CB Omni Agile Online Elemental Cross-Belt Analyzer
CB Omni Agile Online Elemental Cross-Belt Analyzer
Thermo Scientific™

CB Omni Agile Online Elemental Cross-Belt Analyzer

Perform real-time quality control for process optimization using a PGNAA or PFTNA online elemental analyzer.
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Catalog NumberDescription
CBOMNIAGILECB Omni Agile Online Elemental Analyzer
Catalog number CBOMNIAGILE
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Description:
CB Omni Agile Online Elemental Analyzer

The Thermo Scientific™ CB Omni Agile Online Elemental Cross-Belt Analyzer is a flexible process optimization tool that generates real-time information for monitoring of mine grade and plant feed, material sorting and blending, and stockpile control. Utilizing industry-leading Prompt Gamma Neutron Activation Analysis (PGNAA) or Pulsed Fast Thermal Neutron Activation (PFTNA) technology, the CB Omni Agile performs minute-by-minute uniform and precise measurement of an array of elements for entire raw material process streams. A modern, modular design ensures exceptional configurability, simultaneously making the elemental analyzer lightweight and easy to install. Use the CB Omni Agile to eliminate sampling and manual analysis, access new levels of efficiency in process control, and achieve an attractive return on investment.

By delivering precise real-time elemental analysis, the CB Omni Agile allows you to monitor, sort and blend process streams with exceptional efficiency. Key features include:

  • Substantial configurability to meet individual process control requirements for optimal performance across a wide range of applications
  • Real-time, precise, and uniform measurement for timely process upset detection and responsive, accurate process control
  • Choice of one, two or four large volume Nal scintillation gamma ray detectors for superior precision without the need for an additional source
  • Source flexibility(radioisotope or neutron generator)for a safe, cost-effective solution for every process regardless of market fluctuations and shortages
  • Lightweight, modular design for easy installation and relocation
  • Comprehensive choice of industry-specific software packages for relevant data presentation and streamlined data use

Advantages of PGNAA analysis on stockpile feed

Stockpile feed can be highly variable, with some material being highly desirable while other material is below an acceptable grade. With PGNAA online analysis, data about the feed can be obtained before the material reaches the mill. This information can be fed back to the mine or the truck dispatch, allowing corrective action to be taken if necessary. When used in mine-to-mill accounting, PGNAA provides a point of reconciliation that can more easily be related back to a given situation with mine ore feed grades, without the issues of lag time or stockpile dilution that can come with mill flotation feed grade evaluation. It illuminates the variability of the ore grade from mine to mill, allowing action to be taken to reduce variability and thereby ensure a more stable feed supply to the plant.

PGNAA analysis introduces several distinct advantages:

  • Use of PGNAA provides finer resolution than truck or shovel sensors
  • Rapid feedback to the mine helps to optimize operations by allowing quick response to mis-routings or grade control sampling errors
  • Production economics can benefit from data provided on potentially mishandled waste and ore that might be inadvertently routed into the stockpile—such undesirable material could either be recovered or, at the very least, not sent to the plant
  • PGNAA makes possible tracking of gangue minerals through light element analysis

By providing an understanding of the variability of the ore grade from mine to mill, and allowing action to be taken to reduce variability, PGNAA analysis can optimize a plant’s performance. Such refinements can impact the performance of the concentrator and influence the production costs of the final product, improving the business model for the life of the mine.

Advantages of PGNAA analysis on mill feed

While stockpile feed analysis by PGNAA reveals variations in material prior to that material reaching the mill feed, once material reaches the mill feed, further analysis can be performed to determine the optimum particle range size for a given mill feed grade. The grinding that happens in the mill is an essential first step in mineral liberation, but often there is no clear understanding of what the target particle size should be. With more than 50% of energy consumption coming at the crushing and grinding state, overgrinding beyond the ideal particle size rang has definite economic implications. PGNAA analysis on mill feed helps determine what the optimum particle size range should be, thus helping operators set up process control strategies to achieve optimal grind size and maximize the metal yield.

  • PGNAA analysis provides mill feed data to the plant for process control purposes, allowing operating setpoints to be adjusted prior to the Flotation feed
  • Tracking of gangue minerals through light element analysis is made possible, thus allowing decisions/actions on gangue control strategies
  • For end-of-month mine/mill reconciliation purposes, having PGNAA analysis on the mill feed provides a much more "provable" point of issue as compared to attempts at reconciling mine to flotation feed.(In the latter situation, the issue is often difficult to identify and often the flotation feed sampler or weightometers are "blamed," requiring expenditures of time and resource to either prove or disprove the accusation.)

Having the data to find the balance between particle size and circuit throughput, thus limiting consumption of grinding medica and maximizing metal yields, is crucial to limiting energy costs and optimizing plant output. PGNAA analysis of the mill feed can provide this highly advantageous information.

Specifications
DescriptionCB Omni Agile Online Elemental Analyzer
Unit SizeEach

Frequently asked questions (FAQs)

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.