现场与死后过程控制——1-2分钟内基于libs的炉渣分析如何实现火法冶金过程的全新过程管理

Amit Ahsan, Cassian Gottlieb, Ma Chao
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摘要

资源效率和工艺优化是金属制造的主要目标。当今最先进的分析方法(例如XRF)需要繁琐的样品制备,这导致在即时决策,快速设定值调整以及过程纠正方面效率的显著损失。由于这种延迟,如果分析结果显示偏离目标值(最佳),则熔体已经是一个工艺步骤,不能再进行调整或充分处理。通过最少的样品制备,激光诱导击穿光谱(LIBS)可以在不到2分钟的时间内分析异质渣块,并在确保合理精度的同时提供更具代表性的结果。此外,由于稳健性,易于操作和所需的维护工作最少,LIBS系统可以放置在现场(例如,在炉子旁边或附近),并且可以由普通工艺人员操作。因此,不仅节省了样品制备时间,而且可以缩短传统的样品从熔炉到实验室的运输时间。因此,当熔体仍处于类似状态时,可以启动纠正措施,从而对工艺进行“原位”调整。本文探讨了LIBS技术在过程渣分析中获得代表性数据的潜力,并就离线实验室技术面临的分析挑战提供了一些有价值的见解。
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In-situ versus Post-mortem Process Control - How LIBS-based Slag Analysis Within 1-2 Minutes Enables a Completely New Process Management in Pyrometallurgical Processes
Resource efficiency and process optimization are major targets in metals making. Today’s state of the art analytical approaches for slag analysis (e.g. XRF) requires tedious sample preparation which leads to significant loss of efficiency in terms of immediate decision making, fast setpoint adjustment as well as process correction. Due to this delay, if the analysis result reveals deviations from the target value (optimum), the melt is already one process step further and can no longer be adjusted or treated adequately. With minimum sample preparation, Laser Induced Breakdown Spectroscopy (LIBS) can analyse heterogeneous slag pieces in less than 2 minutes and delivers even more representative results while ensuring reasonable accuracy. In addition, due to the robustness, easy handling and required maintenance effort being minimum, LIBS systems can be placed on site (e.g. next to or close to the furnace) and can be operated by normal process personnel. Therefore, not only the sample preparation time is saved but also the conventional sample transportation time from the furnace to the lab can be shortened. Therefore corrective measures can be already initiated while the melt is still in a comparable state resulting a ‘in-situ’ adjustment of the process. This paper explores the potentials of LIBS technology to obtain representative data in process slag analysis and sheds some valuable insight in terms of analytical challenges faced by offline laboratory technologies.
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