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Industry: Difference between revisions
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''This page is about the intersection of industrial systems and machine learning in the context of climate change mitigation. For an overview of industry as a whole, please see the [https://en.wikipedia.org/wiki/Industry Wikipedia page] on this topic.''[[File:Industry.png|thumb|500x500px|Selected opportunities to use machine learning to reduce greenhouse gas emissions in industry.|alt=]]Industrial production is a major cause of difficult-to-eliminate GHG emissions<ref>{{Cite journal|last=Davis|first=Steven J.|last2=Lewis|first2=Nathan S.|last3=Shaner|first3=Matthew|last4=Aggarwal|first4=Sonia|last5=Arent|first5=Doug|last6=Azevedo|first6=Inês L.|last7=Benson|first7=Sally M.|last8=Bradley|first8=Thomas|last9=Brouwer|first9=Jack|last10=Chiang|first10=Yet-Ming|last11=Clack|first11=Christopher T. M.|date=2018-06-29|title=Net-zero emissions energy systems|url=https://www.sciencemag.org/lookup/doi/10.1126/science.aas9793|journal=Science|language=en|volume=360|issue=6396|pages=eaas9793|doi=10.1126/science.aas9793|issn=0036-8075}}</ref>, representing over 30% of global GHG emissions in 2010.<ref name=":1">Fischedick M., J. Roy, A. Abdel-Aziz, A. Acquaye, J.M. Allwood, J.-P. Ceron, Y. Geng, H. Kheshgi, A. Lanza, D. Perczyk, L. Price, E. Santalla, C. Sheinbaum, and K. Tanaka, 2014: Industry. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Available from https://www.ipcc.ch/site/assets/uploads/2018/02/ipcc_wg3_ar5_chapter10.pdf.</ref> The IPCC estimates that energy density can be reduced by up to 25% simply through energy efficiency measures such as replacing and upgrading older equipment, but getting to carbon neutral will require switching carbon-intensive feedstocks, new materials science, improving product life cycles, streamlining supply chains, and even reducing consumer demand.<ref name=":1" />
The global industrial sector – dominated by large firms – spends billions of dollars annually gathering data on their own factory operations and and supply chains<ref>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.</ref>, which can potentially be fed into ML algorithms to help improve production efficiency and decrease carbon-intensive energy use. Such data collection has been facilitated by rapid improvements in sensors, automation technologies, and image recognition, assisted by the growing accessibility and cost of computing infrastructure. This notion of interconnected factory equipment, real-time data collection, and autonomous feedback loops has been referred to variously as [https://en.wikipedia.org/wiki/Fourth_Industrial_Revolution "Industry 4.0"] (or "Industrie 4.0" to recognize its German roots), [https://www2.deloitte.com/us/en/insights/focus/industry-4-0/smart-factory-connected-manufacturing.html "Smart/Connected Factories"], [https://en.wikipedia.org/wiki/Industrial_internet_of_things "Industrial Internet of Things (IIoT)"], and [https://www.ptc.com/en/blogs/corporate/what-is-a-digital-thread "digital thread"] (connectivity across the supply chain/product life cycle).
By intelligently analyzing these emerging factory and supply chain data, ML can lower industrial emissions by helping industry in the following ways: switching to low-carbon fuel sources<ref>{{Cite journal|last=Berral|first=Josep Ll.|last2=Goiri|first2=Íñigo|last3=Nou|first3=Ramón|last4=Julià|first4=Ferran|last5=Guitart|first5=Jordi|last6=Gavaldà|first6=Ricard|last7=Torres|first7=Jordi|date=2010|title=Towards energy-aware scheduling in data centers using machine learning|url=http://dx.doi.org/10.1145/1791314.1791349|journal=Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking - e-Energy '10|location=New York, New York, USA|publisher=ACM Press|doi=10.1145/1791314.1791349|isbn=978-1-4503-0042-1}}</ref> <ref>{{Cite journal|last=Zhang|first=Xiao|last2=Hug|first2=Gabriela|last3=Kolter|first3=J. Zico|last4=Harjunkoski|first4=Iiro|date=2016-07|title=Model predictive control of industrial loads and energy storage for demand response|url=http://dx.doi.org/10.1109/pesgm.2016.7741228|journal=2016 IEEE Power and Energy Society General Meeting (PESGM)|publisher=IEEE|doi=10.1109/pesgm.2016.7741228|isbn=978-1-5090-4168-8}}</ref>; optimizing heating, ventilation, and air conditioning (HVAC) systems<ref name=":0">Richard Evans and Jim Gao. DeepMind AI reduces Google data centre cooling bill by 40%. DeepMind blog, 20, 2016.</ref>; improving production quality and efficiency; predicting equipment failures ahead of time; facilitating discoveries in material science and chemical engineering; designing for more efficient material use<ref>{{Cite journal|last=Kazi|first=Rubaiat Habib|last2=Grossman|first2=Tovi|last3=Cheong|first3=Hyunmin|last4=Hashemi|first4=Ali|last5=Fitzmaurice|first5=George|date=2017-10-20|title=DreamSketch: Early stage 3D design explorations with sketching and generative design.|url=http://dx.doi.org/10.1145/3126594.3126662|journal=Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology|location=New York, NY, USA|publisher=ACM|volume=|pages=|doi=10.1145/3126594.3126662|isbn=978-1-4503-4981-9|via=}}</ref>; and streamlining supply chains.
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