CHEN40060 Bioreactor Modelling and Control

Academic Year 2022/2023

Despite the commercial success of bioprocessing, the bases for bioreaction scale-up are poorly defined and loss of productivity associated with scale-up to pilot- or production-scale reactors is a common feature of bioprocess development. Overcoming these scale-up obstacles is strongly dependent upon understanding the interrelationship between physiological determinants of cell growth and product synthesis and also the physical determinants of culture performance in the intensified bioreactor environment. A mathematical model is a systematic attempt to translate the conceptual understanding of a real-world system into mathematical terms. Successful model development relies on the integration of physical mechanisms (e.g. mass transfer and hydrodynamics) with the investigation of the biological response to conditions expected in the bioreactor environment. If the conceptual model is good, the model should reproduce the relevant phenomena and can be used for a number of purposes; to test hypotheses, to identify bottlenecks in a process; to aid the interpretation of experimental data or to examine processes where experiments are impractical at an early stage in the development of the process. This module will introduce key concepts in Bioreaction Engineering and apply them to the analysis of data from typical bioreactor operations. Fundamentals of process control applied to bioreactors will also be introduced.

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Curricular information is subject to change

Learning Outcomes:

On completion of this module students will be able to:
1. Distinguish between the different types of mathematical models applied to microbial or animal cell bioreactors.
2. Implement basic kinetic and stoichiometric relationships in batch and fed-batch bioreactor models.
3. Analyze and interpret bioreactor process data.
4. Describe the mechanisms associated with non-ideal effects on process performance in bioreactors and describe how these effects can be simulated.
5. Apply sensitivity analysis to model outputs in order to develop an understanding of the nature of predictive associations.
6. Describe key concepts in bioreactor process control.

Student Effort Hours: 
Student Effort Type Hours
Lectures

24
Tutorial

8
Autonomous Student Learning

80
Total

112
Approaches to Teaching and Learning:
1. Lectures
2. Studio work with gPROMS ModelBuilder
3. Task-based learning: individual assignments
4. Critical writing: critical review of published research articles 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Final Assignment Week 12 n/a Graded No

25
Continuous Assessment: Assignments Varies over the Trimester n/a Graded No

25
Examination: Final examination 2 hour End of Trimester Exam No Graded No

50

Carry forward of passed components
Yes
 
Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Feedback will be given to students no later than three weeks after assignment submission.