1. SEPARATION DESCRIBED AS CLASSIFICATION
quality vs. value of main feature
(for one or different products)

2. Elements of analysis of separation
Components: feed [e.g. ], products [e.g. ], primary component [e.g. fraction], secondary components [eg.mineral]
Features: quality, quantity, value of main feature C [e.g size], other features ($ value, magntic field, etc.)

3. separation other features main value
(is based on components, their features and field, space, time)
main
value
Components properties (features)
other features

4. upgrading separation quality vs. quantity (+ name) for one component
main
value
quality vs. quantity (+ name)
for one component
TiO2
(+ other features)
quality vs. qunantity (+ name)
for many or all components (names)

5. quality vs. value of the main feature
classification
separation
quality vs. value of the main feature
(for all components =fractions) (names are not used) (one quantity)
value
(+ other features)
quality vs. value of the main feature for all components and different quantities of products

6. Determination of yields - useful for calculation of such parameter as recovery

7. feature value
quantity
quality

8. Classification balance

9. feed, A, B   feed, A, B c ,  - constant
Classification curves
A. Principal curves
feed, A, B 
 feed, A, B
,  - constant c

10. Classification: l=f(c)
Frequency curves - histograms
Classification: l=f(c)

11. Classification: l=f(c)
Frequency curves
Classification: l=f(c)

12. Classification: l=f(c) No separation
Frequency curves
Classification: l=f(c)
No separation

13. Classification: l=f(c) Ideal separation
Frequency curves
Classification: l=f(c)
Ideal separation

14. Classification: l=f(c) Real separation
Frequency curves
Classification: l=f(c)
Real separation

15. Distribution curves
Classification: Sl=f(c)

16. Distribution curves -no separation
Classification: Sl=f(c)

17. Distribution curves -real separation
Classification: Sl=f(c)

18. Distribution curves -ideal separation
Classification: Sl=f(c)

19. Partition curves
Classification: e=f(c)

20. Partition curve
Classification: e=f(c)

21. Characterization of partition curve:
d50 and Ep, O, N or others
Ep=probable error = (c=75%- c=25%))/2
O = sharpness of separation = c=75%/c=25%
N = slope of linear part of the curve