Industry: Difference between revisions

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(Added summaries everywhere)
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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 materials are leading causes 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>. Fortunately for ML researchers, the global industrial sector spends billions of dollars annually gathering data on factories 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> – aided by improvements in the cost and accessibility of cloud-based storage and computing, as well as sensors and data-gathering mechanisms such as QR codes and image recognition (vaguely referred to as "Industry 4.0" and "Smart/Connected Factories").
 
''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 materials are leading causes 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>. Fortunately for ML researchers, the global industrial sector spends billions of dollars annually gathering data on factories 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> – aided by improvements in the cost and accessibility of cloud-based storage and computing, as well as sensors and data-gathering mechanisms such as QR codes and image recognition (vaguely referred to as "Industry 4.0" and "Smart/Connected Factories").
   
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>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>.
+
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>.
   
 
ML can have a substantial positive climate impact on the industrial sector under the following circumstances:
 
ML can have a substantial positive climate impact on the industrial sector under the following circumstances:
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* '''[[Goods Demand Forecasting]]:''' The production, shipment, and climate-controlled warehousing of excess products is a major source of industrial GHG emissions. ML may be able to mitigate overproduction and/or the overstocking of goods by improving models for forecasting consumer demand, especially for perishable goods or "fashionable" items that quickly become obsolete.
 
* '''[[Goods Demand Forecasting]]:''' The production, shipment, and climate-controlled warehousing of excess products is a major source of industrial GHG emissions. ML may be able to mitigate overproduction and/or the overstocking of goods by improving models for forecasting consumer demand, especially for perishable goods or "fashionable" items that quickly become obsolete.
   
* '''[[Greenhouse Gas Emissions Detection|Greenhouse gas emissions mapping]]''' '''for supply chains''': While some factories release publicly-available data on their emissions, this data is not available (or is misreported) in many cases, especially in emerging markets. ML can help map greenhouse gas emissions using a combination of remote sensing and on-the-ground data, leading both consumers and upstream suppliers to make greener decisions in sourcing products.
+
* '''[[Greenhouse Gas Emissions Detection|Greenhouse Gas Emissions Mapping]]''' '''for supply chains''': While some factories release publicly-available data on their emissions, this data is not available (or is misreported) in many cases, especially in emerging markets. ML can help map greenhouse gas emissions using a combination of remote sensing and on-the-ground data, leading both consumers and upstream suppliers to make greener decisions in sourcing products.
   
 
=== Developing cleaner products and materials ===
 
=== Developing cleaner products and materials ===
   
* '''[[Accelerated Science|Accelerated science]] for clean energy technologies''': Designing new materials is important for many applications, including energy storage via batteries or solar/chemical fuels. ML can help suggest promising materials to try, thereby speeding up the materials discovery process.
+
* '''[[Accelerated Science]] for clean energy technologies''': Designing new materials is important for many applications, including concrete and steel alternatives, energy storage via batteries, and solar/chemical fuels. ML can help suggest promising materials to try, thereby speeding up the materials discovery process.
* '''[[Generative Design]]:''' ML-enabled 3D-modelling software can can help create new designs for physical structures that reduce the need for carbon-intensive materials – especially cement and steel.
+
* '''[[Generative Design]]:''' ML-enabled 3D design software can can help create new designs for physical structures that reduce the need for carbon-intensive materials – especially cement and steel.
* '''[[Additive Manufacturing]]''': Novel manufacturing techniques such as 3D printing allow for the production of unusual shapes that use less material or different types of material, but may be impossible to produce through traditional manufacturing methods.
+
* '''[[Additive Manufacturing]]''': Novel additive manufacturing techniques such as 3D printing allow for the production of unusual shapes that use less material and/or more carbon-friendly types of material, but may be impossible to produce through traditional manufacturing methods. ML can improve the algorithms controlling the printing process, leading to more faster and more accurate production.
   
 
=== Optimizing factory operations ===
 
=== Optimizing factory operations ===
   
*'''[[Electricity Supply Forecasting|Supply]] and [[Energy Demand Forecasting|demand]] forecasting''': The supply and demand of power must both be forecast ahead of time to inform electricity planning and scheduling. ML can help make these forecasts more accurate, improve temporal and spatial resolution, and quantify uncertainty.
+
*'''[[Electricity Supply Forecasting|Electricity supply]] and [[Energy Demand Forecasting|demand]] forecasting''': The supply and demand of power must both be forecast ahead of time to inform industry's electricity planning and scheduling, thus helping factories optimize their electrical usage for renewable energy. ML can help make these forecasts more accurate, improve temporal and spatial resolution, and quantify uncertainty.
  +
*[[Demand response|'''Demand response''']]: By accurately predicting and quickly responding to changes in the supply of electricity, factories can reduce their electricity usage during peak periods and diminish the need for utilities to resort to fossil fuels. ML can assist with helping factories optimize the timing of their production to take advantage of inconsistent solar and wind power.
* '''[[Methane Leak Detection]]''': Fertilizer factories and some other chemical plants often leak methane, a powerful greenhouse gas. ML can help detect and prevent these leaks.
+
* '''[[Methane Leak Detection]]''': Fertilizer factories and other chemical plants often leak methane, a powerful greenhouse gas. ML can help detect and prevent these leaks.
* '''[[Adaptive Systems Control]]: ....'''
 
  +
*'''[[Adaptive Systems Control]]:''' Most factories today rely on complex networks of disconnected production equipment, leading to inefficiencies in control systems such as temperature control, power usage, and material flow. ML techniques such as image recognition, regression trees, and time delay neural networks can help optimize the control and efficient automation of industrial systems.
*[[Predictive Maintenance|'''Predictive Maintenance''']]: Quickly detecting machinery faults can help reduce electricity and materials waste. ML can help detect faults in real time from machinery sensor data, or even forecast them ahead of time to enable preemptive repair or replacement.
+
* [[Predictive Maintenance|'''Predictive Maintenance''']]: Quickly detecting machinery faults can help reduce electricity and materials waste. ML can help detect faults in real time from machinery sensor data, or even forecast them ahead of time to enable preemptive repair or replacement.
* [[Demand response|'''Demand response''']]: '''<u>by reducing or shifting their electricity usage during peak periods in response to time-based rates or other forms of financial incentives.</u>'''
 
   
 
== Background Readings ==
 
== Background Readings ==
   
  +
=== Primers ===
* Rebitzer, G. et al. [https://www.sciencedirect.com/science/article/pii/S0160412003002459 Intro to life-cycle analysis] (2004)
 
  +
* '''Industry 4.0:''' Wikipedia's article on the nebulous concept of Industry 4.0. Available [https://en.wikipedia.org/wiki/Industry_4.0 here].
* 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]
 
  +
* '''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].
* Gustavsson, J. et al. [http://www.madr.ro/docs/ind-alimentara/risipa_alimentara/presaentation_food_waste.pdf Intro to food waste] (2011)
 
 
* '''Machine Learning Applications for Data Center Optimization (2016)'''<ref name=":0" />''':''' A technical paper explaining how Google optimized their data center power usage using ML. Available [https://docs.google.com/a/google.com/viewer?url=www.google.com/about/datacenters/efficiency/internal/assets/machine-learning-applicationsfor-datacenter-optimization-finalv2.pdf here].
* Wikipedia - [https://en.wikipedia.org/wiki/Industry_4.0 Intro to Industry 4.0]
 
  +
* '''Global Food Losses and Food Waste (2011):''' The UN Food and Agriculture Organization's manual on how food gets wasted across supply chains. Available [http://www.fao.org/3/mb060e/mb060e00.htm here].
*Carbon180 - [https://static1.squarespace.com/static/5b9362d89d5abb8c51d474f8/t/5b98383aaa4a998909c4b606/1536702527136/ccr02.innovationplan.FNL.pdf Building a New Carbon Economy] (substantial overlap with carbon sequestration and agriculture)
 
  +
*'''Building a New Carbon Economy (2020):''' Carbon180's manual on research and business opportunities for creating a carbon-neutral United States, including substantial references to industry. Available [https://static1.squarespace.com/static/5b9362d89d5abb8c51d474f8/t/5b98383aaa4a998909c4b606/1536702527136/ccr02.innovationplan.FNL.pdf here].
  +
  +
=== Textbooks ===
  +
  +
=== Other ===
   
 
== Online Courses and Course Materials ==
 
== Online Courses and Course Materials ==
  +
'''Autodesk Sustainability Workshop''': "free online learning resources that teach the principles and practice of sustainability in engineering and design. This site was created to help students, educators and professionals learn more about sustainability in engineering and architecture professions." Practical videos on sustainable product design, materials selection, and engineering available [https://www.youtube.com/user/AutodeskEcoWorkshop/videos here].
 
  +
* '''"Introduction to Manufacturing Systems"''': MIT's introductory course on factory operations, taught by a seasoned professor with extensive industry experience. "This course provides ways to analyze manufacturing systems in terms of material flow and storage, information flow, capacities, and times and durations of events. Fundamental topics include probability, inventory and queuing models, optimization, and linear and dynamic systems. Factory planning and scheduling topics include flow planning, bottleneck characterization, buffer and batch-size analysis, and dynamic behavior of production systems." Course materials available [https://ocw.mit.edu/courses/mechanical-engineering/2-854-introduction-to-manufacturing-systems-fall-2016/ here].
 
* '''Autodesk Sustainability Workshop''': "free online learning resources that teach the principles and practice of sustainability in engineering and design. This site was created to help students, educators and professionals learn more about sustainability in engineering and architecture professions." Practical videos on sustainable product design, materials selection, and engineering available [https://www.youtube.com/user/AutodeskEcoWorkshop/videos here].
   
 
==Conferences, Journals, and Professional Organizations==
 
==Conferences, Journals, and Professional Organizations==
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===Major journals===
 
===Major journals===
   
  +
* '''Journal of Cleaner Production:''' "[A]n international, transdisciplinary journal focusing on Cleaner Production, Environmental, and Sustainability research and practice. Through our published articles, we aim at helping societies become more sustainable. 'Cleaner Production' is a concept that aims at preventing the production of waste, while increasing efficiencies in the uses of energy, water, resources, and human capital." Website [https://www.sciencedirect.com/journal/journal-of-cleaner-production here].
* [https://www.sciencedirect.com/journal/journal-of-cleaner-production Journal of Cleaner Production]
 
  +
* '''International Journal of Advanced Manufacturing Technology:''' "[B]ridges the gap between pure research journals and the more practical publications on advanced manufacturing and systems. It therefore provides an outstanding forum for papers covering applications-based research topics relevant to manufacturing processes, machines and process integration." Website [https://www.springer.com/engineering/industrial+management/journal/170 here].
* [https://www.springer.com/engineering/industrial+management/journal/170 Industrial Management Journal]
 
  +
* '''Energy and Buildings:''' "[A]n international journal publishing articles with explicit links to energy use in buildings. The aim is to present new research results, and new proven practice aimed at reducing the energy needs of a building and improving indoor environment quality." Website [https://www.journals.elsevier.com/energy-and-buildings here].
* [https://www.journals.elsevier.com/energy-and-buildings Energy and Buildings]
 
   
 
===Major professional organizations===
 
===Major professional organizations===
  +
* '''Institute of Industrial and Systems Engineers:''' "IISE is the world's largest professional society dedicated solely to the support of the industrial engineering profession and individuals involved with improving quality and productivity. Founded in 1948, IISE is an international, nonprofit association that provides leadership for the application, education, training, research, and development of industrial engineering." Website [https://www.iise.org/Home/ here].
'''IEEE Power & Energy Society''': "[T]he world's largest forum for sharing the latest in technological developments in the electric power industry, for developing standards that guide the development and construction of equipment and systems, and for educating members of the industry and the general public." Website here.
 
 
* '''BlueGreen Alliance's Clean Economy Manufacturing Center (USA)''': "The Clean Economy Manufacturing Center (CEMC) helps manufacturers, public officials, and economic development organizations capitalize on the opportunities presented by the fast-growing clean economy." Website [https://www.bgafoundation.org/programs/clean-economy-manufacturing-center/ here].
* [https://www.ellenmacarthurfoundation.org/ Ellen MacArthur Foundation -- consortium around the circular economy and sustainable supply chains/materials development]
 
**Helps (mostly large) manufacturers consider the entire life cycle of their products Website here.
 
* The BlueGreen Alliance's Clean Economy Manufacturing Center (USA) helps American manufacturers and construction firms improve efficiency and sustainability. Website [https://www.bgafoundation.org/programs/clean-economy-manufacturing-center/ here].
 
 
==Libraries and Tools==
 
==Libraries and Tools==
   
  +
* '''Industrial Assessment Centers (USA):''' "Industrial Assessment Centers (formerly called the Energy Analysis and Diagnostic Centers) were created by the Department of Commerce in 1976 in response to the oil embargo and rising energy costs. The program was specifically focused on helping small and medium-sized manufacturing facilities cut back on unnecessary costs from inefficient energy use." This database provides a wealth of energy-saving solutions resulting from university collaborations with small factories, available [https://iac.university/ here].
* [https://materialsproject.org/ The Materials Project]
 
* [https://icsd.fiz-karlsruhe.de/ Inorganic Crystal Structure Database]
 
* [https://www.cas.org/products/scifinder SciFinder (paid)]
 
* [https://iac.university/ USA Industrial Assessment Centers: database of solutions from university collaborations with SMEs to save energy and reduce costs]
 
   
 
==Data==
 
==Data==
 
''See the subpages listed [[Industry#Machine%20Learning%20Application%20Areas|above]] for application-specific datasets.''
 
''See the subpages listed [[Industry#Machine%20Learning%20Application%20Areas|above]] for application-specific datasets.''
  +
* [https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength UCI Machine Learning Repository datasets, e.g. “Concrete Compressive Strength”]
 
  +
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==
 
==Relevant Groups and Organizations==
  +
[https://www.sasb.org/standards-overview/ Sustainability Accounting Standards Board (SASB)] -- popular global standards board for corporate sustainability efforts Website here.
 
  +
* '''Ellen MacArthur Foundation:''' "[W]e develop and promote the idea of a circular economy. We work with, and inspire, business, academia, policymakers, and institutions to mobilise systems solutions at scale, globally." This Foundation is a UK-based business consortium focused on sustainable, zero-waste supply chains and materials development. Website [https://www.ellenmacarthurfoundation.org/ here].
  +
* '''Sustainability Accounting Standards Board (SASB):''' A popular global standards board that has developed a "complete set of globally applicable industry-specific standards which identify the minimal set of financially material sustainability topics and their associated metrics for the typical company in an industry." Website [https://www.sasb.org/standards-overview/ here].
  +
 
*
 
*
*[https://carbon180.org/newcarboneconomy Carbon180 New Carbon Economy Consortium] is focused on carbon capture and sequestration, including industry, agriculture and forestry. Website here.
+
*'''Carbon180's New Carbon Economy Consortium:''' "[A]n alliance of universities, national labs, and NGOs working in partnership with industry leaders to build a carbon-conscious world." The Consortium has a particular focus on carbon capture and sequestration, including industry, agriculture and forestry. Website [https://carbon180.org/newcarboneconomy here].
 
==References==
 
==References==

Revision as of 21:55, 22 December 2020

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 Wikipedia page on this topic.
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 cloud-based storage and computing, as well as sensors and data-gathering mechanisms such as QR codes and image recognition (vaguely referred to as "Industry 4.0" and "Smart/Connected Factories").

ML can potentially reduce industrial emissions by helping to streamline supply chains, invent cleaner materials and chemicals, design for fewer materials[3], improve production quality, predict machine breakdowns, optimize heating and cooling systems[4], and prioritize the use of clean electricity over fossil fuels[5][6].

ML can have a substantial positive climate impact on the industrial sector 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).

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[7].

Machine Learning Application Areas

Optimizing supply chains

  • Freight Consolidation: Optimizing the shipping and transportation of goods across today's globalized supply chains is a complex, multifaceted challenge – especially for perishable goods. Bundling shipments together through freight consolidation can dramatically reduce the number of trips and associated GHG emissions. ML can optimize complex relationship between the various dimensions involved in shipping decisions, such as shipment mode and origin-destination pairs.
  • Goods Demand Forecasting: The production, shipment, and climate-controlled warehousing of excess products is a major source of industrial GHG emissions. ML may be able to mitigate overproduction and/or the overstocking of goods by improving models for forecasting consumer demand, especially for perishable goods or "fashionable" items that quickly become obsolete.
  • Greenhouse Gas Emissions Mapping for supply chains: While some factories release publicly-available data on their emissions, this data is not available (or is misreported) in many cases, especially in emerging markets. ML can help map greenhouse gas emissions using a combination of remote sensing and on-the-ground data, leading both consumers and upstream suppliers to make greener decisions in sourcing products.

Developing cleaner products and materials

  • Accelerated Science for clean energy technologies: Designing new materials is important for many applications, including concrete and steel alternatives, energy storage via batteries, and solar/chemical fuels. ML can help suggest promising materials to try, thereby speeding up the materials discovery process.
  • Generative Design: ML-enabled 3D design software can can help create new designs for physical structures that reduce the need for carbon-intensive materials – especially cement and steel.
  • Additive Manufacturing: Novel additive manufacturing techniques such as 3D printing allow for the production of unusual shapes that use less material and/or more carbon-friendly types of material, but may be impossible to produce through traditional manufacturing methods. ML can improve the algorithms controlling the printing process, leading to more faster and more accurate production.

Optimizing factory operations

  • Electricity supply and demand forecasting: The supply and demand of power must both be forecast ahead of time to inform industry's electricity planning and scheduling, thus helping factories optimize their electrical usage for renewable energy. ML can help make these forecasts more accurate, improve temporal and spatial resolution, and quantify uncertainty.
  • Demand response: By accurately predicting and quickly responding to changes in the supply of electricity, factories can reduce their electricity usage during peak periods and diminish the need for utilities to resort to fossil fuels. ML can assist with helping factories optimize the timing of their production to take advantage of inconsistent solar and wind power.
  • Methane Leak Detection: Fertilizer factories and other chemical plants often leak methane, a powerful greenhouse gas. ML can help detect and prevent these leaks.
  • Adaptive Systems Control: Most factories today rely on complex networks of disconnected production equipment, leading to inefficiencies in control systems such as temperature control, power usage, and material flow. ML techniques such as image recognition, regression trees, and time delay neural networks can help optimize the control and efficient automation of industrial systems.
  • Predictive Maintenance: Quickly detecting machinery faults can help reduce electricity and materials waste. ML can help detect faults in real time from machinery sensor data, or even forecast them ahead of time to enable preemptive repair or replacement.

Background Readings

Primers

  • Industry 4.0: Wikipedia's article on the nebulous concept of Industry 4.0. Available here.
  • Life Cycle Assessment (2004)[8]: A friendly paper introducing the concept of measuring carbon emissions along the entire supply chain and life-cycle of physical products. Available here.
  • Machine Learning Applications for Data Center Optimization (2016)[4]: A technical paper explaining how Google optimized their data center power usage using ML. Available here.
  • Global Food Losses and Food Waste (2011): The UN Food and Agriculture Organization's manual on how food gets wasted across supply chains. Available here.
  • Building a New Carbon Economy (2020): Carbon180's manual on research and business opportunities for creating a carbon-neutral United States, including substantial references to industry. Available here.

Textbooks

Other

Online Courses and Course Materials

  • "Introduction to Manufacturing Systems": MIT's introductory course on factory operations, taught by a seasoned professor with extensive industry experience. "This course provides ways to analyze manufacturing systems in terms of material flow and storage, information flow, capacities, and times and durations of events. Fundamental topics include probability, inventory and queuing models, optimization, and linear and dynamic systems. Factory planning and scheduling topics include flow planning, bottleneck characterization, buffer and batch-size analysis, and dynamic behavior of production systems." Course materials available here.
  • Autodesk Sustainability Workshop: "free online learning resources that teach the principles and practice of sustainability in engineering and design. This site was created to help students, educators and professionals learn more about sustainability in engineering and architecture professions." Practical videos on sustainable product design, materials selection, and engineering available here.

Conferences, Journals, and Professional Organizations

Major conferences

Major journals

  • Journal of Cleaner Production: "[A]n international, transdisciplinary journal focusing on Cleaner Production, Environmental, and Sustainability research and practice. Through our published articles, we aim at helping societies become more sustainable. 'Cleaner Production' is a concept that aims at preventing the production of waste, while increasing efficiencies in the uses of energy, water, resources, and human capital." Website here.
  • International Journal of Advanced Manufacturing Technology: "[B]ridges the gap between pure research journals and the more practical publications on advanced manufacturing and systems. It therefore provides an outstanding forum for papers covering applications-based research topics relevant to manufacturing processes, machines and process integration." Website here.
  • Energy and Buildings: "[A]n international journal publishing articles with explicit links to energy use in buildings. The aim is to present new research results, and new proven practice aimed at reducing the energy needs of a building and improving indoor environment quality." Website here.

Major professional organizations

  • Institute of Industrial and Systems Engineers: "IISE is the world's largest professional society dedicated solely to the support of the industrial engineering profession and individuals involved with improving quality and productivity. Founded in 1948, IISE is an international, nonprofit association that provides leadership for the application, education, training, research, and development of industrial engineering." Website here.
  • BlueGreen Alliance's Clean Economy Manufacturing Center (USA): "The Clean Economy Manufacturing Center (CEMC) helps manufacturers, public officials, and economic development organizations capitalize on the opportunities presented by the fast-growing clean economy." Website here.

Libraries and Tools

  • Industrial Assessment Centers (USA): "Industrial Assessment Centers (formerly called the Energy Analysis and Diagnostic Centers) were created by the Department of Commerce in 1976 in response to the oil embargo and rising energy costs. The program was specifically focused on helping small and medium-sized manufacturing facilities cut back on unnecessary costs from inefficient energy use." This database provides a wealth of energy-saving solutions resulting from university collaborations with small factories, available here.

Data

See the subpages listed above 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.

  • The Materials Project: "[C]omputed information on known and predicted materials as well as powerful analysis tools to inspire and design novel materials." Available here.
  • Inorganic Crystal Structure Database: "[T]he world's largest database for completely identified inorganic crystal structures." Available here.
  • SciFinder: Chemical and materials science database (paid), available here.
  • "Concrete Compressive Strength": Dataset of concrete compressive strength available 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 here.

Relevant Groups and Organizations

  • Ellen MacArthur Foundation: "[W]e develop and promote the idea of a circular economy. We work with, and inspire, business, academia, policymakers, and institutions to mobilise systems solutions at scale, globally." This Foundation is a UK-based business consortium focused on sustainable, zero-waste supply chains and materials development. Website here.
  • Sustainability Accounting Standards Board (SASB): A popular global standards board that has developed a "complete set of globally applicable industry-specific standards which identify the minimal set of financially material sustainability topics and their associated metrics for the typical company in an industry." Website here.
  • Carbon180's New Carbon Economy Consortium: "[A]n alliance of universities, national labs, and NGOs working in partnership with industry leaders to build a carbon-conscious world." The Consortium has a particular focus on carbon capture and sequestration, including industry, agriculture and forestry. Website here.

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: Early stage 3D design explorations with sketching and generative design". 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. 4.0 4.1 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)
  8. Rebitzer, G.; Ekvall, T.; Frischknecht, R.; Hunkeler, D.; Norris, G.; Rydberg, T.; Schmidt, W. -P.; Suh, S.; Weidema, B. P.; Pennington, D. W. (2004-07-01). "Life cycle assessment: Part 1: Framework, goal and scope definition, inventory analysis, and applications". Environment International. 30 (5): 701–720. doi:10.1016/j.envint.2003.11.005. ISSN 0160-4120.