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Unit outline_

CHNG9202: Chemical Engineering Modelling and Analysis

Semester 1, 2023 [Normal day] - Camperdown/Darlington, Sydney

Virtually every aspect of a chemical engineer's professional life will involve some use of mathematical techniques. Not only is the modern chemical engineer expected to be proficient in the use of these techniques, they are also expected to be able to utilise computer-based solutions when analytical solutions are unfeasible. This unit of study aims to expose students to an appropriate suite of techniques and enable them to become proficient in the use of mathematics as a tool for the solution of a diversity of chemical engineering problems. Specifically, this unit consists of two core modules: MODULE A: Applied Statistics for Chemical Engineers and MODULE B: Applied Numerical Methods for Chemical Engineers. These modules aim at furthering knowledge by extending skills in statistical analysis and Chemical Engineering computations. This unit will also enable the development of a systematic approach to solving mathematically oriented Chemical Engineering problems, which will help with making sound engineering decisions. In addition, there will be considerable time spent during the semester on advanced topics related to mathematical analysis techniques in engineering and recent associated developments.

Unit details and rules

Academic unit Chemical and Biomolecular Engineering
Credit points 6
CHNG2802 OR CHNG5702
Assumed knowledge

Enrolment in this unit of study assumes that first year undergraduate core maths, science and engineering UoS (or their equivalent) have been successfully completed

Available to study abroad and exchange students


Teaching staff

Coordinator Fengwang Li,
Lecturer(s) Fengwang Li,
Type Description Weight Due Length
Small continuous assessment Take-home question
One additional question to consolidate learning outcome in lecture/tutorial
25% Multiple weeks 1 hour
Outcomes assessed: LO1 LO3 LO6 LO7 LO2 LO4 LO5
Assignment group assignment Project: Design of Experiments
Submission of report and oral presentation
25% Week 07 2 h
Outcomes assessed: LO3 LO1 LO2
Tutorial quiz Module A quiz
Paper based assessment + upload code screenshot
25% Week 07 2 hours
Outcomes assessed: LO1 LO4 LO2 LO3
Tutorial quiz Module B quiz
Paper based assessment + upload code screenshot
25% Week 12 2 hour
Outcomes assessed: LO1 LO7 LO2 LO3 LO4 LO5 LO6
group assignment = group assignment ?

Assessment summary

  • Project 1: Develop an experimental activity to practice your knowledge in the design of experiments and present the results in written form and orally in front of all students.
  • A problem-solving assignment in numerical computations.

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 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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

WK Topic Learning activity Learning outcomes
Week 01 Review: introduction to Matlab, statistical distributions and their properties Computer laboratory (4 hr) LO4
Week 02 Basics of design of experiments Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 03 Comparison of means Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 04 Analysis of data obtained with a design of experiments scheme: analysis of variance Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 05 Statistical quality control analysis Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 06 Analysis of data obtained with a design of experiments scheme: surface response models Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 07 Submission and oral presentations of practical project Project (4 hr) LO1
Week 08 Review: introduction to Matlab, Making loops and Matlab M-files Computer laboratory (4 hr) LO4
Week 09 Numerical procedures to solve typical engineering equations: least-square techniques for maximising, minimising and finding roots of a set of equations with multiple variables Lecture and tutorial (4 hr) LO4 LO5 LO6 LO7
Week 10 Numerical differentiation and integration Lecture and tutorial (4 hr) LO4 LO5 LO6 LO7
Week 11 Dynamic system with initial value problem Lecture and tutorial (4 hr) LO4 LO5 LO6 LO7
Week 12 Dynamic system with boundary conditions Lecture and tutorial (4 hr) LO4 LO5 LO6 LO7
Week 13 Application of Laplace transform in chemical engineering Lecture and tutorial (4 hr) LO6 LO7

Attendance and class requirements

Attendance: All lectures and tutorials sessions will be delivered face to face. Lectures and Tutorials will be scheduled and announce in the Canvas site at least one week in advance. You are also encouraged to attend and participate in the collaborative group learning activities. We understand that unavoidable commitments may occasionally prevent some people from attending every session. However, we consider our designed activities and meetings indispensable for your learning, so absences are regarded as exceptional. Nevertheless, all lectures will be recorded and made available in Canvas. 

Requirements: The content of this course is fundamental to engineering, so it is important that you can independently demonstrate competency in the syllabus material. Working alone and in groups are both important components of mastering the required knowledge. Legitimate co-operation between you and your fellow students is encouraged. However, direct copying of another student’s work is plagiarism, unacceptable, and unfair to fellow students, the community and the engineering profession. Tutorial submissions that are identified as unacceptable copies will be marked as acceptable with no possibility of resubmission. You should not make your assignment available to a fellow student, but you are encouraged to help your colleges through any difficulties they may have in understanding the subject matter of this course.

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.

Required readings

These textbooks are recommended if you are looking for a reference to consult while studying for the unit of study.


Module A: Design of Experiments for Chemical Engineers

Basic experimental strategies and data analysis for science and engineering. John Lawson and John Erjavec, CRC Press, 2017


Module B: Applied Numerical Methods for Chemical Engineers

Numerical Methods for Engineers. Steven C. Chapra and Raymond P. Canale, MacGraw Hill. 2010.

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. effectively communicate both technical and non-technical material in written, electronic, graphics, and spoken forms
  • LO2. propose experimental and computational approaches to bring together and apply knowledge to numerically characterise, analyse and solve a wide range of engineering problems
  • LO3. use the standard techniques of statistical design of experiments to evaluate the effect of input variables in the response of chemical engineering processes
  • LO4. apply computational methods to get insights into steady and non-steady conditions of chemical engineering processes
  • LO5. fit polynomials to experimental data
  • LO6. use numerical procedures to solve typical engineering equations with multiple variables
  • LO7. write computer codes in Matlab to numerically integrate and differentiate data obtained from experimental observations.

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

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

The structure of the unit remains the same from last year. The mode of teaching has been changed to in person only. Changes have been made for some assessments. The final exam is replaced by two in-class quizzes in the middle and at the end of the semester, which aims to cope with the mix nature of handwriting and coding of this unit. Another change is that the online Canvas quiz is integrated with the take-home assessments to keep consistency over the course of the semester.


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.