August 2, 2010

Real-time following complex mixing process 
Manufacture has to add proteins to feed during the production process. If you cannot measure this accurately, you have to add a safe dose. This however leads to unnecessary  high operating costs. 
This can be easily solved by following the process on-line using chemometrically trained NIR. 
The graph (click to enlarge) shows how feed manufacturers add fixed amounts of protein. Using chemometrically trained NIR, however, makes it possible to exactly and accurately dose the amount of proteins.
 
Making the process more profitable and the feed more safe.
For more information: http://www.nirvention.nl/pages/welcome
 

Real-time following complex mixing process 

Manufacture has to add proteins to feed during the production process. If you cannot measure this accurately, you have to add a safe dose. This however leads to unnecessary  high operating costs. 

This can be easily solved by following the process on-line using chemometrically trained NIR. 

The graph (click to enlarge) shows how feed manufacturers add fixed amounts of protein. Using chemometrically trained NIR, however, makes it possible to exactly and accurately dose the amount of proteins.

 

Making the process more profitable and the feed more safe.

For more information: http://www.nirvention.nl/pages/welcome

 

July 26, 2010
Mining made easy with mindmapping
Analysis of copper ore and raw copper materials is extremely important in the exploration of copper mines  (or gold or any other precious metal). One of the options is to use ED-XRF. A major disadvantage of this technique is the necessary and time consuming sample preparation. Samples have to be crushed, pulverised, and as homogeneously pressed as possible. Inhomogeneity and grain size effect measurements. This can be made easier by using chemometrically trained XRF-instruments as you can see on this mindmap (click to enlarge).A coarse grinding of the copper material is all you need. This means considerably time savings on sample pretreatment. What is needed to train the XRF-instrument is the following:
weighing the samples
analysing the samples with ED-XRF
analysing the samples in a laboratory using an alternative method, e.g. AAS.
To avoid the effects of background and grain size variations, the training set should consist out of a statistically sound number of samples. All the data gathered lead to a model that is used to tune the ED-XRF to the specific analysis of copper in ore. The model is verified and validated. The instrument is now ready to analyse an almost untreated sample to deliver straight away the copper content in g/kg.
For more information: http://www.nirvention.nl/pages/welcome
Mindmap by Hans Buskes, used with permision

Mining made easy with mindmapping

Analysis of copper ore and raw copper materials is extremely important in the exploration of copper mines (or gold or any other precious metal). One of the options is to use ED-XRF. A major disadvantage of this technique is the necessary and time consuming sample preparation. Samples have to be crushed, pulverised, and as homogeneously pressed as possible. Inhomogeneity and grain size effect measurements.

This can be made easier by using chemometrically trained XRF-instruments as you can see on this mindmap (click to enlarge).

A coarse grinding of the copper material is all you need. This means considerably time savings on sample pretreatment.

What is needed to train the XRF-instrument is the following:

  • weighing the samples
  • analysing the samples with ED-XRF
  • analysing the samples in a laboratory using an alternative method, e.g. AAS.


To avoid the effects of background and grain size variations, the training set should consist out of a statistically sound number of samples.

All the data gathered lead to a model that is used to tune the ED-XRF to the specific analysis of copper in ore. The model is verified and validated. The instrument is now ready to analyse an almost untreated sample to deliver straight away the copper content in g/kg.

For more information: http://www.nirvention.nl/pages/welcome

Mindmap by Hans Buskes, used with permision

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