Industry: Difference between revisions

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=== Optimizing supply chains ===
 
* [[Freight consolidation|'''Freight Consolidationconsolidation''']]: 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|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 Gasgas Emissionsemissions Mappingmapping]]''' '''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|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|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|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 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|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 Maintenancemaintenance''']]: 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].
* '''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 ==
==Conferences, Journals, and Professional Organizations==
===Major conferences===
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===Major journals===