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, 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 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:
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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.
* '''[[Greenhouse Gas Emissions Detection|Greenhouse
=== Developing cleaner products and materials ===
* '''[[Accelerated Science
* '''[[Generative Design]]:''' ML-enabled 3D
* '''[[Additive Manufacturing]]''': Novel additive manufacturing techniques such as 3D printing allow for the production of unusual shapes that use less material and/or
=== Optimizing factory operations ===
*'''[[Electricity Supply Forecasting|
*[[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
*'''[[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.
== Background Readings ==
=== Primers ===
* '''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].
▲*
* '''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].
*'''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 ==
'''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==
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===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].
* '''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].
* '''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].
===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].
*
▲* 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==
* '''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].
==Data==
''See the subpages listed [[Industry#Machine%20Learning%20Application%20Areas|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 [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==
* '''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].
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==References==
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