Course coordinator
Dr Stephan Tait
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Chemical Engineering School
Process models and systems analysis are now widely used across industry to understand, engineer and control water and wastewater treatment. It is not possible to optimise wastewater treatment without process control, and models have become enabling tools. This influences traditional aspects such as sanitation and underpins emerging innovations such as resource recovery (energy, nutrients and water). Modelling and control is able to capture complex interconnectivity between technologies and local systems in an integrated manner, which provides a holistic understanding and better solutions. The importance of this course is emphasized by the fact that most people in water and wastewater industries frequently use models or model outputs for decision making, and hence need to understand models and their application.
This course introduces systematic approaches to develop process simulation models of water and wastewater treatment, and the use of such models to understand, design, troubleshoot and control treatment processes. Course attendees will become competent at using these approaches to solve engineering problems faced by the water and wastewater treatment sectors. To ensure that the course has direct industry relevance, the course is structured around application case studies using real process scenarios.
Course requirements
Assumed background
A foundational level understanding of mass and heat transfer processes, and chemical reactions.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
CHEE2001 or CHEE3002 or CHEE2040
Course contact
Course staff
Lecturer
Dr Stephan Tait
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
Understand and apply systematic process modelling and analysis approaches to understand, design, troubleshoot and control water and wastewater treatment processes.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Explain relevant interactions of physics, chemistry and biology relevant to water and wastewater treatment processes.
LO2.
Examine given water or wastewater treatment processes to discuss key objectives of operation and control and develop appropriate control strategies.
LO3.
Solve process models computationally. Analyze and debug model code, obtain and analyze outputs to assess model performance and achieve model study objectives.
LO4.
Analyze and adapt the model in response to field data. Conduct parameter estimation and model validation using field data.
LO5.
Use models to evaluate water and wastewater treatment options and select and defend water and wastewater solutions.
LO6.
Apply models to analyse process dynamic behaviour and use this to devise control and optimisation solutions.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Quiz |
In-semester in-class quiz
|
15% |
3/09/2024 |
| Paper/ Report/ Annotation, Project | Modelling Project Report | 25% |
20/09/2024 - 25/10/2024
This assignment has 2 parts. Part 1 is due week 9, 20/09/2024, 4PM Part 2 is due week 13, 25/10/2024, 4PM |
| Computer Code, Quiz |
Computing practical in-class examination
|
15% |
9/10/2024 |
| Examination |
Theory exam
|
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
In-semester in-class quiz
- Identity Verified
- In-person
- Mode
- Written
- Category
- Quiz
- Weight
- 15%
- Due date
3/09/2024
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02
Task description
The duration of the written quiz will be Reading: 10 minutes; Duration: 50 minutes.
Closed book, Invigilated In-class in-semester quiz.
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.
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Modelling Project Report
- Mode
- Written
- Category
- Paper/ Report/ Annotation, Project
- Weight
- 25%
- Due date
20/09/2024 - 25/10/2024
This assignment has 2 parts.
Part 1 is due week 9, 20/09/2024, 4PM
Part 2 is due week 13, 25/10/2024, 4PM
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
Individual report.
Part 1: Produce a modelling report on a batch time-dependent biological test. (10%)
Part 2: Produce a modelling report using modelling tools to evaluate and select control and optimisation options for water and wastewater treatment. (15%)
Due Date Assignment report part 1 - 20/09/2024 Assignment report part 2 - 25/10/2024
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.
Submission guidelines
Submissions will be electronically submitted through blackboard. A turnitin link will be made available before the due date.
Deferral or extension
You may be able to apply for an extension.
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)
Computing practical in-class examination
- Identity Verified
- In-person
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Quiz
- Weight
- 15%
- Due date
9/10/2024
- Other conditions
- Time limited.
- Learning outcomes
- L03, L05, L06
Task description
Open-Book Invigilated 90 minute In-class computing exam.
Reading: 10 minutes
Duration: 90 minutes
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.
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Theory exam
- 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
- Learning outcomes
- L01, L02, L05, L06
Task description
Identity Verified Assessment. Closed book. Students are required to achieve a minimum final exam score of 40% to achieve a passing grade. Do NOT use red pen or pencil.
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
40% final exam scoreExam details
| Planning time | 10 minutes |
|---|---|
| Duration | 90 minutes |
| Calculator options | (In person) Casio FX82 series or UQ approved , labelled calculator only |
| Open/closed book | Closed Book examination - no written materials 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: Overall course score below 20%. |
| 2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: Overall course score 20-44.99%. |
| 3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: Overall course score of 45-49.99%. |
| 4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: Overall course score of 50-64.99% and final exam score of 40% or more. |
| 5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: Overall course score of 65-74.99% and final exam score of 40% or more. |
| 6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: Overall course score of 75-84.99% and final exam score of 40% or more. |
| 7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Overall course score above 85% and final exam score of 40% or more. |
Additional course grading information
Percentages stated indicate the typical boundaries between grades.
Marking for in-semester assessment pieces will be in accordance with rubrics made available on blackboard.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Use of Calculators
Unless specified elsewhere in the Course Profile, ONLY University approved calculators can be used in all exams for this course. Please consult ﾠhttps://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
Please join and regularly monitor the course Ed page. ﾠAn invitation will be sent out.
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 |
|---|---|---|
Lecture |
Week 1 - Lecture and in-class demonstration Introduction to modelling. Mass, energy balances, solving in Excel and Python. Learning outcomes: L01, L02, L03 |
|
Information technology session |
Week 1 - Computer-based learning session Computing based exercises focused on practical integrated problems. Learning outcomes: L01, L02, L03, L04, L05, L06 |
|
Lecture |
Week 2 - Lecture and in-class demonstration Biological models in wastewater treatment. Solving dilute systems. Dynamic vs steady state modelling. Learning outcomes: L01, L02, L03, L05 |
|
Information technology session |
Week 2 - Computer-based learning session Computing based exercises focused on practical integrated problems. Learning outcomes: L01, L02, L03, L04, L05, L06 |
|
Lecture |
Week 3 - Lectures and in-class demonstration Modelling multi-compartment systems. Building integrated models. Model types. State transformations. Learning outcomes: L01, L02, L03, L05 |
|
Information technology session |
Week 3 - Computer-based learning session Computing based exercises focused on practical integrated problems. Learning outcomes: L01, L02, L03, L04, L05, L06 |
|
Lecture |
Week 4 - Lecture and in-class demonstration IWA Models focusing on activated sludge models, but also anaerobic digestion and other (e.g. Anammox models). Inorganic chemistry. Learning outcomes: L01, L02, L03, L05 |
|
Lecture |
Week 5 - Lecture and in-class demonstration Multicompartment modelling including indices and loops in models, Dynamic dissolved oxygen, introduction of kLa and implementing disturbances and dynamic input data. Learning outcomes: L01, L02, L03, L05, L06 |
|
Information technology session |
Week 5 - Computer-based learning session Computing based exercises focused on practical integrated problems. Learning outcomes: L01, L02, L03, L04, L05, L06 |
|
Lecture |
Week 6 - Lecture and in-class demonstration Control theory. Implementing and testing controllers in simple systems. Sensor, controller, and actuator models including ASM1 and settler model (6stage models). Quiz preparation. Learning outcomes: L01, L02, L03, L05, L06 |
|
Information technology session |
Week 6 - Computer-based learning session Computing based exercises focused on practical integrated problems. Learning outcomes: L01, L02, L03, L04, L05, L06 |
|
Information technology session |
Week 7 - Computer-based learning session Computing based exercises focused on practical integrated problems. Learning outcomes: L01, L02, L03, L04, L05, L06 |
|
Lecture |
Week 8 - Lecture and in-class demonstration Expansion of ASM1 including settler modelling and Parameter estimation. Learning outcomes: L02, L03, L04, L05, L06 |
|
Information technology session |
Week 8 - Computer-based learning session Computing based exercises focused on practical integrated problems. Learning outcomes: L01, L02, L03, L04, L05, L06 |
|
Lecture |
Week 9 - Lecture and in-class demonstration Wastewater characterisation. Learning outcomes: L02, L03, L04, L05, L06 |
|
Information technology session |
Week 9 - Computer-based learning session Computing based exercises focused on practical integrated problems. Learning outcomes: L01, L02, L03, L04, L05, L06 |
|
Lecture |
Week 10 - Lecture and in-class demonstration Integrated plant wide modelling, control and optimisation. Practice practical exam demonstration. Learning outcomes: L02, L03, L04, L05, L06 |
|
Information technology session |
Week 10 - Computer-based learning session Computing based exercises focused on practical integrated problems. Learning outcomes: L01, L02, L03, L04, L05, L06 |
|
Lecture |
Week 11 - Lecture and in-class demonstration Collaborative modelling. Learning outcomes: L01, L05, L06 |
|
Lecture |
Week 12 - Lecture and in-class demonstration Course review. Learning outcomes: L01, L02, L03, L04, L05, L06 |
|
Information technology session |
Week 12 - Computer-based learning sessions Computing based exercises focused on practical integrated problems. Learning outcomes: L01, L02, L03, L04, L05, L06 |
|
Information technology session |
Week 13 - Computer-based learning sessions Computing based exercises focused on practical integrated problems. Learning outcomes: L01, L02, L03, L04, L05, L06 |
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.