Who should attend
- Junior and senior metallurgists
- Honours or HDR students.
Engineering degree and experience in metallurgical processes such as comminution, flotation, dewatering, etc.
What you'll learn
- Appreciate data analytics for process benchmarking and developing decision support systems for operators.
- Understand Model Predictive Control – from its basics through to model building and the development of soft sensors.
- Examine supervisory control in practice through an in-depth case study.
- Explore the application of machine learning and artificial intelligence in process control.
This is an in-person course that will require roughly 12 hours to complete.
- 2 days of classroom teaching, 19-20 April 2023
- With the option to join remotely
There is no assessment for this course.
Certification and accreditation
Students who successfully complete the course will receive a Certificate of Completion to verify their skills and achievements.
|Individual domestic ex. GST
|Individual international (GST not payable)
|Student domestic ex. GST
|Student international (GST not payable)
Please see the Terms and Conditions (PDF, 123.12 KB).
GST is excluded from the cost
Discounts can't be used with other discounts, offers or special promotions.
We accept credit cards (Visa/MasterCard) for payment, including corporate credit cards. If you do not have access to a card, please contact our team at firstname.lastname@example.org to discuss your options.
Sir James Foots Building (#47A)
Sustainable Minerals Institute
Level 4 Seminar Room
St Lucia, QLD, 4067