Module manager: Dr DW Dixon
Email: d.w.dixon@leeds.ac.uk
Taught: Semesters 1 & 2 (Sep to Jun) View Timetable
Year running 2026/27
This module is not approved as a discovery module
The module integrates the teaching of engineering management (scope-time-resource triangle, quality and project management) with application of it on a process development project. The project aims to deliver a process improvement through planning, management and delivery of practical lab work and the analysis of the data. The students will develop an awareness of project management through learning and practice, and develop their critical thinking and problem-solving to find a solution to a process engineering problem.
The module aim is to couple formal education of engineering management principles to an experimental lab project in which students have to work towards a specified development objective.
A further objective is for students to apply the taught engineering management on their experimental project. Hereto students will be split over 4 different experimental themes. Within their theme, students will form project groups of 3-4 students.
Within their project group students will work together to practice engineering management skills (e.g. development of the scope of their project, a PERT diagram, a project Gantt Chart, a risk register, team work, project communication) and their laboratory skills (practical work, design of experiments and data analysis and fitting).
All project groups within a theme will meet several times in S1 and S2 to support them progressing their project definition, present data and discuss experimental design and feedback on their work by the theme leads (academic staff). The data generated by each group will be shared with all groups in the theme so as to ensure a significant data set is available to all students. Data analysis tools and AI are practiced throughout the module, and covered in lectures as well as discussion in the workgroup meetings.
The assessment is focused on communicating project progress coupled to personal reflection, and an individual viva at the end of the module to test student’s knowledge on the taught materials and their contribution to the project work.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Adopt an inclusive approach to engineering practice, recognising the responsibilities, benefits and importance of supporting equality, diversity and inclusion.
2. Understand and be able to apply knowledge of engineering management principles and techniques, including project and change management, and understand their limitations.
3. Be aware of quality assurance issues and their application to continuous improvement.
4. Have a knowledge and understanding of laboratory practice, and ability to operate bench scale chemical engineering equipment.
5. Be able to design, plan and undertake experimental or plant work and critically interpret, analyse and report on experimental data.
6. Have developed a range of effective communication skills. Be able to find and apply, with judgement, information from technical literature and other sources.
7. Understand the principles of risk assessment and of safety management and be able to apply techniques for the assessment of process hazards.
On successful completion of the module students will have demonstrated the following skills:
a. Work-Ready: Communication skills, writing, presenting, representing the team.
b. Enterprise: Project management skills, scoping, planning, stakeholder management.
c. Digital: use of AI in research and communication (use of AI, use of Excel).
d. Work-Ready: Teamwork/Collaboration, influencing, team roles, resource management.
e. Technical: Lab skills, experimental design, analysis and data interpretation.
f. Work-Ready/Academic: Project work, problem solving & analytical skills,
critical & creative thinking in application to innovation.
Engineering Management:
Developing scope, managing resources, and planning time. Project objectives and quality, understanding teams, stakeholders, and managing expectations. Execution and risk management, communication of progress and managing the perception of your project. Using AI in support of the project.
Application:
Project design and management, planning and execution of scoping process and analytical experiments. Data analysis, detailed experimental design (design of experiments, use of AI and machine learning) data fitting and further experimentation. Data handling. Reporting project progress and Presenting project outcomes.
Practice:
Experimental design (awareness of Experimental Design and Design Space). Development of Experimental design for implementation in lab practical.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 20 | 1 | 20 |
| Practical | 1 | 1 | 1 |
| Practical | 4 | 3 | 12 |
| Seminar | 8 | 1.5 | 12 |
| Private study hours | 155 | ||
| Total Contact hours | 45 | ||
| Total hours (100hr per 10 credits) | 200 | ||
The students will be split over 4 different experimental themes, of ~30 students per themes. Within their theme students will form project groups of 3-4 students. Within their project group students will work together to practice engineering management skills (e.g. development of the scope of their project, a PERT diagram, a project Gantt Chart, a risk register) and their laboratory skills (practical work, design of experiments and data analysis and fitting).
All project groups within a theme will meet 4 times (1.5 hr) in S1 and S2 to support them progressing their project definition, present data and discuss experimental design. Feedback on their work will be provided by the theme leads. The data generated by each group will be shared with all groups in the theme so as to ensure a significant data set is available to all students.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Assignment | Group Project | 30 |
| Presentation | Presentation | 20 |
| Viva | Viva | 50 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
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