Theory And Method Of Intelligent Optimizing Cut-off Grade And Grade Of Crude Ore

Every period of mining and milling in mine system should control the grades of ore. Thegrades of ore contain geological grade, the boundary grade, cut-off grade, grade of crude ore,concentrate grade and so on, and they relate to geological exploration, mining, milling and manyother departments, involving many factors, such as the geological conditions, mining technicalconditions and technical and economic conditions of milling and smelting. Many experts andscholars continue to explore the scientific method for decision-making, the main objectives are tomake full use of resources and to maximize the revenue, which need to find out the optimalcut-off grade and grade of crude ore. Cut-off grade is the grade of ore in the last time of oredrawing during sublevel caving with no sill pillar. If the cut-off grade is low, the dilution rate ofore will be high. This will increase the cost of ore processing, and will decrease the amount of theconcentrate in the condition of a certain mineral processing ability. Contrarily, high cut-off gradewill not only lead to waste of mine resource, but also increase the cost of fundamentalconstruction. Grade of crude ore plays a role as a link between mining and milling. It isdetermined by the average grade of ore bodies, cut-off grade and dilution rate. And it affects themilling recovery and concentrate grade. The optimum grade of crude ore can improve the benefitof mine and make use of mine resource. Therefore, reasonable determination of cut-off grade andgrade of crude ore is crucial to the economic benefit of enterprise and sustainable utilization ofresource, and it has important theoretical and practical meaning.Because of the particularity of production environment, multiplicity of ore drawing andextension of mine management, in mine enterprise with sublevel caving with no sill pillar,generally, cut-off grade and grade of crude ore are determined by experiment data or worker'sexperience. Along with the increase of resources' scarcity, enhancement of technical andmanagement level, the experts and managers gradually realize that, the determining methodbased on workers' experience, easy and feasible though, greatly increases the mining and millingcost, and wastes resource seriously. With the difference in geological grade and the improvementin production technology, management technology and market economic condition, it is verynecessary to set up mathematical models and relative systems from cut-off grade and grade ofcrude ore to economic benefit and utilization benefit of resource, in order to dynamicallyoptimize cut-off grade and grade of crude ore. This dissertation is supported by the Project "Research on the optimization of cut-off gradeand production dynamic management in metal mine", which originated from Daye Iron Mine inWuhan Iron and Steel Group Co. Ltd. It mainly resolves two questions. The first one is that, howto determine the cut-off grade for drawing and grade of crude ore, which meet the givenconcentrate grade, not only make the revenue of mining and milling maximum, but also take intoaccount the sustainable utilization of resource? The second one is that, when the optimal cut-offgrade and grade of crude ore are given, how to dynamically control the amounts and grades ofmining ore, milling ore and concentrate ore? So this research aims to resolve the problem ofoptimization and management of cut-off grade and crude ore in metal mine, to provide thescientific method and means for solving the series of questions, in order to upgrade the utilizationof mineral resources, and improve enterprises' economic benefit.This dissertation mainly researches on the theories and methods of intelligent optimizing twokey grades, namely cut-off grade and grade of crude ore in mining and milling system, byartificial neural network(ANN), fuzzy systems(FS), evolutionary computation(EA) and so on,and take Daye Iron Mine as case studies. Its main contents are as follows.(1) Propose a novel method that uses numerical simulation to acquire the value of the cut-offgrade in a certain period, use particle swann optimization for structure identification andparameter estimation of the function of loss rate, then construct the function relationship ofcut-off grade and loss rate. Due to the lack of record data of cut-off gradeα_j in practicalproduction, we can't acquire expressionφ(α_j).We know from the apriori information: Thecut-off grade value is normally distributed in certain universe of discourse; the greater cut-offgrade is, the greater loss rate will be, and vice versa. It can be concluded that the distributionfunction of cut-off grade value is f(α_j)= 1/(2π)e^(a_j-18)2/2 , where, the mean is u = 18, variance isσ= 1. The numerical simulation algorithm of cut-off grade is proposed, the convergence proofis given, and we successfully obtain cut-off grade values from January 2005 to November 2007in Daye Iron Mine. The Particle Swarm Optimization is used for structure identification andparameter estimation of math model between loss rate and cut-off grade, and the loss ratefunction isφ=1.6508α_j -0.1175, which is the basis of optimizing cut-off grade and grade ofcrude ore.(2) Evolutionary algorithm and neural network are nested to be a EA-ANN integrated model,and use it to optimize the cut-off grade and crude ore grade. Optimal cut-off grade and grade ofcrude ore are based on maximizing the net present value (NPV). Considering the relationshipamong profit, cost, geological reserves and all kinds of grades, we establish a nonlinear model.The main four parts of intelligent optimization are as follows. The first one is data simulation,which is used to determine relationship between cut-off gradeα_j and loss rateφ; the second isBP neural network, which is to determine relationship between the milling recovery rateεandcut-off grade a_j, crude ore gradeα_r, geological gradeα_t , geological reserves q_t ; the third one is fuzzy system, which is used for determining relationship between the cost of mining & milling Cand cut-off gradeα_j, crude ore gradeα_r, geological gradeα_t, geological reserves q_t,; the lastone is the integration of EA and ANN. The result shows that, during the period of January toNovember in the year 2007, the optimal cut-off grade is 17.8337-17.8367%, and optimal grade ofcrude ore is 46.4%. Comparing with the present scheme (cut-off grade is 18%, grade of crude oreis 41-42%), the optimized scheme can increase the amount of concentrate by 139200 tons, andimprove the net present value by 6.698 million Yuan. Compared PSO-ANN with the GA-ANNand SA-ANN algorithms, it demonstrates that the performance of PSO-ANN is obviouslysuperior to GA-ANN and SA-ANN. A simple input-output interface of grades optimization isdesigned.(3) Take account into the iron and copper, put total cost into several parts, construct thenonlinear optimization model to maximize the profit. An integration of PSO and ANNs to be aPSO-ANN integrated model, to optimize the cut-off gradeα_j and crude ore gradeα_r. Thedetails are as follows: Join cut-off grade and grade of crude ore together as particle of swarm forevolutionary computation; Make self-adaptive neural network to get the local connectionbetween the income value (fitness function) and each particle; Use PSO algorithm globally tosearch the optimal cut-off grade and grade of crude ore to maximize fitness function. Theproduction plan of Daye Mine in the year 2008 is that, geological reserves is 1.5317 million ton,geological grades are 52.60% and 0.306% for Fe and Cu respectively, through calculation, theoptimal cut-off grade and optimal grade of crude ore are 14.6848%, and 42.1388%, respectively.(4) Balancing between economic benefit and resource utilization benefit, set up multi-objectiveoptimization model of cut-off grade and grade of crude ore. Neural networks are used toconstruct mapping relationship from cut-off grade, grade of crude ore to the amount ofconcentrate, total present value and total utilization rate; then make fuzzy comprehensiveevaluation of grade combinations, put the weighted fuzzy membership value as fitness functionof genetic algorithm, finally, globally search the best grades combination that make the fitnessfunction maximum, to research dynamic optimization of cut-off grade and grade of crude ore, toassist decision-making and management for mine enterprise.(5) Divide mining and milling system into three stages, and establish functions of key indexes(amount and grade of ore). Given a certain combination of cut-off grade and grade of crude ore,we can calculate the amount of ore and the grade of ore of every stage, then we can control andmanagement each process of production. If we adopt cut-off grade 17.83%, grade of crude ore46.4% to lead practical production in the year 2008, it can be concluded as follows. In the miningstage, the average loss rate of the whole year is 18.31%, the dilute rate is 22.17%, the amount ofore recovery is 1.25123 million ton, the amount of mixed rock is 356.415 thousand ton, the totalamount and average grade of ore mining are 1.607645 million ton and 40.94%, respectively. Inthe mixing stage, if the grade of ore that should buy from outside is 35%, the amount of ore that need buy is 808.278 thousand ton. In the milling stage, the amount of ore milling is 2.415923million ton, the proportion of the amount of milling ore and concentrate ore is around 1.9877, theamount of concentrate of the whole year is 1.215436 million ton, and the grade of concentrate isaround 64.5%.