Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Chemical Engineering School
Students completing a dual-major in Chemical and Environmental Engineering must complete either CIVL3150 or CHEE3007. Note that CHEE2501 is a recommended pre-requisite for CIVL3150. Only students enrolled in the Chemical / Environmental Engineering dual major are eligible for CIVL3150, students from all other Chemical Engineering plans must complete CHEE3007. Mathematical process modelling for design, control and optimisation of process systems. Conservation principles, development of constitutive equations in models and analysis of resultant models for use in control and diagnosis of process faults. Model verification, calibration and validation based on process data is emphasised.
The chemical and biological processes increasingly rely on models of various types and complexities. These models play a vital role in designing, scaling up/down, optimising, and operating reactors, separators, and heat exchangers. They are also instrumental in planning and evaluating experiments, and developing a mechanistic understanding of complex systems. Examples of mathematical models commonly used in these applications include differential and integral (mass and energy) balance models, as well as algebraic equilibrium models.
In this course, we will delve into the fundamental concepts of mathematical model formulation. You will learn how to translate dynamic physical and chemical processes into mathematical language, and solve these models numerically. It is important to remember that all models are abstractions of real systems and processes. However, they serve as powerful tools for engineers and scientists to gain insights into dynamic systems.
Within the context of process engineering, mathematical modelling serves as a prerequisite for process control and the development of effective control systems. This course extensively covers these topics, exploring the open-loop (uncontrolled) dynamic behavior of chemical and biological processes. Upon completing the modelling of open-loop systems, we will introduce process control - closed loop.
In this segment, you will learn about the principles and techniques used to design and implement closed-loop control systems. These systems utilise feedback mechanisms to automatically adjust process variables, ensuring that industrial plants operate efficiently and safely. Topics covered will include PID (Proportional-Integral-Derivative) control, tuning of controllers, stability analysis, and performance optimisation. Practical examples and case studies will illustrate how closed-loop control systems are applied to maintain desired operating conditions in reactors, separators, heat exchangers, and other process equipment.
Course requirements
Assumed background
- Basic principles of physics including conservation of mass, energy and momentum plus mass and heat transfer principles.
- Elementary mathematics for engineering such as algebraic and ordinary differential equations.
- Basic matrix operations and the calculation of eigenvalues from simple square matrices.
- Basic knowledge of PYTHON. You will be tested on your competence and will be obliged to do extra work if you are not up to a sufficient standard. Modules on the Blackboard site (Under orientation week - material) will help.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
CHEE2010 and (CHEE3002 or CHEE2040) and (CHEE3003 or CHEE2030)
Incompatible
You can't enrol in this course if you've already completed the following:
CHEE3205 or CIVL3150
Course contact
Course staff
Lecturer
Dr Gloria Milena Monsalve Bravo
Dr Quang Loi
Dr Mark Allenby
Tutor
Dr Christian Zuluaga Bedoya
Mr Jun Lee
Mr Aydin Neighbour
Mr Yifan Bao
Mr Naif Raja
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
- Lectures will be held on Tuesdays 09:30-11:00 in 50-T103
- Lectures will be recordedﾠ
- Tutorials will be held on:
- Thursdays 14:00-16:00 in 46-242/243
- Fridays 10:00-12:00 in 49-316/316A
- These will run each weekﾠ
- The experiments, upon which you will be determining control parameters (i.e. Project), will be held at various times in week 9. Please check Blackboard for your allocated time slot and group. You will work in teams of about 4 to 5 people, but submit the project ﾠindividually. You must study your task sheets and understand your tasks and lab safety before conducting the experiments.ﾠﾠ
Aims and outcomes
The course focuses on the modelling and control of dynamic process systems. These systems encompass chemical and biological processes that can be represented using mathematical equations. The primary objective of this course is to simulate the dynamics of these systems and implement control mechanisms that guarantee their safe and efficient operation, while minimising the need for human intervention. Throughout the course, students will acquire the essential skills for deriving mathematical models for dynamic systems and designing effective controllers. While a basic understanding of computer programming is necessary, students will be guided through various introductory subjects that foster comprehension of numerical methods in process modelling and control.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Understand the role and use of system models: Concepts of modelling and the use of models in the product/process life cycle: Understand why modelling is done in industry and consultancy and where it is applied across the life cycle. Appreciate what models allow you to do in terms of decision making.
LO2.
Understand the role and use of system models: Have the ability to observe particular engineered or natural systems for their dynamic behaviour and relate this to underlying physics and chemistry.
LO3.
Understand the role and use of system models: Understand how to evaluate dynamic behaviour to prevent critical system failure. This prevents environmental, social, and safety violations.
LO4.
Model development strategy and framework: Understand and apply a systematic model development framework applied to a range of relevant problems.
LO5.
Model development strategy and framework: Development of relevant phenomenological models through the application of basic physics and chemistry.
LO6.
Model development strategy and framework: Develop an empirical model from input-output plant data time series and apply the model to an application area.
LO7.
Analysis and solution techniques: An understanding of the structural characteristics of models and the implications on solution approaches.
LO8.
Analysis and solution techniques: The ability to translate model descriptions into executable programs.
LO9.
Analysis and solution techniques: The ability to critically analyse written code, debug it and obtain dynamic trend predictions.
LO10.
Application of a model in process control: Develop the capability to quantitatively describe the behaviour of simple control systems and design effective control systems.
LO11.
Application of a model in process control: Cultivate proficiency in utilising computer software to facilitate the description and design of control systems.
LO12.
Application of a model in process control: Develop techniques for tuning controllers to achieve desired system performance, with opportunities to apply this knowledge in laboratory settings.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Tutorial/ Problem Set |
Take-Home Assignment
|
30% |
Assignment 1: 5/08/2024 2:00 pm Assignment 2: 26/08/2024 2:00 pm Assignment 3: 16/09/2024 2:00 pm
Assignment 1 & 2: Submission: The written assignment response, in Word or PDF format, should be submitted to Gradescope in Blackboard. Assignment 3 Submission: Python code files should be submitted to Gradescope in Blackboard. |
| Practical/ Demonstration |
Online Laboratory OH&S Training
|
Pass/Fail |
19/09/2024 1:00 pm
Students will be given available time slots, and they will need to sign up in advance. |
| Project |
Project
|
25% Hurdle |
21/10/2024 2:00 pm
Submission: The project files should be submitted to Gradescope in Blackboard. |
| Examination |
Final Examination
|
45% Hurdle |
End of Semester Exam Period 2/11/2024 - 16/11/2024 |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Take-Home Assignment
- Online
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 30%
- Due date
Assignment 1: 5/08/2024 2:00 pm
Assignment 2: 26/08/2024 2:00 pm
Assignment 3: 16/09/2024 2:00 pm
Assignment 1 & 2: Submission: The written assignment response, in Word or PDF format, should be submitted to Gradescope in Blackboard.
Assignment 3 Submission: Python code files should be submitted to Gradescope in Blackboard.
- Other conditions
- Student specific.
- Learning outcomes
- L01, L02, L04, L05
Task description
Assignment 1 - 5%: In this take-home assignment, students will work on two dynamic system examples. They are required to apply the five model-building steps learned in Week 1. These steps include defining the modelling goal, determining the model use, identifying key assumptions, describing controlling mechanisms, and summarising input data.
Assignment 2 - 10%: In this assignment, students will be given a dynamic system and will be required to develop a mathematical model by applying model-building steps learned in Weeks 1-3.
Assignment 3 - 15%: In this assignment, students will be given a dynamic system and will be required to develop a mathematical model by applying the model-building steps learned in Weeks 1-3. They will also need to solve the model using numerical methods covered in Weeks 2-8.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance. A failure to reference AI use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Submission is via Blackboard.
Deferral or extension
You may be able to apply for an extension.
Assignment 1: A maximum of 7 calendar days is possible, as feedback will be released within a week so that students can use it to prepare for Assignment 2. Students with an approved reason for an extension will be exempt from submitting Assignment 1 and have the weighting for Assignment 2 increased from 20% to 30% . Students may submit Assignment 1 for formative feedback only if they wish.
Assignment 2: A maximum of 7 calendar days is possible, as feedback will be released within a week so that students can use it to prepare for Assignment 3. Students with an approved reason for an extension will be exempt from submitting Assignment 2 and have the weighting for Assignment 3 increased from 20% to 30% . Students may submit Assignment 2 for formative feedback only if they wish.
Assignment 3: The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Assessments must be submitted on or before the due date. Late submissions of assessment items will only be accepted if approval for late submission has been obtained prior to the due date.
Penalties Apply for Late Submission
Refer PPL Assessment Procedure Section 3 Part C (48)
Online Laboratory OH&S Training
- Team or group-based
- Online
- Mode
- Activity/ Performance
- Category
- Practical/ Demonstration
- Weight
- Pass/Fail
- Due date
19/09/2024 1:00 pm
Students will be given available time slots, and they will need to sign up in advance.
- Learning outcomes
- L02, L03
Task description
Occupational Health and Safety
Students should be familiar with the University policy 2.30.14 Laboratory Safety in Teaching Laboratories (https://ppl.app.uq.edu.au/content/2.30.14-occupational-health-and-safety-laboratory). Pertinent information is in the mandatory online module and assessment, UGRD01. UGRD01 can be found on Blackboard in the Training courses Tab > UQ Workplace Inductions and OHS Training > UG Lab Students. UGRD01 only needs to be completed once.
This must be completed before the end of week 2 or you will not be able to do the lab experiment.
The Minimum PPE required across all School of Chemical Engineering undergraduate laboratories is: Safety spectacles or over glasses, lab coat, long trousers that cover the ankles, and fully enclosed shoes.
Submission guidelines
There is no submission for the practical, but students must attend the practical to obtain the data sets needed for the project assignment.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
If students couldn't attend the practical due to medical reasons, they will be given the practical data and a recorded video of the practical.
Project
- Hurdle
- Online
- Mode
- Written
- Category
- Project
- Weight
- 25% Hurdle
- Due date
21/10/2024 2:00 pm
Submission: The project files should be submitted to Gradescope in Blackboard.
- Other conditions
- Student specific.
- Learning outcomes
- L07, L08, L09, L10, L11, L12
Task description
In the Project, students will develop a PID control system for a lab-based heat exchanger using open-loop data obtained in Week 9. They will determine the unknown parameters through both graphical and optimisation methods. Additionally, students will assess various combinations of advanced control arrangements and fine-tune the control parameters.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance. A failure to reference AI use may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
To pass the course, you need to achieve a passing grade (>=50%) on the final exam AND at least 50% of the Project.Submission guidelines
Submission: Blackboard.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.
A maximum of 14 days of extension is possible. Beyond this period, it would overlap with exam periods and grade release dates.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Assessments must be submitted on or before the due date. Late submissions of assessment items will only be accepted if approval for late submission has been obtained prior to the due date.
Penalties Apply for Late Submission
Refer PPL Assessment Procedure Section 3 Part C (48)
Final Examination
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 45% Hurdle
- Due date
End of Semester Exam Period
2/11/2024 - 16/11/2024
- Other conditions
- Student specific.
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L07, L08, L09, L10, L11, L12
Task description
This will be three-hour theory/paper exam covering all concepts learned throughout the semester.
The exam will be in person, invigilated, and closed book.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
To pass the course, you need to achieve a passing grade (>=50%) on the final exam AND at least 50% of the Project.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 180 minutes |
| Calculator options | (In person) Casio FX82 series or UQ approved , labelled calculator only |
| Open/closed book | Closed Book examination - specified written materials permitted |
| Materials | One A4 sheet of handwritten notes, double sided, is permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Description |
|---|---|
| 1 (Low Fail) |
Absence of evidence of achievement of course learning outcomes. Course grade description: Typically overall mark less than 20%. |
| 2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: Typically overall mark between 20 and 44.99%. |
| 3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: Overall course score of 45 - 49.99%, your mark for the final exam is below 50%, AND your mark for the project is below 50%, you may not meet the passing criteria for the course. |
| 4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: Overall course score of 50 - 64.99%, you must also pass the final exam and the Project. The passing mark for both the final exam and the project is >= 50%. |
| 5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: Overall course score of 65 - 74.99%, your mark for the final exam is above 60% AND your mark for Project is above 60%. |
| 6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: Overall course score of 75 - 84.99%, your mark for the final exam is above 70% AND your mark for Project is above 70%. |
| 7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Overall course score above 85, your mark for the final exam is above 80% AND your mark for Project is above 80%. |
Additional course grading information
To pass the course, you must
- pass the final exam
- pass Project Assignment
If you don't meet any of these hurdles your maximum grade is a 3.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Use of Calculators
Only University approved and labelled calculators can be used in all exams for this course. Please consult https://my.uq.edu.au/services/manage-my-program/exams-and-assessment/sitting-exam/approved-calculators for information about approved calculators and obtaining a label for non-approved calculators.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Additional learning resources information
Python ﾠMaterial: There are a number of readily accessible online Python ﾠguides, for instance the official 'getting started' guide at
https://docs.python.org/3/tutorial/index.html.
ﾠ
ﾠ
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks From Week 1 To Week 13 |
Tutorial |
Theory-based tutorial Please see Learning Pathway on the CHEE3007 Blackboard website for details of the Learning Activities Learning outcomes: L01, L02, L03, L04, L05, L06, L07, L08, L09, L10, L11, L12 |
Tutorial |
Computer-based tutorial Please see Learning Pathway on the CHEE3007 Blackboard website for details of the Learning Activities Learning outcomes: L01, L02, L03, L04, L05, L06, L07, L08, L09, L10, L11, L12 |
|
Lecture |
Lecture Please see Learning Pathway on the CHEE3007 Blackboard website for details of the Learning Activities Learning outcomes: L01, L02, L03, L04, L05, L06, L07, L08, L09, L10, L11, L12 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
School guidelines
Your school has additional guidelines you'll need to follow for this course:
- Safety Induction for Practicals
Course guidelines
Safety Induction for Practicals
Anyone undertaking courses with a practical component must complete the UQ Undergraduate Student Laboratory Safety Induction and pass the associated assessment.
Specific instructions, usage guidelines and rules for each of the undergraduate laboratories will be delivered as part of each course.
In some cases, students may be required to attend a specific face-to-face laboratory induction/training session.