Process Control and Analytics - Advanced
This short course will cover advanced topics in process control, focusing on developing applied knowledge for mineral processing engineers
Overview: This short course will cover advanced topics in process control, focusing on developing applied knowledge for mineral processing engineers. The course is designed in four modules that can be tailored to the needs and the level of knowledge of the participants.
Module one focuses on the use of data analytics for process benchmarking and diagnosis and the development of decision support systems for operators.
Module two covers Model Predictive Control, from its basics to model building and development of soft sensors.
Module three focuses on supervisory control and a case study to represent features of supervisory control in practise.
Module four covers machine learning and artificial intelligence and their application in process control. All modules include practical examples relevant to mineral processing plants.
Target audience: This short course is aimed at both junior and senior metallurgists, and students at various levels. the course will provide an understanding of advanced process control tools and frameworks, resulting in the ability to design and evaluate different process control systems actively.
A practical approach is taken when covering all the topics in this course, with real-life case studies and best practises. The prerequisites for attendees are engineering degrees and experience in metallurgical processes (comminution, flotation, dewatering etc).