EPSRC Reference: |
GR/T28416/01 |
Title: |
Enhancing Manufacturing by linking models derived from ultra-scale down and early process data via an intelligent agent |
Principal Investigator: |
Titchener-Hooker, Professor N |
Other Investigators: |
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Researcher Co-Investigators: |
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Project Partners: |
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Department: |
Biochemical Engineering |
Organisation: |
UCL |
Scheme: |
Standard Research (Pre-FEC) |
Starts: |
06 June 2005 |
Ends: |
05 June 2008 |
Value (£): |
199,503
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EPSRC Research Topic Classifications: |
Bioprocess Engineering |
Design of Process systems |
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EPSRC Industrial Sector Classifications: |
Chemicals |
Pharmaceuticals and Biotechnology |
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Related Grants: |
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Panel History: |
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Summary on Grant Application Form |
Biological products are manufactured typically in a chain of process units, each of which has variability associated with it and combined together it can significantly influence plant productivity. In the design and operation of process plants it is vitally important to take into account the overall process behaviour and the interactions that occur between units. It is only when the unit interactions are considered that the overall process can be optimised. In chemical process design and operation this issue is receiving extensive attention. However, the complexities of biochemical processes significantly complicate the overall process optimisation and pose a major challenge to existing technology. The objective of this research is to develop an intelligent computing scheme (known as agents) to capture the interactions between units and the performance of all the units. Information extracted from small scale mimics of the process (USD model) will be combined with that from process data. The combined information will be used within an optimisation algorithm to formulate improved operating procedures. The scheme will be validated on an industrial case study.
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Key Findings |
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Potential use in non-academic contexts |
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Impacts |
Description |
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Summary |
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Date Materialised |
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Sectors submitted by the Researcher |
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Project URL: |
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Further Information: |
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Organisation Website: |
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