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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, logistics, and building materialsis area leadingmajor causescause 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 Fortunatelyname=":1">Fischedick forM., MLJ. researchersRoy, theA. globalAbdel-Aziz, industrialA. sectorAcquaye, spendsJ.M. billionsAllwood, 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 dollarsClimate annuallyChange. gatheringContribution dataof Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on factoriesClimate andChange supply[Edenhofer, chains<ref>MikeO., GualtieriR. Pichs-Madruga, NoelY. YuhannaSokona, HolgerE. KiskerFarahani, RowanS. CurranKadner, BrandonK. PurcellSeyboth, SophiaA. ChristakisAdler, ShreyasI. WarrierBaum, andS. MatthewBrunner, IzziP. TheEickemeier, ForresterB. Wave:Kriemann, BigJ. dataSavolainen, streamingS. analyticsSchlömer, Q1C. 2016von Stechow, T. ForresterZwickel and J.comC. Minx (eds.)]. Cambridge University Press, JanuaryCambridge, United Kingdom and New York, NY, 2016USA. Available from https://www.ipcc.ch/site/assets/uploads/2018/02/ipcc_wg3_ar5_chapter10.pdf.</ref> The aidedIPCC estimates that energy density can be reduced by improvementsup into the25% costsimply andthrough accessibilityenergy ofefficiency cloud-basedmeasures storagesuch as replacing and computingupgrading older equipment, asbut wellgetting asto sensorscarbon andneutral data-gatheringwill mechanismsrequire suchswitching ascarbon-intensive QRfeedstocks, codesnew andmaterials imagescience, recognitionimproving (vaguelyproduct referredlife tocycles, asstreamlining "Industrysupply 4.0"chains, and even reducing consumer demand.<ref name="Smart:1" /Connected> Factories").
 
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).
ML can potentially reduce industrial emissions by helping to streamline supply chains, invent cleaner materials and chemicals, design for fewer materials<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>, improve production quality, predict machine breakdowns, optimize heating and cooling systems<ref name=":0">Richard Evans and Jim Gao. DeepMind AI reduces Google data centre cooling bill by 40%. DeepMind blog, 20, 2016.</ref>, and prioritize the use of clean electricity over fossil fuels<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><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>.
 
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.
ML can have a substantial positive climate impact on the industrial sector under the following circumstances:
 
YetNonetheless, greaterimproving efficiencyindustrial mayemissions increaserequires the productionavailability ofand goodssharing andof thushigh-quality GHG emissionsdata (viawhich theis Jevonsoften paradoxproprietary to firms), unlessas industrialwell actorsas havethe sufficientalignment of firms' incentives towith reduceactually overallreducing emissions instead of simply producing more goods at a lower cost.<ref>{{Cite journal|last=Sorrell|first=Steve|date=2009-04|title=Jevons’ Paradox revisited: The evidence for backfire from improved energy efficiency|url=http://dx.doi.org/10.1016/j.enpol.2008.12.003|journal=Energy Policy|volume=37|issue=4|pages=1456–1469|doi=10.1016/j.enpol.2008.12.003|issn=0301-4215}}</ref> In addition, ML is most useful when factory processes or complex supply chains can actually be adjusted accordingly, and when objective functions are clear.
* 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).
 
Yet 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<ref>{{Cite journal|last=Sorrell|first=Steve|date=2009-04|title=Jevons’ Paradox revisited: The evidence for backfire from improved energy efficiency|url=http://dx.doi.org/10.1016/j.enpol.2008.12.003|journal=Energy Policy|volume=37|issue=4|pages=1456–1469|doi=10.1016/j.enpol.2008.12.003|issn=0301-4215}}</ref>.
 
== Machine Learning Application Areas ==
 
== Background Readings ==
* '''The IPCC's Climate Change 2014 section on Industry'''<ref name=":1" />''':''' A comprehensive summary of the sources of industrial emissions worldwide and their potential for mitigation. Available [https://www.ipcc.ch/site/assets/uploads/2018/02/ipcc_wg3_ar5_chapter10.pdf here].
* '''Industry 4.0:''' Wikipedia's article on the nebulous concept of Industry 4.0. Available [https://en.wikipedia.org/wiki/Industry_4.0 here].
* '''Life Cycle Assessment (2004)'''<ref>{{Cite journal|last=Rebitzer|first=G.|last2=Ekvall|first2=T.|last3=Frischknecht|first3=R.|last4=Hunkeler|first4=D.|last5=Norris|first5=G.|last6=Rydberg|first6=T.|last7=Schmidt|first7=W. -P.|last8=Suh|first8=S.|last9=Weidema|first9=B. P.|last10=Pennington|first10=D. W.|date=2004-07-01|title=Life cycle assessment: Part 1: Framework, goal and scope definition, inventory analysis, and applications|url=http://www.sciencedirect.com/science/article/pii/S0160412003002459|journal=Environment International|language=en|volume=30|issue=5|pages=701–720|doi=10.1016/j.envint.2003.11.005|issn=0160-4120}}</ref>: A friendly paper introducing the concept of measuring carbon emissions along the entire supply chain and life-cycle of physical products. Available [https://www.sciencedirect.com/science/article/pii/S0160412003002459 here].
 
==Data==
{{SectionStub}}
''See the subpages listed [[Industry#Machine%20Learning%20Application%20Areas|above]] for application-specific datasets.''
 
''See the subpages listed [[Industry#Machine%20Learning%20Application%20Areas|above]] (e.g., the subpage on [[Accelerated Science|accelerated science]]) for application-specific datasets.''
Most industrial information is proprietary, so it may be useful for researchers to connect directly with factories, to negotiate confidential access to relevant data.
 
Most industrial information is proprietary, so it may be useful for researchers to connect directly with factories, to negotiate confidential access to relevant data.
* '''The Materials Project''': "[C]omputed information on known and predicted materials as well as powerful analysis tools to inspire and design novel materials." Available [https://materialsproject.org/ here].
*'''Inorganic Crystal Structure Database''': "[T]he world's largest database for completely identified inorganic crystal structures." Available [https://icsd.products.fiz-karlsruhe.de/en/ here].
*'''SciFinder''': Chemical and materials science database (paid), available [https://www.cas.org/products/scifinder here].
*'''"Concrete Compressive Strength"''': Dataset of concrete compressive strength available [https://archive.ics.uci.edu/ml/datasets/ here] via the UCI Machine Learning Repository.
*'''Open Catalyst Project:''' Dataset of 1.2 million molecular relaxations with results from over 250 million DFT calculations, aimed towards the discovery of new catalysts for use in renewable energy storage. Available [https://opencatalystproject.org/index.html here].
 
==Relevant Groups and Organizations==
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