BIO09095 2021 Bio-Industry 4.0 Theory and Practice
Full Title
Bio-Industry 4.0 Theory and Practice
Transcript Title
Bio-Industry 4.0 Theory and Pr
Code
BIO09095
Attendance
N/A %
Subject Area
BIO - Bio Tech/Eng/Chem
Department
LIFE - Life Sciences
Level
09 - NFQ Level 9
Credit
10 - 10 Credits
Duration
Semester
Fee
€
Start Term
2021 - Full Academic Year 2021-22
End Term
9999 - The End of Time
Author(s)
Mary Butler
Programme Membership
SG_SBIOI_E09 202100 Postgraduate Certificate in BioIndustry 4.0
SG_SVALI_O09 202300 Postgraduate Diploma in Science in Validation and Digitalisation Technologies
SG_SVALI_M09 202300 Master of Science in Validation and Digitalisation Technologies
SG_SBIOI_M09 202300 Master of Science in BioIndustry 4.0
SG_SVALD_O09 202300 Postgraduate Diploma in Science in BioPharmaceutical Validation and Digitalisation
Description
|
This module aims to provide students with an understanding of the key concepts of Industry 4.0 and outlines the application of the latest digital trends and technologies in the Bio-Industry 4.0 field, and how they can be practically applied. |
Learning Outcomes
On completion of this module the learner will/should be able to;
1.
Critically assess the smart factory concept in the biopharma industry, considering digital supply network, real-time data, optimised and predictive production.
2.
Evaluate the role of PAT in the implementation of the smart factory concept, focusing on efficient use of data from different sources old and new, to bring about measured efficiencies and controls.
3.
Evaluate the current applications of automation and the future potential applications and benefits of automation and data integration in the implementation of the smart factory including the identification of challenges.
4.
Demonstrate an understanding of the ISA-95 model, the international standard for the integration of enterprise and control systems, along with other automation standards such as NAMUR and Profinet.
5.
Covering the entire bioproduction supply-chain from design to delivery of the product to the patient, learn to evaluate various digital tools reproducing essential production elements (digital twins, serious games, immersive reality, virtual reality, and augmented reality) and cognitive approaches supported by artificial intelligence to promote understanding of processes and the appropriation of professional practices, using case studies where appropriate.
6.
Engage in and demonstrate independent learning as well as communicate effectively as an Individual and or a member of a team
Teaching and Learning Strategies
This module will be taught using on‑line lectures. A range of Computer‑Aided Learning (CAL) packages are also used to support this module (e.g. Moodle,Adobe Connect, Panopto, Camtasia). Students are provided with electronic materials for self‑assessment and preparation for assessments/assignments. Self‑directed, student‑centred, independent learning is a core aspect through completion of module coursework.
Module Assessment Strategies
The assessment approach for this module will be 100% continuous employed including some of the following: Enquiry based Projects, Assignments/mini projects including Viva/Presentation Short-form assessment exams incl. MCQs, Short answer and Long Answer Questions.
Repeat Assessments
Students will have opportunities to re-submit work as agreed with their lecturer.
Indicative Syllabus
The following is a summary of the main topics included in this module: Smart factory, PAT, ISA95 model, measurement and instrumentation, process control in biopharma manufacturing, shift from traditional supply chain to digital supply network.
Coursework & Assessment Breakdown
Coursework & Continuous Assessment
100 %
Coursework Assessment
| Title | Type | Form | Percent | Week | Learning Outcomes Assessed | |
|---|---|---|---|---|---|---|
| 1 | MCQ 1 | Coursework Assessment | Multiple Choice/Short Answer Test | 10 % | Week 3 | 1,2 |
| 2 | MCQ 2 | Coursework Assessment | Multiple Choice/Short Answer Test | 10 % | Week 6 | 1,2,3 |
| 3 | End of Semester Assessment | Coursework Assessment | Assignment | 30 % | Week 11 | 1,2,3,4,5,6 |
| 4 | Project & Viva | Project | Project | 50 % | Week 12 | 1,2,3,4,5,6 |
Part Time Mode Workload
| Type | Location | Description | Hours | Frequency | Avg Workload |
|---|---|---|---|---|---|
| Lecture | Online | Lecture | 4 | Weekly | 4.00 |
| Independent Learning | Not Specified | Self Study | 10 | Weekly | 10.00 |
Total Part Time Average Weekly Learner Contact Time 4.00 Hours
Module Resources
Non ISBN Literary Resources
Journal Resources
URL Resources
Senatore, M. (2017) ‘Digitizing the Supply Chain: Why Pfizer Is Investing in IoT Technologies’.
Merck Millipore. (2019) Innovation and Digitalization - Merck Corporate Responsibility Report 2018.
Kirdar, A.O., Green, K.D. and Rathore, A.S. (2008) ‘Application of Multivariate Data Analysis for Identification and Successful Resolution of a Root Cause for a Bioprocessing Application’. Biotechnology Progress, 24(3), pp. 720–726.
Ündey, C. et al. (2010) ‘Applied Advanced Process Analytics in Biopharmaceutical Manufacturing: Challenges and Prospects in Real-Time Monitoring and Control’. Journal of Process Control, 20(9), pp. 1009–1018.
Bedre, P. (2013) ‘Applying QbD and Pat in Biological Manufacturing for “Continued Process Verification”’. Open Access Dissertations.
Chen, Y. et al. (2020) (9) ‘Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review’. Processes, 8(9), p. 1088.
Chapman, C. (2018) How Amgen Uses AI Tools to Improve Manufacturing Deviation Investigations.
Other Resources
None
Additional Information