| Feb. 13, 2024, 4:20 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 67156, "fields": {"project": 15410, "organisation": 11085, "amount": 1034389, "start_date": "2021-09-01", "end_date": "2023-08-31", "raw_data": 188629}}]
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| Jan. 30, 2024, 4:25 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 59978, "fields": {"project": 15410, "organisation": 11085, "amount": 1034389, "start_date": "2021-09-01", "end_date": "2023-08-31", "raw_data": 169862}}]
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| Jan. 2, 2024, 4:16 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 52844, "fields": {"project": 15410, "organisation": 11085, "amount": 1034602, "start_date": "2021-09-01", "end_date": "2023-08-31", "raw_data": 143225}}]
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| Dec. 5, 2023, 4:24 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 45589, "fields": {"project": 15410, "organisation": 11085, "amount": 1034602, "start_date": "2021-08-31", "end_date": "2023-08-30", "raw_data": 122533}}]
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| Nov. 27, 2023, 2:16 p.m. |
Added
35
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{"external_links": []}
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| Nov. 21, 2023, 4:43 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 38316, "fields": {"project": 15410, "organisation": 11085, "amount": 1034602, "start_date": "2021-08-31", "end_date": "2023-08-30", "raw_data": 79155}}]
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| Nov. 21, 2023, 4:43 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 115116, "fields": {"project": 15410, "organisation": 19387, "role": "PARTICIPANT_ORG"}}]
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| Nov. 21, 2023, 4:43 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 115115, "fields": {"project": 15410, "organisation": 12416, "role": "PARTICIPANT_ORG"}}]
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| Nov. 21, 2023, 4:43 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 115114, "fields": {"project": 15410, "organisation": 19385, "role": "PARTICIPANT_ORG"}}]
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| Nov. 21, 2023, 4:43 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 115113, "fields": {"project": 15410, "organisation": 18986, "role": "PARTICIPANT_ORG"}}]
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| Nov. 21, 2023, 4:43 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 115112, "fields": {"project": 15410, "organisation": 12471, "role": "PARTICIPANT_ORG"}}]
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| Nov. 21, 2023, 4:43 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 115111, "fields": {"project": 15410, "organisation": 19386, "role": "PARTICIPANT_ORG"}}]
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| Nov. 21, 2023, 4:43 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 115110, "fields": {"project": 15410, "organisation": 19387, "role": "LEAD_ORG"}}]
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| Nov. 21, 2023, 4:43 p.m. |
Created
40
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[{"model": "core.projectperson", "pk": 72429, "fields": {"project": 15410, "person": 21217, "role": "PM_PER"}}]
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| Nov. 20, 2023, 2:06 p.m. |
Updated
35
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{"title": ["", "AI6S \u2013 AI lean six-sigma process optimisation for energy efficiency and waste reduction in foundation industries"], "description": ["", "\nAI6S will develop a novel toolkit for process optimisation in foundation industries (FI), enabling process efficiency improvements resulting in reduced energy consumption and reduced waste and improved ability to meet challenging (and commercially attractive) specifications and short turnaround times. Current lean six-sigma methodologies provide efficiency gains through continuous improvement following highly manual procedures over process iterations. AI6S will make a step change in the ability of companies to achieve "right first time" production output to challenging specifications and stringent quality criteria whilst reducing energy consumption, waste and cost through higher yields, more efficient processes and faster throughput with potential for annual savings 21,800,111 kgCO2e (carbon dioxide equivalent)/ £58M for the UK and 675million kgCO2e/ £1,800million globally.\n\nThese benefits will be achieved through the use of a machine-learning optimisation approach tailored, for the first time, specifically to the needs of foundation industries (FIs) and integrated within a lean six-sigma framework. The system will automate and massively speed up process improvement and reuse of historical data to optimise process conditions. Novel aspects of our approach include the use of a special two-stage optimisation process which provides rapid global optimisation even for complex processes involved in high energy thermal processes and the use of data synthesis to achieve faster and more accurate model training.\n\nThis novel technological approach will be integrated into a lean six-sigma framework for rapid adoption by practitioners within the foundation industries. The algorithms will be implemented in a software platform for ease of use and integration with other quality and enterprise software tools. The effectiveness of the toolkit will be demonstrated trough two use cases in metal forging and glass production, providing the two foundation industry companies with direct gains in process performance.\n\n"], "extra_text": ["", "\n\n\n\n"], "status": ["", "Closed"]}
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| Nov. 20, 2023, 2:06 p.m. |
Added
35
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{"external_links": [60402]}
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| Nov. 20, 2023, 2:06 p.m. |
Created
35
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[{"model": "core.project", "pk": 15410, "fields": {"owner": null, "is_locked": false, "coped_id": "8a23d306-1aac-47ea-bf8f-c203631f6d61", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 79138, "created": "2023-11-20T13:58:04.193Z", "modified": "2023-11-20T13:58:04.194Z", "external_links": []}}]
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