CHEMMAT 773 : Food Process Systems Engineering

Engineering

2025 Semester Two (1255) (15 POINTS)

Course Prescription

Advanced understanding of the theory and application of process systems engineering for the food industry. Includes advanced process analytical technology, real-time quality control, multivariate data analysis, advanced statistical process control, advanced control methods and strategies, and real-time optimisation. Teaching is highly research informed with examples from the Industrial Information and Control Centre (I2C2) and includes an independent laboratory based project.

Course Overview

This course provides a comprehensive introduction to process systems engineering with a strong focus on the integration of digital twins, big data analytics, and advanced data-driven approaches for process optimization. The course is designed to equip students with both theoretical knowledge and practical skills in modern engineering methods, specifically tailored for applications in the food industry. It includes the following four sections:

Process Modeling for Food Systems: Students will learn to build mathematical and empirical models tailored to food processes, covering essential modeling techniques and considerations unique to food production environments. This section emphasizes developing models that can simulate, predict, and optimize various food processing stages.

Statistical Process Control (SPC) for Quality Control and Optimization: This segment introduces students to SPC as a tool for monitoring, controlling, and optimizing production processes. Students will explore how to use SPC techniques to ensure quality and consistency in manufacturing through models, with a focus on interpreting control charts and understanding variation in food processing operations.

Design of Experiments (DoE): The DoE section teaches students how to systematically investigate the effects of multiple independent variables on one or more dependent variables, a critical skill for optimizing process conditions. Students will learn how to plan and conduct experiments using models that yield valuable insights and enhancing process efficiency.

Machine Learning Techniques for Process Analysis: This section covers the fundamentals of machine learning as applied to process data, focusing on multivariable analysis, and pattern recognition in food processing systems. Students will gain exposure to machine learning algorithms that can identify complex relationships within data, enabling improved process performance.

Course Requirements

No pre-requisites or restrictions

Capabilities Developed in this Course

Capability 3: Knowledge and Practice
Capability 4: Critical Thinking
Capability 5: Solution Seeking

Learning Outcomes

By the end of this course, students will be able to:
  1. Use process modelling and simulation for food processing with a commercial process simulator (Capability 3.1 and 3.2)
  2. Classify variations in food processing (Capability 4.1 and 4.2)
  3. Apply statistical process control to evaluate process performance (Capability 4.2 and 5.1)
  4. Create an experimental design using the classical Design of Experiments approach and analyze the results (Capability 4.2 and 5.1)
  5. Execute and implement selected machine learning and other advanced process systems engineering methods (Capability 4.1, 4.2 and 5.1)

Assessments

Assessment Type Percentage Classification
Test 10% Individual Test
Reports 40% Group & Individual Coursework
Presentation 10% Group & Individual Coursework
Final Exam 40% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5
Test
Reports
Presentation
Final Exam

  • A passing mark is 50% or higher, according to UoA policy.
  • Students must sit the final test to pass the course. Otherwise, a DNS (did not sit) result will be returned.
  • A written project report must be submitted via the Canvas assignment page by 12 noon on the due data or late penalties will apply.
  • The penalty for lateness is 5 marks for each day (or part of day thereof) in which the digital copy of the assessment is submitted late. For the avoidance of doubt, penalties are applied on a calendar day basis (not a 24 hour basis). For the purposes on online-only submission, Saturday and Sundays count as calendar days.

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, in a typical week you can expect 2 hours of lectures, a 2 hour tutorial, 3.5 hours of reading and thinking about the content and 5 hours of work on assignments and/or test preparation.

Delivery Mode

Campus Experience

Attendance is required at scheduled activities including tutorials to complete/receive credit for components of the course.
Lectures will be available as recordings. Other learning activities including tutorials will not be available as recordings.
The course will not include live online events.
Attendance on campus is required for the test.
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Matlab / Python software, Symmetry simulator software.

Health & Safety

There is no lab work for this course.

Students must ensure they are familiar with their Health and Safety responsibilities, as described in the university's Health and Safety policy.

Student Feedback

At the end of every semester students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions.

Your feedback helps teachers to improve the course and its delivery for future students.

Class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

Based on student's feedback about past examine questions: "Maybe getting the class to answer past questions more would help solidify some of the content we have learned",  we will offer more tutorials to explain some typical questions in the past exam.

Based on student's feedback about workshops: "The workshops were slightly outdated compared to what we were doing, with some stuff written in the assignment sheets being different and unchanged from what they actually wanted us to do.", we will update the workshop instruction manual and provide more instructions about modelling.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework, tests and examinations as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. A student's assessed work may be reviewed against electronic source material using computerised detection mechanisms. Upon reasonable request, students may be required to provide an electronic version of their work for computerised review.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website http://disability.auckland.ac.nz

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.

Disclaimer

Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.