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MINE517 MINERAL PROCESS ENGINEERING ANALYSIS

Course Objectives

The student will learn:

1. to apply statistical tools to process data analysis, experimental design, and empirical modeling of mineral processes;

2. to use statistical and mathematical tools to develop material balances in process circuits.  

3. basic concepts of material transport through mineral process units;

4. to formulate steady-state mechanistic models of particulate processes for computer simulation.

Course Content

Statistical techniques for process analysis; development and analysis of empirical and mechanistic models of unit operations in mineral processing; material balancing; process analysis by computer simulation.

Course Learning Outcomes

Upon successful completion of the course, the student should be able to:

1.1. Analyze process or experimental data by statistical tools.

1.2. Analyze one-factor experiments by one-way and two-way ANOVA.

1.3. Apply factorial experimental designs for fitting first-order and second-order linear-parameter models in the field of mineral processing.

1.4. Develop empirical models for particulate processes using multiple linear regression and test their adequacy.

2.1. Calculate the minimum number of streams to be sampled to ensure production of complete circuit mass balance for a complex process circuit.

2.2. Calculate consistent mass balances for a process circuit using operating data.

3.1. Describe flow patterns in mineral process vessels.

3.2. Determine the parameters of residence time distribution functions from experimental data.

4.1. Describe particle populations using property density and distribution functions.

4.2. Learn fundamentals of population balance modeling.

4.3 Learn matrix model framework and kinetic model approach to size reduction.

4.4. Learn existing mathematical models for batch and continuous flotation processes.

4.5. Use size reduction and flotation models for process simulations

Program Outcomes Matrix