In view of the problems such as the relatively small proportion of feasibility and sustainability indicators in the allocation of carbon emission, and the lack of potential analysis for regions lacking carbon quotas after allocation, a study on carbon quota allocation and emission reduction potential of provinces was carried out. Under the target of carbon peaking, a multi-index system was constructed, taking into account the principles of fairness, efficiency, feasibility and sustainability. The entropy method and ZSG-DEA method were combined to obtain and optimize the quota allocation results. Based on the results of carbon emission spatial balance, emission reduction potential models were developed to evaluate the emission reduction potential under different preference scenarios. The applicability of the model is verified by analyzing the behavior of non-ferrous metals industry. The results show that the regions with a prosperous non-ferrous metals industry have a high proportion of carbon quota and significant differences in emission reduction potential, showing a distribution pattern of "high in southeast and low in northwest", with variations among provinces. The research findings can provide a way to improve the carbon quota allocation process for non-ferrous metal industry in different provinces.
The crushing process of material particles in a vertical shaft impact (VSI) crusher is highly complex. It is hard to quantify and characterize the crushing behavior of particles. In this work, an experimental setup for active impact crushing is designed and constructed to investigate the crushing behavior of particles in a VSI crusher. Impact crushing experiments are carried out on limestone particles using this device to investigate the effects of impact velocity and particle size on the crushing probability as well as the size distribution of the crushed products. The results show that the crushing probability of particles is related to the impact energy and particle size, and the strength of particles increases with the decrease of particle size. The tn-t10 particle size distribution prediction model is established through regression analysis of crushing experimental data, and a family of tn curves is constructed for the particle size distribution of crushed products, and the experimental validation of the particle size distribution prediction model is able to predict the size distribution of the crushed products very well. This study offers a theoretical foundation for optimizing material crushing in VSI crushers.
In the context of the dual carbon strategy, efficient recyclingRecycling and utilization of waste heat from aluminum electrolysisAluminum electrolysis cellsCell is one of the implementation paths for the aluminum electrolysisAluminum electrolysis industry to achieve deep energyEnergy conservation and carbon reduction. In response to the technical bottleneck problem of difficult recoveryRecovery of waste heat on the side of aluminumAluminum electrolytic cellsCell, a dedicated heat exchanger for waste heat recoveryWaste heat recovery on the side of the electrolytic cellCell has been developed. The flow rate of the heat transferring medium in the heat exchanger has been optimized and controlled, and the heat extraction rate has been adjusted to ensure stable operationOperation of the electrolytic cellCell without affecting it. The radiant heat on the side of the electrolytic cellCell shell has been efficiently recovered. The technology was tested in an industrial scale on a 200 kA test cellCell. The results showed that the overall operationOperation of the aluminum electrolysisAluminum electrolysis cellCell waste heat recoveryWaste heat recovery system was stable and efficient, with an average heat recoveryRecovery of 77 734 kJ/h, equivalent to 21.59 kWh, 317 kWh per ton of aluminumAluminum, and a heat recovery efficiencyHeat recovery efficiency of 99.71
智能技术作为引领性的高新技术,包含许多发展快速的新兴领域,具有自感知、自学习、自决策、自适应的能力,可以适应复杂多变的工业环境,已经在工业领域中解决了许多传统方法难以解决的问题.矿物加工领域作为人员密度大、劳动力强、工艺过程复杂的传统基础工业,在企业精细化管理和安全高效生产等方面具有很大提升空间.随着全球各行业智能技术的不断发展和迭代,未来传统选矿领域必将朝着高效、智能化和可持续的方向发展.目前,针对矿物加工过程智能化的研究取得了一定进展,但各种智能技术的应用处于初步探索和验证阶段,不同矿物加工过程的相关智能技术和数据分析方法研究较为分散和杂乱,缺乏系统性总结.为此,在详细分析了智能技术在矿物加工工艺流程中的应用原理和方式的基础上,首次依据智能化差异及数据复杂程度将矿物加工领域的智能技术归纳为自动化控制、神经网络、大数据开发三大类.系统性总结了各智能技术在破碎、磨矿、浮选、磁选等矿物加工过程的研究和应用发展现状,揭示了矿物加工过程中智能技术对品位提高、指标预测和流程稳定的重要意义,可为矿物加工领域的智能化、自动化、信息化研究提供一定帮助.
结合大红山铁矿三选厂铜浮选工艺流程和选矿工艺学特点,并为解决浮选现场人工控制不及时,以及出矿速率稳定性、回收率指标有待提高等问题,设计和开发了一套铜浮选智能化系统.该系统融合了 PID控制技术、模糊逻辑控制技术以及泡沫影像分析技术,主要由泡沫影像系统、专家处理系统、人机交互界面三部分组成.系统结构简明合理,各组成部分间,以及系统与现场PLC控制柜之间数据通讯采用成熟稳定的OPC通讯协议交互完成,控制逻辑为泡沫速率控制逻辑,即通过设定目标泡沫速率,系统智能化协调控制各浮选槽进气量和泡沫层厚度等参数变化,使得各槽泡沫速率尽可能稳定在目标泡沫速率周围,来实现出矿速率稳定,并最终使回收率等指标稳定和提升.结合工厂实际需求和智能化系统重点关注优化原则,本系统现主要在三选厂铜浮选两个系列工艺流程的粗扫选段实施,以常用深部铜系列为例,系统投入运行一段时间后,相比投入之前,确实可以实现稳定泡沫速率,提高回收率,并减少班组间回收率偏差波动等指标优化.其中铜浮选流程粗选泡沫速率稳定性相对提高30.15％,铜理论回收率提高1.01个百分点,四个班组间铜回收率最大偏差值减少1.26％,这表明本铜浮选智能化系统运行效果良好,能带来较大的潜在经济效益,值得在国内推广和应用.
