HOW ON-LINE NIR SENSOR ENHANCE ORE PROCESSING AUTOMATION

Critical components of any ore processing automation strategy are the sensors that provide compositional, mineralogical, and ore property information that are essential for process optimization decisions. On-line over-the-conveyor sensors provide real-time information that is then available for feed-forward control of a wide range of ore processing operations.

Near-infrared (NIR) reflectance spectroscopy is widely used to provide mineralogic and ore property information required for ore transport, sorting, and processing decision making. The advantage of the NIR technique is the speed of measurement and the need for minimal or no sample preparation. Many of our customers have developed quantitative calibrations for a wide range of gangue minerals and ore properties; these include swelling clays, total clays, kaolinite, calcite, talc, hornblende, moisture, acid consumption, and hardness.  Analysis of geolocated blast hole chip samples using high-throughput NIR analyzers currently provides information essential for heap leach optimization and, for accurate short-term loading and hauling planning at many operating mines.

With the availability of the QualitySpec 7000 over-the-conveyor NIR sensor, these calibrations are now able to provide the real-time information necessary to implement feed-forward control of ore processing. This presentation provides an overview of the NIR method, current usage examples in operational mines, and an overview of how the QualitySpec 7000 over-the-conveyor NIR sensor enables ore processing automation.

Near-infrared (NIR) reflectance spectroscopy is widely used to provide mineralogic and ore property information required for ore processing decision making.

 

Written by: Brian Curtiss, Posted by: Malvern Panaltyical (www.materials-talks.com)

3 Things to Consider Before Analyzing a Sample

  1. Is your instrument optimized?

In a recent blog we explored the importance of instrument optimization for ICP-OES and ICP-MS, but all instruments should be frequently checked to confirm they are operating as expected. Preventive maintenance and appropriate start up and shut down SOPs are key to the integrity of analysis.

  1. How should you calibrate your instrument?

If you are following a standard method, calibration points might be recommended, however, you must also consider the expected levels of your specific samples when designing your calibration curve. Other best practices to consider include:

  • Don’t bracket your calibration curve too tightly around your samples expected levels
  • Match the matrix of your calibration standards to that of your samples to improve accuracy
  • Include a check standard in your run schedule

It is also important to be sure you have a high-quality calibration blank! Purchasing a CRM with a certificate of analysis that includes a trace scan can give you confidence in your blank.

  1. What is the best way to prepare your samples?

Top goals of sample preparation include bringing the sample to a suitable form for your instrument/ technique, ensuring sample homogeneity, and to manage potential interference’s resultant from the sample’s original form. Equally important is considering the quantity sample preparations and rate of throughput required for your lab. While there are many methods for sample analysis, some preparation techniques offer additional advantages in terms of speed and number of samples that can be prepared at once.

 

How do you prepare your instrument and samples for analysis? Share your best practices in the comments!

 

Written by: Courtney Dillon, Posted by : LGC ARMI MBH (www.armi.com)

THE IMPORTANCE OF CALIBRATING YOUR REMOTE SENSING IMAGERY

Most people using satellite or aerial imagery understand the importance of geometric calibration; you need to tie the pixels in the imagery to the corresponding coordinate locations on the earth so that you can use the imagery in mapping applications, and for analyses such as change detection.

However, there is another type of calibration that you should be doing on your optical remote sensing imagery to ensure that you are getting high quality and useful results from your images.  Accurate radiometric calibration is a critical component for successful imagery analysis.

Radiometric calibration, also known as radiometric correction, is important to successfully convert raw digital image data from satellite or aerial sensors to a common physical scale based on known reflectance measurements taken from objects on the ground’s surface.  This type of correction is important for reliable quantitative measurements of the imagery.

Each pixel in a spectral image has a signature based on the object or objects that the pixel represents as shown below:

On the left is a subset of an AVIRIS hyperspectral image that has been converted to reflectance. The spectral signature of the material associated with the pixel in the crosshairs is shown in the Spectral Profile plot on the right. ENVI software was used to display the image and the spectral plot.

You won’t see much of a difference in the image itself when correcting an image from radiance to reflectance.  The difference show up in the spectral signature associated with each pixel.

Ground Target Collection

The collection of known reflectance measurements from ground targets is performed using a field spectroradiometer like ASD’s FieldSpec 4.  To get the best results, you would try to measure the ground targets with the FieldSpec coincident with the overflight of the imaging sensor.  The collected field data is then used as input, along with the remote sensing imagery, in a software tool like ENVI to convert the radiance data to reflectance.

 

Measuring reflectance using a FieldSpec 4

What does this actually mean? 

Well, imagery typically starts out with uncalibrated, raw digital numbers (DN) for pixel values in the image.  These DN values get converted to radiance by applying a series of gains and offsets supplied by the data provider.  Imagery data that you purchase has often already been converted to radiance, or the data provider attaches some metadata with the corrections that you can apply using a remote sensing analysis package like ENVI software.

Radiance depends on the illumination (intensity and direction), orientation and position of the target feature being imaged, and the path of the light through the atmosphere.

Factors that affect radiance (Diagram from Humboldt State University [1])

The issue with radiance is that is has a lot of variability in terms of solar illumination and atmospheric effects such as water vapor in the atmosphere, so to get reliable and repeatable results, radiance typically gets converted to reflectance for image analysis.  Reflectance is the proportion of radiation striking a surface to the radiation reflected from it. With reflectance, the atmospheric variability is removed, so you get much more reliable measurements.

Spectral signatures showing radiance versus reflectance for a pure white panel, vegetation, and a dirt road. The radiance signatures show some of the main areas where atmospheric effects are a problem. [2]

Converting your imagery data to reflectance by using field reflectance measurements collected with a FieldSpec  gives you the most accurate results and greatly improves your ability to analyze your imagery, whether you are characterizing features or identifying target materials in your imagery.

If you would like more information on the FieldSpec or the benefits of calibrating your imagery, please click here

 

Written by : Ms. Susan Parks, Posted by: Malvern Panalytical (www.material-talks.com)