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

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.)

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

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)

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

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

feature value quantity quality

Classification balance

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

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

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

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

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

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

Distribution curves Classification: Sl=f(c)

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

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

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

Partition curves Classification: e=f(c)

Partition curve Classification: e=f(c)

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