资源利用与能耗约束下铁矿采选品位的智能优化
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摘要
集成非线性目标规划模型与差分进化(DE)及神经网络(ANN)等智能算法,提出了资源利用和节能降耗约束下铁矿采选生产品位的动态优化方法。首先建立以截止品位和入选品位为决策变量,精矿产量、资源利用率、总用电量以及经济效益为目标约束的非线性目标规划模型,模型中包括损失率、选矿金属回收率和采选成本三个非线性函数;然后将所构建的非线性目标规划模型转化成无约束优化问题,将DE的高效寻优能力和ANN的建模功能相结合,构成DE-ANN算法来搜索最优采选品位组合;最后以D铁矿为例进行研究,结果表明了所提出方法的有效性。该方法综合考虑了资源利用、节能降耗等因素,为新时期铁矿的采选品位优化提供了科学可行的思路。
The method of dynamic optimization of mining & milling grades is proposed under the constraints of the resource utilization and energy consumption,by integrating nonlinear goal programming( NGP) model and the intelligent algorithms of differential evolution( DE) and artificial neural network( ANN). First,the nonlinear goal programming model is established,with cut-off grade and dressing grade as decision variables,and concentrate production,resource utilization rate,total electricity consumption and economic benefit as goal constraints. The NGP model contains three nonlinear functions which are loss rate function,milling metal recovery rate function and the cost function. Then the NGP model is transformed into unconstrained optimization problem. Considering the advantage optimization ability of DE and modeling function of ANN,the DE and ANN are combined to the DE-ANN algorithm,to search the optimal grades combination. Finally,taking D Iron Mine as a case study,the result shows the validity of the proposed method. Taking into consideration of the resource utilization and energy consumption,the proposed method provides a scientific and feasible idea for grades optimization.
The method of dynamic optimization of mining & milling grades is proposed under the constraints of the resource utilization and energy consumption,by integrating nonlinear goal programming( NGP) model and the intelligent algorithms of differential evolution( DE) and artificial neural network( ANN). First,the nonlinear goal programming model is established,with cut-off grade and dressing grade as decision variables,and concentrate production,resource utilization rate,total electricity consumption and economic benefit as goal constraints. The NGP model contains three nonlinear functions which are loss rate function,milling metal recovery rate function and the cost function. Then the NGP model is transformed into unconstrained optimization problem. Considering the advantage optimization ability of DE and modeling function of ANN,the DE and ANN are combined to the DE-ANN algorithm,to search the optimal grades combination. Finally,taking D Iron Mine as a case study,the result shows the validity of the proposed method. Taking into consideration of the resource utilization and energy consumption,the proposed method provides a scientific and feasible idea for grades optimization.
引文
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[2]Ataei M,Osanloo M.Using a combination of genetic algorithm and the grid search method to determine optimum cutoff grades of multiple metal deposits[J].International Journal of Surface Mining,Reclamation and Environment,2003,18(1):60-78.
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[13]邵安林.五品联动模式下品位决策问题建模与优化[J].中国管理科学,2013,21(6):139-144.
[14]潘贵豪,明世祥,徐兰军.无底柱分段崩落法放矿模型研究及应用[J].矿业研究与开发,2014,34(1):4-7.
[15]刘文胜,李铁钢,李克庆,刘保顺,袁怀雨.“五品联动”矿冶工程管理优化决策支持系统研究及应用[J].金属矿山,2015,(12):1-4.
[16]Storn R,Price K.Differential evolution:a simple and efficient heuristic for global optimization over continuous spaces[J].Journal of Global Optimization,1997,11(4):341-359.
[17]Das S,Suganthan P N.Differential evolution:a survey of the state-of-the-Art[J].IEEE Transaction on System,Man and Cybernetics-Part B:Cybernetics,2011,15(1):4-31.
[18]Mc Clelland J L,Rumelhart D E.Explorations in parallel distributed processing,a handbook of models,programs and exercises[M].Cambridge,MA:MIT Press,1989.
[19]Rumelhart D E,Hinton G E,Williams R J.Learning representations by back-propagating errors[J].Nature,1986,323(9):533-536.
[20]Broomhead D S,Lowe D.Multi-variable functional interpolation and adaptive networks[J].Complex Systems,1988,(2):321-355.
[21]Moody J,Darken C.Fast learning in networks of locally-tuned processing units[J].Neural Computation,1989,1(2):281-294.
[22]He Y,Zhu K,Gao S.Theory and method of geneticneural optimizing cut-off grade and grade of crude ore[J].Expert Systems with Applications,2009,36(4):7617-7623.
[23]Jang J S R,Sun C T.Functional equivalence between radial basis function networks and fuzzy inference system[J].IEEE Transactions on Neural Networks,1993,4(1):156-159.
[2]Ataei M,Osanloo M.Using a combination of genetic algorithm and the grid search method to determine optimum cutoff grades of multiple metal deposits[J].International Journal of Surface Mining,Reclamation and Environment,2003,18(1):60-78.
[3]Minnitt R C A.Cut-off grade determination for the maximum value of a small wits-type gold mining operation[J].The Journal of the South African Institute of Mining and Metallurgy,2004,(6):277-283.
[4]Bascetin A,Nieto A.Determination of optimal cut-off grade policy to optimize NPV using a new approach with optimization factor[J].Journal of the South African Institute of Mining and Metallurgy,2007,107(2):87-94.
[5]Asad M W A,Topal E.Net present value maximization model for optimum cut-off grade policy of open pit mining operations[J].Journal of the South African Institute of Mining and Metallurgy,2011,111(11):741-750.
[6]Azimi Y,Osanloo M.Determination of open pit mining cut-off grade strategy using combination of nonlinear programming and genetic algorithm[J].Archives of Mining Sciences,2011,56(2):189-212.
[7]Azimi Y,Osanloo M,Esfahanipour A.An uncertainty based multi-criteria ranking system for open pit mining cut-off grade strategy selection[J].Resources Policy,2013,38(2):212-223.
[8]Rahimi E,Ghasemzadeh H.A new algorithm to determine optimum cut-off grades considering technical,economical,environmental and social aspects[J].Resources Policy,2015,46(12):51-63.
[9]袁怀雨,刘宝顺,李克庆.合理入选品位整体动态优化[J].北京科技大学学报,2002,24(3):239-242.
[10]姜谙男,赵德孝,孙豁然.矿石入选品位优化决策支持系统的开发[J].矿业研究与开发,2003,23(4):43-45.
[11]余伟建,刘爱辉.金属矿山入选品位动态优化研究[J].中国钨业,2004,19(3):27-30.
[12]姚旭龙,胡乃联,刘大斌,李国清.基于异构数据融合技术的地下矿生产计划优化[J].金属矿山,2012,(11):116-120.
[13]邵安林.五品联动模式下品位决策问题建模与优化[J].中国管理科学,2013,21(6):139-144.
[14]潘贵豪,明世祥,徐兰军.无底柱分段崩落法放矿模型研究及应用[J].矿业研究与开发,2014,34(1):4-7.
[15]刘文胜,李铁钢,李克庆,刘保顺,袁怀雨.“五品联动”矿冶工程管理优化决策支持系统研究及应用[J].金属矿山,2015,(12):1-4.
[16]Storn R,Price K.Differential evolution:a simple and efficient heuristic for global optimization over continuous spaces[J].Journal of Global Optimization,1997,11(4):341-359.
[17]Das S,Suganthan P N.Differential evolution:a survey of the state-of-the-Art[J].IEEE Transaction on System,Man and Cybernetics-Part B:Cybernetics,2011,15(1):4-31.
[18]Mc Clelland J L,Rumelhart D E.Explorations in parallel distributed processing,a handbook of models,programs and exercises[M].Cambridge,MA:MIT Press,1989.
[19]Rumelhart D E,Hinton G E,Williams R J.Learning representations by back-propagating errors[J].Nature,1986,323(9):533-536.
[20]Broomhead D S,Lowe D.Multi-variable functional interpolation and adaptive networks[J].Complex Systems,1988,(2):321-355.
[21]Moody J,Darken C.Fast learning in networks of locally-tuned processing units[J].Neural Computation,1989,1(2):281-294.
[22]He Y,Zhu K,Gao S.Theory and method of geneticneural optimizing cut-off grade and grade of crude ore[J].Expert Systems with Applications,2009,36(4):7617-7623.
[23]Jang J S R,Sun C T.Functional equivalence between radial basis function networks and fuzzy inference system[J].IEEE Transactions on Neural Networks,1993,4(1):156-159.
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