏的一个重要指标?一般通过实验室全? 收稿日期w 2015-11-17 基金目w “十二五”国家科技支撑计划目(~号:2012BAC09B02)? 作者简介ꢀ 刘恒??,?dE师,高工程师?通讯作者ꢀ 张钦C?1964?,?教授,博士研究生导师? · 47· ȝ 475 期ꢀ w w w w w w w w w w w w 金ꢀ w 属ꢀ w 矿ꢀ w 屘ˢ w w w w w w w w w 2016 q第 1 ? 配比试验,得不同配比参数下的充填体强?q? 推荐满采矿Ҏ强度要求的配比参?但全面试? 工作量大,且容易受Zؓ操作{因素干?在实际生 产应用中h一定的局限性?为减试验次?推荐 满不同充填充强度要求配比参?q年?业内? 者对于充填体强度预测q行了多斚w研究,提出了回 ?1w 正交试验因素水^安排 Table 1w Factors and levels of orthogonal experiments 各因素取? 水^ A/ % 68 B C / % 0 1 2 3 4 1 ?4 1 ?6 1 ?8 1 ?10 70 0Q?4 0Q?8 1Q?2 72 [ 4] [5] 归分?、神l网l预?{强度预方?取得? 一定的成果?某矿研究采用全尾砂胶l充?因其? l粒U颗_含量大、渗透系数小,D充填料浆 的流动性能?因此,需要加入减水剂以改善充填料 的动性能、降低井下充填料的泌水率。?相比 于传l的分񔞮胶结充填,全尾砂充填体因其物化 性质的特D?D其强度各影响因素之间存在着? 为复杂的化学物理作用,而回归分析法中回归函数的 选取主要以经验ؓ丅R科学性不?D预测l果? 74 ?2w 正交试验l果 Table 2w The data of orthogonal experiments 试验 序号 各因素水q? 试验l果/ MPa A 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 B C 1 2 3 4 2 1 4 3 3 4 1 2 4 3 2 1 σ(7 d) 1Q?21 0Q?55 0Q?42 0Q?36 1Q?61 0Q?87 0Q?52 0Q?37 2Q?10 0Q?91 0Q?94 0Q?46 2Q?41 1Q?45 1Q?05 0Q?67 σ(28 d) 3Q?23 1Q?12 0Q?96 0Q?69 3Q?57 1Q?77 1Q?13 0Q?71 4Q?91 2Q?20 1Q?43 1Q?02 6Q?61 2Q?36 1Q?76 1Q?20 1 2 3 4 5 6 7 8 9 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 [ 6] 差较?,难以满全尾砂胶l充填体强度预测? _ֺ要求? 因此,本研I对全充填料浆的实际情? [ 7] 设计采用正交试验 以科学合理地安排与分析多? 1 1 0 1 [ 8] 响因素试?q利用灰色关联度理论 探究各因? 对充填体的媄响程?在此基础?建立Z正交? 验的 BP 经|络全尾砂充填体强度预测模型,对全 充填体的强度q行预测,l果表明:预测l果? 实测D差较?可以满强度预测的精度要求? 12 1 1 1 1 3 4 5 6 1 w 充填配比正交试验设计 正交试验以概率论、数理统计和实践l验为基 w w ?σ(7 d)、?28 d)分别为全胶结充填?7 d ?8 d 抗压? 度倹{? ,Ҏ正交性从全面试验中挑选出部分有代表性的 点进行试?是研I多因素多水q的一U经高效的 设计Ҏ? 关系的大、强弱和ơ序?一般来说若兌度≥0Q?8, 认ؓ兌度很?介于 0Q?6 ?0Q?8 之间兌度较? 于 0Q?5 ?认ؓ基本不相兟? 充填料浆一般由充填骨料、胶凝材料和?3 部分 搅拌而成?本试验采用某矉K厂全尾?其粒径组? ?0Q?075 ~ 0Q?005 mm 的颗_占 54Q?6% ,但中值粒? 仅ؓ 0Q?062 mm,属于l粒U全;胶凝材料选用? ( 1)定分析序列:以充填体抗压强度(x0 )为母 序列,以充填料质量浓?x1 )、灰砂比(x2 )、减? 剂比?x3 )3 个媄响因素ؓ子序列?建立母序列和 子序列计矩? # ?32Q?5 酸盐水?此外,为改善充填料流动? [ x0 ,x1 ,x2 ,x3 ] = ?采用减水剂作为外加剂?Ҏ该矿情况,以料? 度、灰砂比和减水剂比例作ؓ影响因素(依次为因 ?A、B、C),遵@均衡试验的原?各个因素水q? 控制在最优范围内,每个因素选取 4 个试验水q?? 计了 3 因素? 水^正交试验Ҏ(?1),试验l果 如表 2 所C? é x0(1) x1(1) x2(1) x3(1) ù x0(2) x1(2) x2(2) x3(2) |? |? |? |? x0(16) x1(16) x2(16) x3(16) ë ] w w (2)数据无量U化处理:对结果数据进行均值化 2 w 灰色兌度分? 法无量纲化处? ׃概况全尾砂细泥含量较?充填料浆中加? xi(k) m xi(k) = (i = 1,2,3;k = 1,2,?16)Q? 减水?各因素对于强度的影响E度不能仅依靠传l? l验q行判断?灰色兌度分析是一U多因素的统 计分析法,主要通过计算灰色兌度来表示因素之间 1 m ? i x (k) k = 1 w w (3)计算兌度系? · 48· w w w 刘恒亮等:全尾砂充填体正交wBP 经|络强度预测w w w w w w w w w w w w w w 2016 q第 1 ? miin(Δi(min)) + 0. 5 miax(Δi(max)) x0(k) - xi(k) + 0. 5 miax(Δi(max))Q? 的关键之一,节点数设|太多会D学习旉q长 甚至不能收敛,而节Ҏ讄太少,BP 经|络计算 l果误差偏大?本研I过Ҏ分析不同节点个数 的网l性能,军_采用 3w?w? 型的 BP |络l构(? ξi(k) = 其中, miin(Δi(min)) = miin(mkin x0(k) - xi(k) ); miax(Δi(max)) = miax(mkax x0(k) - xi(k) ); 1 ),l果表明能够很好地满_际的需要? ξi(k) 是第 k 个时L较曲U?xi 与参考曲U?x0 的相 对差?它称?xi ?x0 ?k 时刻的关联系?0. 5 ? 分LpL? ( 4)计算兌? m ri = 1 ∑ξi(k). m k = 1 w w 按照上述步骤对试验结??2) q行兌度计 ?1w BP |络l构模型 ?分别得出 7 d ?28 d 充填体强度与料浆质量? 度、灰砂比、减水剂比例 3 个媄响因素的兌度及? Fig. 1w Model of BP network structure 经|络计算前需要对所有样本数据进行归一 联度排序,如表 3 所C? 化处?以消除各l数据间数量U差?同样?在输 出预结果时,要对l果数据q行反归一化处理? BP 经|络训练计算的步数如?2 所C?整个法 程如图 3 所C? ?3w 灰色兌度计结? Table 3w Results of grey relational analysis ?7 d 强度 ?28 d 强度 影响因素 质量 度 减水? 比ꢀ ? 质量 度 减水? 比ꢀ ? 灰砂? 灰砂? 兌?0Q?666 6 0Q?659 8 0Q?578 3 0Q?686 7 0Q?813 5 0Q?589 兌排序 1 2 3 2 1 3 w w p 3,l合灰色兌度理论可?对充填体? 期强?7 d)影响最大的是料质量浓?灰砂比即 水惔含量影响ơ之,两者相差不大?对充填体后期? ?28 d)影响最大的是料灰砂比,质量度ơ之? 虽然增加水惔的掺量能够提供充填体的强?但充? 成本亦高,因此,应尽量提高充填料的质量度,q? 样不仅可以提高充填体的强?q可以降低充填成 本? ?2w 训练步数 Fig. 2w Training steps 3 w 充填体强度预? BP(Back Propagation)经|络是一U按误差? 传播法训练的多层前馈网l?是目前应用最q泛? 经|络模型之一?BP |络能学习和存贮大量的输 入—输出模式映关p?而无需事前揭示描述q种? 关pȝ数学方程?它的学习规则是用最速下? ?通过反向传播来不断调整网l的权值和阈?? |络的误差^方和最?BP 经|络模型拓扑l构 主要包括输入?input)、隐含层(hidden layer) 和输 ?3w 法程 Fig. 3w Flow chart of arithmetic 3 Q?2w 充填体强度的预测 训练h随机选取?2 中的 12 l数?剩余? l数据作为测试样?采用 Matlab 软g提供?BP 4 经|络工具p行计分?BP 经|络的预? l果如表 4 所C? [ 9-11] 出层(output layer) Q?1w |络模型的选取 以充填料质量浓度、灰砂比、减水剂比例 3 ? ? 3 从表 4 中可以看出预结果与试验l果最大误 差ؓ 9Q?98% ,q_误差?2Q?71% ,_ֺ较高,预测? 靠性强,对于该矿全尾砂胶l充填体强度预测h一 定的应用价倹{? 因素作ؓ|络输入因子,全尾砂胶l充填体 7 d 抗压 强度?28 d 抗压强度作ؓ|络输出因子,建立 BP ? l网l?隐含层节点个数的选取?BP 经|络? · 49· ȝ 475 期ꢀ w w w w w w w w w w w w 金ꢀ w 属ꢀ w 矿ꢀ w 屘ˢ w w w w w w w w w 2016 q第 1 ? ?4w 充填体强度预结? 充填体强度预及评h[ J]. 采矿与安全工E学?2012,29 5):714-718. ( Table 4w Prediction results of the filling body strength Han Bin,Wang Xianlai,Xiao Weiguo,et al. Estimation and evalua- tion of backfill strength in underground stope based on multivariate nonlinear regression analysis[ J]. 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