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Unit of study_

CHNG5603: Advanced Process Modelling and Simulation

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.


Academic unit Chemical and Biomolecular Engineering
Unit code CHNG5603
Unit name Advanced Process Modelling and Simulation
Session, year
Semester 1, 2020
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

Assumed knowledge

It is assumed that students have a general knowledge of: (MATH1001 OR MATH1021) AND (MATH1003 OR MATH1023) AND (CHNG2802 OR MATH2XXX)

Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Amirali Ebrahimi Ghadi,
Lecturer(s) Farshad Oveissi ,
Amirali Ebrahimi Ghadi,
Type Description Weight Due Length
Assignment Project1
individual project
20% Week 05 n/a
Outcomes assessed: LO1
In-semester test Quiz1
Mid-semester quiz
30% Week 07 n/a
Outcomes assessed: LO1 LO2 LO3
Assignment group assignment Project2
Group project
25% Week 10 n/a
Outcomes assessed: LO2 LO3
Assignment group assignment Project3
Group project
25% Week 13 n/a
Outcomes assessed: LO2 LO3
group assignment = group assignment ?
  • 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


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, and the resources available to all students.

The University expects students and staff to act ethically and honestly and will treat all allegations of academic dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes
Week 01 Industry 4.0, examples of smart chemical industrial systems Lecture and tutorial (5 hr) LO1 LO2
Week 02 Operation 4.0, Augmented Reality (AR) for smart factories, wearables and localisation devices, intelligent health and safety devices for operators Lecture and tutorial (5 hr) LO1 LO2
Week 03 Sensors, smart systems, and IoT in biochemical industries Lecture and tutorial (5 hr) LO1 LO2
Week 04 Cybersecurity in chemical industries, Artificial Intelligence (AI) in Chemical Engineering Lecture and tutorial (5 hr) LO1 LO2 LO3
Week 05 Introduction to Machine Learning and Artificial Neural Networks (ANNs) Lecture and tutorial (5 hr) LO2 LO3
Week 06 Introduction to deep learning Lecture and tutorial (5 hr) LO2 LO3
Week 07 Mid-semester Quiz Lecture and tutorial (2 hr) LO1 LO2 LO3
Week 08 Applications of Artificial Neural Networks (ANNs) in chemical process control Lecture and tutorial (5 hr) LO2 LO3
Week 09 Neural network predictive control 1- Group Project Lecture and tutorial (5 hr) LO2 LO3
Week 10 Neural network predictive control 2- Group Project Lecture and tutorial (5 hr) LO2 LO3
Week 11 Applications of Artificial Neural Networks (ANNs) in chemical process design Lecture and tutorial (5 hr) LO2 LO3
Week 12 Neural network in optimisation of chemical processes 1- Group Project Lecture and tutorial (5 hr) LO2 LO3
Week 13 Neural network in optimisation of chemical processes 2- Group Project Lecture and tutorial (5 hr) LO2 LO3

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 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.

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
The whole content of the unit has been changed. Only the title of the unit remains the same, but in terms of content, assessment, student learning outcome, etc. this is the first time this unit has been offered.


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