Industry: Difference between revisions

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== Background Readings ==
 
== Background Readings ==
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* Rebitzer, G. et al. [https://www.sciencedirect.com/science/article/pii/S0160412003002459 Intro to life-cycle analysis] (2004)
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* Gao, J. et al. [https://docs.google.com/a/google.com/viewer?url=www.google.com/about/datacenters/efficiency/internal/assets/machine-learning-applicationsfor-datacenter-optimization-finalv2.pdf Google’s white paper on ML for data center optimization]
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* Gustavsson, J. et al. [http://www.madr.ro/docs/ind-alimentara/risipa_alimentara/presaentation_food_waste.pdf Intro to food waste] (2011)
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* Wikipedia - [https://en.wikipedia.org/wiki/Industry_4.0 Intro to Industry 4.0]
   
 
== Online Courses and Course Materials ==
 
== Online Courses and Course Materials ==
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==Community==
 
==Community==
 
===Journals and conferences===
 
===Journals and conferences===
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* [https://www.sciencedirect.com/journal/journal-of-cleaner-production Journal of Cleaner Production]
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* [https://www.springer.com/engineering/industrial+management/journal/170 Industrial Management Journal]
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* [https://www.journals.elsevier.com/energy-and-buildings Energy and Buildings]
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===Societies and organizations===
 
===Societies and organizations===
 
===Past and upcoming events===
 
===Past and upcoming events===
 
==Libraries and Tools==
 
==Libraries and Tools==
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* [https://materialsproject.org/ The Materials Project]
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* [https://icsd.fiz-karlsruhe.de/ Inorganic Crystal Structure Database]
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* [https://www.cas.org/products/scifinder SciFinder (paid)]
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==Data==
 
==Data==
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* [https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength UCI Machine Learning Repository datasets, e.g. “Concrete Compressive Strength”]
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==References==
 
==References==

Revision as of 19:24, 31 August 2020

Selected opportunities to use machine learning to reduce greenhouse gas emissions in industry.
Industrial production, logistics, and building materials are leading causes of difficult-to-eliminate GHG emissions[1]. Fortunately for ML researchers, the global industrial sector spends billions of dollars annually gathering data on factories and supply chains[2] – aided by improvements in the cost and accessibility of sensors and other data-gathering mechanisms (such as QR codes and image recognition). The availability of large quantities of data, combined with affordable cloud-based storage and computing, indicates that industry may be an excellent place for ML to make a positive climate impact. ML demonstrates considerable potential for reducing industrial GHG emissions under the following circumstances:
  • When there is enough accessible, high-quality data around specific processes or transport routes.
  • When firms have an incentive to share their proprietary data and/or algorithms with researchers and other firms.
  • When aspects of production or shipping can be readily fine-tuned or adjusted, and there are clear objective functions.
  • When firms’ incentives align with reducing emissions (for example, through efficiency gains, regulatory compliance, or high GHG prices).

In particular, ML can potentially reduce global emissions by helping to streamline supply chains, improve production quality, predict machine breakdowns, optimize heating and cooling systems, and prioritize the use of clean electricity over fossil fuels[3][4][5][6]. However, it is worth noting that greater efficiency may increase the production of goods and thus GHG emissions (via the Jevons paradox) unless industrial actors have sufficient incentives to reduce overall emissions[7].

Machine Learning Application Areas

  • Optimizing supply chains
  • Improving materials
  • Optimizing energy usage

Background Readings

Online Courses and Course Materials

Community

Journals and conferences

Societies and organizations

Past and upcoming events

Libraries and Tools

Data

References

  1. Davis, Steven J.; Lewis, Nathan S.; Shaner, Matthew; Aggarwal, Sonia; Arent, Doug; Azevedo, Inês L.; Benson, Sally M.; Bradley, Thomas; Brouwer, Jack; Chiang, Yet-Ming; Clack, Christopher T. M. (2018-06-29). "Net-zero emissions energy systems". Science. 360 (6396): eaas9793. doi:10.1126/science.aas9793. ISSN 0036-8075.
  2. Mike Gualtieri, Noel Yuhanna, Holger Kisker, Rowan Curran, Brandon Purcell, Sophia Christakis, Shreyas Warrier, and Matthew Izzi. The Forrester Wave: Big data streaming analytics, Q1 2016. Forrester.com, January 2016.
  3. Kazi, Rubaiat Habib; Grossman, Tovi; Cheong, Hyunmin; Hashemi, Ali; Fitzmaurice, George (2017-10-20). "DreamSketch". Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology. New York, NY, USA: ACM. doi:10.1145/3126594.3126662. ISBN 978-1-4503-4981-9.
  4. Richard Evans and Jim Gao. DeepMind AI reduces Google data centre cooling bill by 40%. DeepMind blog, 20, 2016.
  5. Zhang, Xiao; Hug, Gabriela; Kolter, J. Zico; Harjunkoski, Iiro (2016-07). "Model predictive control of industrial loads and energy storage for demand response". 2016 IEEE Power and Energy Society General Meeting (PESGM). IEEE. doi:10.1109/pesgm.2016.7741228. ISBN 978-1-5090-4168-8. Check date values in: |date= (help)
  6. Berral, Josep Ll.; Goiri, Íñigo; Nou, Ramón; Julià, Ferran; Guitart, Jordi; Gavaldà, Ricard; Torres, Jordi (2010). "Towards energy-aware scheduling in data centers using machine learning". Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking - e-Energy '10. New York, New York, USA: ACM Press. doi:10.1145/1791314.1791349. ISBN 978-1-4503-0042-1.
  7. Sorrell, Steve (2009-04). "Jevons' Paradox revisited: The evidence for backfire from improved energy efficiency". Energy Policy. 37 (4): 1456–1469. doi:10.1016/j.enpol.2008.12.003. ISSN 0301-4215. Check date values in: |date= (help)