CB Omni Agile 在线元素跨带式分析仪
CB Omni Agile 在线元素跨带式分析仪
Thermo Scientific™

CB Omni Agile 在线元素跨带式分析仪

使用 PGNAA 或 PFTNA 在线元素分析器对工艺优化进行实时质量控制。
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货号 CBOMNIAGILE
价格(CNY)
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Thermo Scientific™ CB Omni Agile 在线元素跨带式分析仪是一种灵活的工艺优化工具,可生成实时信息,用于监测矿场级和工厂进料、材料分拣和混合以及堆场控制。CB Omni Agile 采用行业当先的瞬发 γ 中子活化分析 (PGNAA) 或脉冲快热中子活化 (PFTNA) 技术,可对整个原料工艺流的一系列元素进行每分钟均匀且精准的测量。现代模块化设计确保了出色的可配置性,同时使元素分析仪轻便且易于安装。使用 CB Omni Agile,您可消除采样和手动分析,获得新水平的过程控制效率,并实现具吸引力的投资回报。

CB Omni Agile 可提供精准的实时元素分析,让您以出色的效率对工艺流进行监测、分拣和混料。主要优势包括:

  • 具有良好的可配置性,以满足单独的过程控制要求,可在广泛的应用中实现理想性能
  • 实时、精准和统一的测量可实现及时的过程干扰检测并达到具响应性且精准的过程控制
  • 可选择一个、两个或四个大容量 Nal 闪烁伽玛射线探测器,无需额外的放射源,即可实现良好的精密度
  • 放射源灵活性(放射性同位素或中子发生器),无论市场波动和短缺如何,都能为每个工艺提供安全、高性价比的解决方案
  • 轻便的模块式设计,易于安装和搬迁
  • 提供了行业特定软件包的多种选择,可实现相关数据展示和简化数据使用

PGNAA 分析破碎后物料的优势

料场给矿成分波动范围较大,因为某些材料可能是非常理想的,而其他材料则低于可接受的等级。通过 PGNAA 在线分析,在将矿料传到轧机前可获得给矿数据。此信息可反馈给矿场或卡车派单,以便在必要时采取纠正措施。在矿场到轧机的核算中使用时,PGNAA 提供了一个调控依据,可以更容易地将矿山给矿品位与特定情况联系起来,而无需考虑磨矿浮选进料等级评估所带来的滞后时间或堆场稀释问题。它揭示了从矿山到破碎机的矿石品位变化情况,从而可以采取措施减少变化,从而确保为工厂提供更稳定的给料。

PGNAA 分析引入了以下几个独特优势:

  • 使用 PGNAA 可提供比卡车或铲传感器更精细的分辨率
  • 对矿场的快速反馈有助于对路线错误或品位控制采样错误做出快速响应,从而优化运营
  • 生产效益上可以从潜在的错误处理的废物和矿石的数据中获得,这些废物和矿石可能会无意中进入库存——这些不受欢迎的材料要么可以被回收,要么至少不被送到工厂
  • PGNAA 可对低原子序数元素进行在线分析

通过了解矿石品位变化,并允许采取措施降低变化,PGNAA 分析可以优化工厂绩效。这种改良可改进选厂的性能,降低生产成本,延长矿山开采寿命。

PGNAA 分析对轧机进料的优势

虽然 PGNAA 对原矿石进行分析可以提供物料进入磨机前的波动情况,但一旦物料进入破碎机,PGNAA分析仪仍可以对其进行成分,提供实时成分数据帮助对材料进行分级。轧机内发生的磨削是矿物解放过程中的重要第一步,但目标粒度大小该是多少往往没有明确的答案。由于超过 50% 的能耗发生在粉碎和磨削阶段,超过了理想粒度的过度磨削会产生明确的经济影响。磨机进料上的在线粒度分析有助于确定良好粒径范围,从而帮助操作员设置过程控制策略,以实现良好的磨削尺寸并尽可能提高金属产量。

  • 粒度分析仪可为工厂提供轧机进料数据,以供工艺控制使用,从而在浮选进料前调整运行设定点
  • 通过轻元素分析来追踪煤矸石矿物,从而对煤矸石控制策略作出决定/采取行动
  • 对于月末的矿场/轧机调节,与试图调节矿山和浮选给矿相比,对轧机给矿进行 PGNAA 分析提供了一个更可证明的问题点。(在前者情况下,问题往往很难确定,而且常常认为是浮选进料取样器或称重仪的问题,需要花费时间和资源来证明或反驳这种推论。)

利用数据找到粒径与回路吞吐量之间的平衡,从而限制磨机的消耗并大幅度地提高金属产量,这对于限制能源成本和优化工厂输出至关重要。磨机进料的 PGNAA 分析可提供这条具优势的信息。

规格
描述CB Omni Agile 在线元素分析器
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常见问题解答 (FAQ)

Do you have any tips on determining ROI to justify an investment in online elemental analysis?

Justifying any decision around online analysis requires careful and fair consideration of the associated upside.

For a decision around dedicated sampling/analysis stations for a new build, we recommend making a conservative estimate of savings in materials and not underestimating the value of time. A few months' saving on the construction timeline translates directly into an earlier swing from expenditure to income, an inflection point that cannot come soon enough for most projects.

For process control, consider the current situation to determine the magnitude of possible gains. For example:

- What is a recovery improvement worth for your process?

- If you could reduce impurities in the concentrate, what would that mean for selling price?

- What’s the difference in flotation reagent consumption, best to worst current case? What would be the savings if you consistently hit the best case?

- How much are you overmilling over milling to avoid overly coarse material exiting the grinding circuit? What would be the energy savings if you weren’t? Online analysis should pay its way, and easily, so calculations such as these should readily highlight optimal areas for economic implementation and provide evidence to support investment.

How does real-time online elemental analysis data assist in stabilizing grinding circuit and flotation plant operations?

In the grinding circuit, under-grinding typically means poor metal recovery (mineral processing) or sub-standard product (cement). Over-grinding, on the other hand, drives up energy consumption and results in undesirable levels of fines. Milling just enough, balances these competing impacts. Online real-time particle sizing analysis makes it possible both to identify an optimal setpoint for particle size and then reliably maintain it.

In a flotation plant, there is an analogous balance to establish. Poor separation means excessive metal loss while excessive reagent addition is expensive and environmentally undesirable. Here, real-time elemental analysis can provide the information needed to identify the operational sweet spot and optimal control in the face of changes in particle size, ore mineralogy, and pulp density.

In both cases, with real-time data, changes tend to be more frequent but smaller, i.e., the plant stabilizes, with automated control minimizing variability.

Why is high availability of assay data necessary for effective process control in elemental analysis?

If you are aiming for automated process control, then that is only practical with high availability and 95% should be an absolute minimum. Otherwise, switching to and from manual process control will be arduous and problematic with respect to operational efficiency. If availability is not demonstrably high, then operators cannot rely on an analyzer, whether control is manual or automated, and it never becomes an integral part of the control architecture.

How important is measurement interval for process control with on-line elemental analyzer data? How can I determine how often to sample/measure?

When implementing online analysis, there are two key questions to consider: What can I measure? And what can I control to affect that measurement?

Let’s take grinding circuit control as an example. A measurable variable is the particle size of the exiting material, and it can be controlled by parameters such as mill throughput and speed of rotation. How often to measure is then the next question. With manual control, a large interval between measurements inhibits an operator’s ability to adjust the process effectively. There is a long lag between taking action and seeing the result. Increasing measurement frequency, to the limit of real-time measurement, improves feedback allowing the operator to learn how to 'steer' the circuit more effectively. The result will be steadier operation with an automated, well-tuned control loop, the best solution for driving variability to a minimum.

If you can measure and tightly control a vital characteristic of a key stream, in a grinding or flotation circuit, or elsewhere on the plant, then the rewards can be substantial. If you can’t influence a measurable parameter, then there is far less impetus to measure it at all, or with any frequency, though measurement may still be valuable for upset monitoring. Focus on how you would use data if you had it to identify the best places for investment and the frequency of measurement that will be most useful.

What are the operational pros and cons of multi-stream and dedicated analyzers for slurries?

Well-designed dedicated analyzers require only minimal cleaning and maintenance for reliable operation over the long-term. The sample transport associated with centralized systems, on the other hand, requires the addition of pumps and small-bore sample lines adding additional complexity and failure points to the system. These have potential to affect data availability and cost of ownership due to the maintenance, running costs, and emissions associated with pump operation. Such systems are often installed with good intentions and a sound understanding of the practice required to keep them in good working order, but over the years, enthusiasm and rigor have tended to dwindle. Abandoned lines are common with centralized analyzers, an important point to note when assessing upfront CAPEX.

The other significant difference between multi-stream and dedicated analyzers is measurement frequency. For streams that justify real-time measurement, or as close as is feasible, dedicated analyzers are unbeatable.