【作者】 段炼；

【导师】 王晓丽；

【作者基本信息】 中南大学 ， 工程（专业学位）， 2022， 硕士

【摘要】 磨矿分级过程是选矿生产最重要的环节之一,是保障后续浮选环节质量的基础。稳定磨矿分级回路产品质量并提高回路的生产效率一直是选矿厂和工程师们关注的焦点。优化操作设定值是满足生产质量要求和提高生产效率的重要突破口。由于磨矿分级过程工艺机理复杂,使其建模和优化十分困难,此外多变的环境因素,造成磨矿分级过程不确定因素很多。因此,本文研究基于多目标优化的磨矿分级过程操作变量优化设定问题,主要工作和创新如下:(1)针对磨矿分级过程建模困难的问题,建立了基于响应曲面法的磨机功率模型,解决了传统功率建模依赖一些不可测变量的问题;同时,对基于物料总体平衡方法的磨矿和分级过程模型进行了校准。实际生产数据验证结果表明了模型具有较高的精度,为构建多目标优化模型提供基础。(2)建立了最大化一段溢流产品质量和最大化磨机功率的磨矿分级过程确定多目标优化模型。提出了改进的NSGA-Ⅱ算法求解模型,得到分布较均匀的解集。采用多属性决策法获得操作变量的优化设定值。实际生产数据仿真结果表明,溢流产品细度、磨机给矿量和磨机功率相比人工操作平均提高了2.53%、3.83t/h和40.46k W。(3)针对磨机入磨矿石粒度分布波动带来的不确定性问题,结合模糊理论,建立了基于可信性测度的磨矿分级过程模糊机会约束规划(FCCP)模型。实际生产数据仿真结果表明,在确定优化的基础上,不确定优化将溢流产品细度、磨机给矿量和磨机功率进一步提高了0.99%、3.73t/h和5.96k W。针对可信性测度忽略决策者偏好信息的问题,对传统处理模糊事件的方法进行改进,提出了基于m_λ测度的磨矿分级过程FCCP模型。仿真结果表明在确定优化的基础上,乐观决策者将溢流产品细度进一步提高了3.81%,悲观决策者将磨机给矿量进一步提高了6.19t/h。图43幅,表15个,参考文献83篇更多还原

【Abstract】 Grinding-classification process is one of the most important links in beneficiation production and the basis for ensuring the quality of subsequent flotation links.Stabilizing the product quality of grinding-classification circuit and improving the production efficiency of the circuit have always been the focus of concentrators and engineers.Optimizing the set points of operation is an important breakthrough to meet the requirements of production quality and improve production efficiency.Due to the complex process mechanism of grinding-classification process,it is very difficult to model and optimize it.In addition,there are many uncertain factors in grinding-classification process due to changeable environmental factors.Therefore,this paper studies the optimal setting of operating variables in grinding-classification process based on multi-objective optimization.The main research contents and innovations are as follows:(1)Aiming at the difficulty of modeling the grinding-classification process,a mill power model based on response surface method is established,which solves the problem that the traditional power modeling depends on some unmeasured variables.Furthermore,the grinding and classification models based on the population balance method are calibrated.The verification results of actual production data show that the model has high accuracy,which provides a basis for building a multi-objective optimization model.(2)A certain multi-objective optimization model for the grinding-classification process is developed to maximize the quality of primary overflow products and the power of the mill.An improved NSGA-Ⅱ algorithm is then proposed to solve the model,and a more evenly distributed solution set is obtained.The multi-attribute decision-making method is used to obtain the optimal setting value of operation variables.The simulation results show that the fineness of overflow products,the mill feed and the mill power are increased by 2.53%,3.83t/h and 40.46k W on average compared to manual operation.(3)Aiming at the uncertainty caused by the fluctuation of grinding stone particle size distribution,combined with fuzzy theory,a grinding-classification process fuzzy chance constrained programming(FCCP)model based on credibility measure is proposed.The actual production data are used for simulation.The results show that the fineness of overflow product,mill feed and mill power are increased by 0.99%,3.73t/h and5.76k W on average compared to deterministic optimization.Aiming at the problem that the credibility measure ignores the preference information of decision makers,the traditional method of dealing with fuzzy events is improved,and a FCCP model for grinding-classification process based on m_λmeasure is proposed.The simulation results show that on the basis of determining the optimization,the optimistic decision-maker further increases the fineness of overflow products by 3.81%,and the pessimistic decision-maker further increases the mill feed by 6.19t/h.更多还原