Outline

Overview

This course will give students an insight into the use of (computer-based) statistical techniques in extracting information from experimental data obtained from real life bio-physical systems. The issues and techniques required for mathematical modeling as well as monitoring and/or control scheme for bio-physical systems will be discussed and implemented in diverse range of bioprocesses, including biomaterials and fermentation products. We will review statistical distribution; tests based on z, t, F variables; calculation of confidence intervals; hypothesis testing; linear and nonlinear regression; analysis of variance; principal component analysis; and use of computer-based statistical tools. The issues associated with dynamic response of bio-physical processes; inferred or estimated variables; control system design and implementation; introduction to model-based control; use of computer-based control system design and analysis tools will be elaborated. When this course is successfully completed you will acquire knowledge to choose the appropriate statistical techniques within a computer based environment, such as Excel or MATLAB, for a given situation. The students will also obtain potential for monitoring/control scheme based on the key dynamic features of the process. Such information would be beneficial for any future career in Bio-manufacturing companies. Students are encouraged to promote an interactive environment for exchange of information.

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, equivalent to MATH1021 AND MATH1023 AND (CHNG2802 OR MATH2XXX). 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	Amirali Ebrahimi Ghadi, amirali.ebrahimighadi@sydney.edu.au
Lecturer(s)	Farshad Oveissi, farshad.oveissi@sydney.edu.au
Lecturer(s)	Amirali Ebrahimi Ghadi, amirali.ebrahimighadi@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Project 1 individual project	20%	Week 05	n/a
Outcomes assessed:
In-semester test (Record+)	Mid-semester exam Online quiz on topics covered in weeks 1 to 6.	30%	Week 08 Due date: 27 Apr 2021 at 15:00	2 hours
Outcomes assessed:
Assignment	Project 2 Group project	25%	Week 10	n/a
Outcomes assessed:
Assignment	Project 3 Group project	25%	Week 13	n/a
Outcomes assessed:
= group assignment ? = Type B in-semester exam ?

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.

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.

Academic integrity

The Current Student website provides information on academic integrity 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 integrity breaches seriously.

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

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Industry 4.0, examples of smart chemical industrial systems	Lecture and tutorial (4 hr)
Week 02	Operation 4.0, digital twin, Augmented Reality (AR) for smart factories, wearables and localisation devices, intelligent health and safety devices for operators	Lecture and tutorial (4 hr)
Week 03	Sensors, Industrial Internet of Things (IoT), cybersecurity	Lecture and tutorial (4 hr)
Week 04	Introduction to Machine Learning and Artificial Neural Networks (ANNs)	Lecture and tutorial (4 hr)
Week 05	Introduction to programming in Python	Lecture and tutorial (4 hr)
Week 06	Introduction to Deep Learning (in Python)	Lecture and tutorial (4 hr)
Week 07	Applications of Artificial Neural Networks (ANNs) in chemical process design	Lecture and tutorial (4 hr)
Week 08	Mid-semester Quiz	Lecture (2 hr)
Week 08	Neural network in optimisation of chemical processes 1- Group Project	Tutorial (2 hr)
Week 09	Neural network in optimisation of chemical processes 2- Group Project	Lecture and tutorial (4 hr)
Week 10	Applications of Artificial Neural Networks (ANNs) in chemical process control	Lecture and tutorial (4 hr)
Week 11	Neural network predictive control 1- Group Project	Lecture and tutorial (4 hr)
Week 12	Neural network predictive control 2- Group Project	Lecture and tutorial (4 hr)
Week 13	Neural network predictive control 3- Group Project	Lecture and tutorial (4 hr)

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.

No changes have been made since this unit was last offered

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

CHNG5603: Advanced Industrial Modelling and Analysis

Semester 1, 2021 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect