CHNG9202: Semester 1, 2021

University home

Library

Current students

Staff intranet

Find an event

Give

Current students

Units CHNG9202 Semester 1 2021 [Normal day]

Unit outline_

CHNG9202: Chemical Engineering Modelling and Analysis

Semester 1, 2021 [Normal day] - Remote

Overview

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
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	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	No

Teaching staff

Coordinator	Alejandro Montoya, alejandro.montoya@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final Exam Individual examination	30%	Formal exam period	2 hours
Outcomes assessed:
Online task	Online assessments	15%	Multiple weeks	1 hour
Outcomes assessed:
Assignment	Data analysis in Chemical Engineering 3 computing take-home examinations during week 8 to week 12	15%	Multiple weeks	2 hours
Outcomes assessed:
Tutorial quiz	Design of Experiments	15%	Week 06	2 hours
Outcomes assessed:
Assignment	Project: Design of Experiments Submission of report and oral presentation	25%	Week 07	2 h
Outcomes assessed:
= group assignment ? = Type B final exam ?

Assessment summary

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	Review: introduction to Matlab, statistical distributions and their properties	Computer laboratory (4 hr)
Week 02	Basics of design of experiments	Lecture and tutorial (4 hr)
Week 03	Statistical quality control analysis	Lecture and tutorial (4 hr)
Week 04	Analysis of data obtained with a design of experiments scheme: analysis of variance	Lecture and tutorial (4 hr)
Week 05	Analysis of data obtained with a design of experiments scheme: surface response models	Lecture and tutorial (4 hr)
Week 06	Application of design of experiments in the chemical industry	Lecture and tutorial (4 hr)
Week 07	Submission and oral presentations of practical project	Project (4 hr)
Week 08	Review: introduction to Matlab, Making loops and Matlab M-files	Computer laboratory (4 hr)
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)
Week 10	Differential equations relevant to chemical engineering with initial conditions	Lecture and tutorial (4 hr)
Week 11	Differential equations relevant to chemical engineering with boundary conditions	Lecture and tutorial (4 hr)
Week 12	Application of Laplace transform in chemical engineering	Lecture and tutorial (4 hr)
Week 13	Review of content	Lecture and tutorial (4 hr)

Attendance and class requirements

Attendance: All lectures and half of the tutorials sessions will be offered online using zoom. The other half of the tutorial sessions will be delivered in dual mode, online and face-to-face. Lectures and Tutorials will be scheduled and announce in the Canvas site at least one week in advance. You are 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 availabe 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.

Lecture slides and tutorial notes: The lecture slides and tutorial notes will be available on a weekly base before the corresponding section.

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. 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
Learning outcomes

Responding to student feedback

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

The assessment tasks are adjusted following previous feedback to decrease the workload on the last week of the semester

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

Attendance and class requirements

Study commitment

Required readings

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