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The last few decades brought one of the largest explosions of innovation in human history. Life as we know it looks markedly different since the 1990s and 2000s.
Today, more people drive automobiles than ever and consume entertainment and information on the internet. Both innovations improved human life. However, all this growth in such a short time has left us with convenient and widespread but extremely energy-hungry physical and digital infrastructures.
Artificial intelligence (AI) is an emerging tool that may help us engineer better energy efficiency into everything we do. Here are several ways allying with AI improves workflows and profitability.
Many processing and manufacturing facilities use steam as a power source, including chemical plants. Applying AI to such an environment, alongside Internet of Things (IoT) sensors, yields considerable data on steam availability. Thanks to this exchange of information, the system can optimize itself and achieve the ideal balance and flow for each area demanding power.
Artificial intelligence can save a great deal of energy and prevent the ejection of a substantial amount of carbon. AI that serves environmental stewardship functions of this nature could reduce industries’ carbon intensity by 8% by 2030.
Oil and gas companies must leverage every area of savings available to them, and AI also helps here. Shell, which has stakes in oil and gas, uses an AI platform that flags equipment automatically if impending faults or failures are detected. Shell claims the system recovered $1 billion in savings in 2019.
Natural gas and oil operations often rely on sprawling infrastructure in the form of wells and pipelines. Driving the lengths of these pipes or to remote infrastructure features to do routine inspections is extremely wasteful. The labor, time, material resources and gasoline could be better spent elsewhere.
Sensors on valves and embedded in compressor stations can provide a sort of central nervous system for distributed infrastructure. Although gas and oil pipelines will ideally not be necessary for much longer, AI makes physically inspecting them a more efficient and less wasteful and costly endeavor.
AI platforms are also vital to the energy industry because they help maximize the potential draw from each gas or oil field. They can use powerful cameras and other detection equipment to study the subsurface in detail. This provides teams with actionable insights to optimize their approach and get as much fuel energy from the site as possible before moving on.
Maximizing resource utilization – including the energy required to fabricate brand-new products – is a critical mission in the manufacturing industry. AI can be leveraged for something called generative design. This is where engineers choose a fixed set of parameters – such as a minimum amount of material wasted or a particular performance range – and then an AI program to autonomously generate several design candidates based on the requirements.
The implications across industries are huge. Imaging AI designing material goods limits waste and resource exploitation. It could also help engineers improve the efficiency of engines, turbines, actuators, robots and other mechanical products the world relies on.
In one example, a U.K. automaker used generative design to bring down the weight of its electric car by an impressive 40%. AI led the team to new milling techniques that created a far lighter automobile without compromising its structural integrity. The material and cost savings should be considerable, not to mention the energy savings from the electric motor pushing a much lighter vehicle around. The more efficient an electric car, the greater its driving range.
The best-known examples of utilizing AI to achieve energy savings occur in homes and offices everywhere already, thanks to smart thermostats, surveillance systems, and other connected and aware residential or commercial features. Using AI here ensures such systems only kick on when needed and don’t unnecessarily waste power.
Learning thermostats deliver actionable data to property owners or managers, and they tune indoor temperatures automatically and perfectly according to the conditions outside, owner preferences and whether there are occupants. Energy Star provides a list of smart thermostat models independently certified to save energy. Some products claim savings of up to 26%.
This is just the beginning of what AI is capable of. All this data analysis and digital infrastructure serves important purposes throughout the energy sector and industries that rely on it heavily, but AI also has environmental costs of its own. In the years ahead, it will be necessary for experts in both realms to collaborate and better understand the necessary tradeoff between computing power and environmental stewardship.
In the meantime, these applications for AI in saving energy and reducing ecological footprints across industries should be food for thought for innovators everywhere.
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