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DC Field | Value | Language |
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dc.contributor.author | Magenthirarajah, V | - |
dc.contributor.author | Gamage, A | - |
dc.contributor.author | Chandrasiri, S | - |
dc.date.accessioned | 2023-03-03T08:44:17Z | - |
dc.date.available | 2023-03-03T08:44:17Z | - |
dc.date.issued | 2022-12-09 | - |
dc.identifier.citation | V. Magenthirarajah, A. Gamage and S. Chandrasiri, "Carbon Emission Optimization Using Linear Programming," 2022 4th International Conference on Advancements in Computing (ICAC), Colombo, Sri Lanka, 2022, pp. 494-498, doi: 10.1109/ICAC57685.2022.10025276 | en_US |
dc.identifier.isbn | 979-8-3503-9809-0 | - |
dc.identifier.uri | https://rda.sliit.lk/handle/123456789/3289 | - |
dc.description.abstract | In this fast-growing modernization, excess carbon emission plays a crucial role in climate change. Targeting and experimenting with sustainable ways of Carbon neutrality and management is the pathway toward a greener society. Data show that factories and industries take a high market stake in carbon emission and management. In actions, Governments defined a limit for carbon emissions to each organization which is called carbon credit. Every organization must focus on reducing carbon emissions. This is a critical task for each organization, In some cases, it is still not possible to explore other sustainable options. An innovative solution proposed for the above scenario is to implement a real-time platform that can provide insights into the most up-to-date emission statistics of the organization. This paper provides advanced analytics and precise proactive planning and actions in the simplest form and a discussion on future elaborations and insights about conclusions. By finding the minimum optimal emission values of each emission source, organizations can maintain carbon emissions without exceeding their carbon credit. Also, how industries and factories can create a smart carbon optimization system that can create an even greener society. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartofseries | 2022 4th International Conference on Advancements in Computing (ICAC); | - |
dc.subject | Optimization | en_US |
dc.subject | Linear Programming | en_US |
dc.subject | Carbon Emission | en_US |
dc.title | Carbon Emission Optimization Using Linear Programming | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/ICAC57685.2022.10025276 | en_US |
Appears in Collections: | 4th International Conference on Advancements in Computing (ICAC) | 2022 Department of Information Technology Research Papers - IEEE Research Papers - SLIIT Staff Publications Research Publications -Dept of Information Technology |
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Carbon_Emission_Optimization_Using_Linear_Programming.pdf Until 2050-12-31 | 473.64 kB | Adobe PDF | View/Open Request a copy |
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