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[{"model": "core.projectfund", "pk": 29863, "fields": {"project": 7082, "organisation": 4, "amount": 244479, "start_date": "2018-08-31", "end_date": "2019-08-30", "raw_data": 49421}}]
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{"external_links": []}
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| April 11, 2022, 3:48 a.m. |
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[{"model": "core.projectfund", "pk": 21997, "fields": {"project": 7082, "organisation": 4, "amount": 244479, "start_date": "2018-08-31", "end_date": "2019-08-30", "raw_data": 32616}}]
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| April 11, 2022, 3:48 a.m. |
Created
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[{"model": "core.projectorganisation", "pk": 82966, "fields": {"project": 7082, "organisation": 8743, "role": "PARTICIPANT_ORG"}}]
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| April 11, 2022, 3:48 a.m. |
Created
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[{"model": "core.projectorganisation", "pk": 82965, "fields": {"project": 7082, "organisation": 8743, "role": "LEAD_ORG"}}]
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| April 11, 2022, 3:48 a.m. |
Created
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[{"model": "core.projectperson", "pk": 51435, "fields": {"project": 7082, "person": 9873, "role": "PM_PER"}}]
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| April 11, 2022, 1:48 a.m. |
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{"title": ["", "Smart Transient Gas Distribution Network Operations Advisor Platform"], "description": ["", "\n"The UK Government has identified that the UK has the potential to lead the World in the provision of smart energy supply systems and specifically those that match energy supply and demand profiles. Two areas of particular significance are improving the ease and cost of integration of distributed generation and the use of smart systems that integrate energy generation and demand at local, regional or national scale. Atmos International Ltd (Atmos) is a supplier of pipeline operation management systems (including simulation, gas management and leak detection technology) to the oil, gas, water and associated industries. Headquartered in Manchester UK, the company has implemented these technologies on hundreds of pipelines in over 50 countries, including major oil and gas companies such as Shell, BP, ExxonMobil, and Total.\n\nThe aim of the 'Smart Transient Gas Distribution Network Operations Advisor Platform' (the Project) is to develop and evaluate a new software solution that uses Machine Learning (ML) algorithms to optimise the operation of gas pipeline networks so that they can operate at minimum cost while meeting the full set of demands of their customers. The machine learning technology will be combined with a state-of-the-art gas flow distribution simulator to enable both steady state and transient condition analysis thereby enabling the handling of unexpected conditions e.g. system failure, leaks, sabotage, etc. This approach will match gas supply and demand profiles. The software will enable a supplier to integrate energy generation and demand at local, regional and national scale.\n\nThe key benefits that accrue from this new approach are:\n\n* Users of the software will have access to a smart real-time gas network expert advisory system to provide recommendations on how to control their gas transport network to achieve long term improved network performance. It will reduce gas transportation costs whilst ensuring all transport service level agreements/requirements are achieved;\n* Faster and reliable decision making will help gas network operators minimise the consequences of interruptions e.g. unplanned stations shutdowns or other incidents. This will mean improved safety of people, protection of the environment and gas transport equipment and saving of financial cost. The optimisation provides reduction of fuel and energy consumption. Also, it is both environmentally and economically attractive;\n* Maximised utilisation of the pipeline capacity whilst meeting the contractual requirements in event of supply shortages due to unplanned events. This would enable the gas transport companies to maintain or increase sales and avoid penalty payment."\n\n"], "extra_text": ["", "\n\n\n\n"], "status": ["", "Closed"]}
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| April 11, 2022, 1:48 a.m. |
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{"external_links": [25652]}
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| April 11, 2022, 1:48 a.m. |
Created
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[{"model": "core.project", "pk": 7082, "fields": {"owner": null, "is_locked": false, "coped_id": "8d449bb1-e16c-40c9-a794-6ef6d86744c1", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 32599, "created": "2022-04-11T01:44:39.272Z", "modified": "2022-04-11T01:44:39.272Z", "external_links": []}}]
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