Advanced Modeling and Control
Explore cutting-edge techniques in advanced control, including the design and implementation of advanced process control methods in dynamic and multivariable industrial processes using MATLAB’s Simulink tool. Dive into the world of advanced modelling, discovering tools for data analysis, empirical modelling, and process design to optimize the performance and efficiency of process equipment.
Course Overview
This course builds upon introductory process control course (CHEN3005: Process Instrumentation and Control). It introduces advanced modeling and control techniques. The course aims to provide broad overview of various topics and provide foundational knowledge for further exploration of the subject. While not comprehensive, it covers essential material, and current trends.
Most of the material is designed to be easily understood by undergraduate chemical engineering student. However, some background in mathematics, including differential equations, as well as a solid understanding of material and energy balance concepts and unit operations.
The unit is divided into two parts: advanced control, and advanced modeling. In the advanced control section, we delve into the complexities of industrial processes, which are dynamic and often multivariable. To effectively control such processes, advanced techniques beyond simple feedback loops studied in the Process Instrumentation and Control unit are required. The key focus of this section is on the design and implementation of advanced process control methods in process plants. Additionally, we will explore the utilization of Simulink, a MATLAB tool, for controller design and simulation.
In advanced modeling section, we will address the crucial role of mathematical modeling in the design and optimization of process equipment. This section equips students with modeling tools for data analysis, empirical modeling, and process design. These tools are essential for understanding and representing the behavior of complex processes, aiding in optimization efforts and decision-making. This year, we will focus on data driven models and also explore some aspects of machine learning and its applications to statistical process control.
By combining the two components, students will gain the necessary skills to tackle the challenges of dynamic and multivariable industrial processes, enabling them to understand, and design effective control strategies and optimize process performance.
What do process control engineers do?
Learning Outcomes
- Apply a range of advanced control techniques to chemical, mineral and petrochemical processes.
- Explain multi-loop control design, including pairing and decoupling of chemical processes.
- Apply a range of advanced model formulation and solution techniques to chemical processes.
- Demonstrate the effective use of software, such as MATLAB for process modeling and for control system design.
Teaching Staff
A/Prof. Ranjeet Utikar
- Building 204, Room 432
Bentley Campus - r.utikar@curtin.edu.au
- +61 08 9266 9837
- Schedule an appointment
Contacting me
Best way to contact is via email. I hope to respond within 24-36 hours. If you would like to see me, please book an appointment using “schedule an appointment” link on the left.
A/Prof. Ranjeet Utikar
- Building 204, Room 432
Bentley Campus - r.utikar@curtin.edu.au
- +61 08 9266 9837
- Schedule an appointment
Contacting me
Best way to contact is via email. I hope to respond within 24-36 hours. If you would like to see me, please book an appointment using “schedule an appointment” link on the left.
Ms. Ruby Lo
- Faculty of Science and Engineering
Bentley Campus - TeachingSupport-WASM-MECE@curtin.edu.au
Contacting me
Best way to contact is via email. I hope to respond within 24-36 hours. If you would like to see me, please book an appointment.