Outline

Overview

The chemical manufacturing industry is currently witnessing the fourth industrial revolution, better known as Industry 4.0, where the 'real' and the 'virtual' world are connected, giving rise to smart factories. This unit of study prepares students for the digital transformation of chemical factories and the analysis of a large set of numbers from the machines and to turn them into a competitive advantage. The unit comprises three main components: (1) Manufacturing using smart systems (2) Industrial internet of things, and (3) processing big-data through sophisticated data-driven approaches. Various materials and techniques within the discipline of cyber-physical production systems are covered. For example, the industrial internet of things (IoT), communications, interfaces, machine learning, neural network, and deep learning. Students will also learn how to transfer real-time data from a unit operation to a system of software and hardware elements and understand the basics of security of open communications in order to ensure the safe operations of a smart factory.

Unit details and rules

Academic unit	Chemical and Biomolecular Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	It is assumed that students have a general knowledge of mathematics typical of an undergraduate degree in chemical engineering. This unit is for postgraduate students and also is offered as an elective for fourth year undergraduate students
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Gobinath Rajarathnam, gobinath.rajarathnam@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final Exam Final examination covering all materials Weeks 1-13 inclusive.	30%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Creative work	Project 1 Using Vuforia to deliver interdisciplinary AR learning experience	30%	Week 06	6 weeks	AI allowed
Outcomes assessed:
Case studies	Project 2: Spatio-Temporal Data Modelling & Visualisation Data wrangling, modelling and visualisation using PowerBI and coding to deliver a Short Comms article	30%	Week 12	6 weeks	AI allowed
Outcomes assessed:
Interactive oral	Panel Interview Group interview	10%	Week 13	30 minutes	AI prohibited
Outcomes assessed:
= group assignment ?

Assessment summary

Assignment: The assignments will involve a self-study module and the aims are to encourage revision during the course, allow students to determine their progress in different subjects, and to gain an understanding of the learning expectations of the course.
Mid-sem exam: There will be a mid-semester quiz.
Project: Real-life projects will be given to each group of 2 to 4 students to promote analytical, modeling and computer skills acquired during the course.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

In accordance with University policy, these penalties apply when written work is submitted after 11:59 pm on the due date: *Deduction of 5% of the maximum mark for each calendar day after the due date. **After ten calendar days late, a mark of zero will be awarded

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Course Intro, Project 1 Kickoff, Digital Twins	Lecture (4 hr)
Week 02	AR/VR Toolchain: Vuforia & Creo	Lecture (4 hr)
Week 03	Connected Systems and Security	Lecture (4 hr)
Week 04	Industry & Operations 4.0 and Interactive Models	Lecture (4 hr)
Week 05	User Experience & Learning Design	Lecture (4 hr)
Week 06	Project 1 Studio & Consultations	Tutorial (4 hr)
Week 07	Project 2 Kickoff & PowerBI Foundations	Lecture (4 hr)
Week 08	Data Visualisation & Mapping	Lecture (4 hr)
Week 09	Trends, Scenarios & Forecasting	Lecture (4 hr)
Week 10	Scientific Writing: Short Communications	Lecture (4 hr)
Week 11	Analysis Integration & Storytelling	Lecture (4 hr)
Week 12	Project 2 Studio & Consultations	Tutorial (4 hr)
Week 13	Panel Interview	Assessment (4 hr)

Attendance and class requirements

- Students are expected to attend all lectures and tutorials, and engage actively in group discussions.
- Active participation in group projects is required, with peer assessments contributing to final grades.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Employ smart devices, technologies, and data acquisition systems to capture data from chemical engineering units
LO2. Demonstrate the use of big data in smart factory applications
LO3. Apply advanced analytical methods and modelling techniques to analyse real-time information from production processes

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

This course has been updated this year to setup projects in a way which empowers students with strong technical and scientific communication skills: - Project 1: Addresses international water transport issues via advanced modeling techniques and introduces research thinking. - Project 2: Develops critical analysis skills through a structured mini-review, reinforcing research writing. - Project 3: Applies AR/VR modeling to chemical engineering education, enhancing visualisation of unit operations for engineers. The course aims to guide students in solving real challenges in their communities and the wider world, using cutting-edge technology, best practice and forefront knowledge.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

CHNG5603: Advanced Industrial Modelling and Analysis

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links