Natural resources keep the world spinning—and AI wants to do its part, too. In this vast field, maintenance, proper input and output balancing, and fault avoidance are key to keeping businesses profitable and future-proof. Learn how AI extends such benefits by offering maintenance prediction via advanced sensors, eradication of energy consumption imbalance, fault detection and prevention for natural energy equipment, and oil rig corrosion detection. And not only that—AI can also help warehousing and manufacturing sectors integrate intelligent solutions to bolster cost and work efficiency, automation, and overall compliance.
In energetics and manufacturing, it’s all about continuity and making sure everything runs like clockwork. AI can determine even the smallest disturbances in the systems before an incident occurs thanks to its predictive algorithms, machine learning, and precise, data-based scans, including voltage measurements. External-facing equipment, such as power grids, solar arrays, and wind turbines can also be monitored using sensors and drones with infrared cameras supported by neural networks. The same is applicable to any factory equipment that is subject to wear and tear and where sensors can be installed.
Meeting Energy Demand with Generation
Another common struggle of green energy companies is an imbalance in power production and consumption. While during a light day, bulk consumption is high and goals may be unmet, at night time, the consumption falls, and the power producers have to retain or sell the surplus. This poses several dilemmas—How to account for the consumption spikes? How much energy is to be produced and how much stored?
In supply chain networks, such as renewable energy output, AI has proven to eliminate up to 50% of proneness to error, saving a lot of sales in the process as well as estimating geography-bound demand and predicting precise amounts of energy for production, so that not one spark or ray goes to waste.
Detecting and Preventing Faults
Renewable resources, such as the sun, wind, water, and biomass, enable humankind to meet its energy needs without polluting the environment. Increasingly common issues of clean energy producers are downtimes and power cuts due to unexpected device or power grid failures. AI allows for detecting damaged wires, solar panels, and windmills by drones equipped with infrared cameras. AI technologies may also schedule maintenance for hydropower turbines and bioreactors. Therefore, cumbersome quality assurance checks of the equipment are no longer needed. This means the maintenance is performed only if necessary, only where it is needed, and ahead of the expected breakage.
Smart Seismic Survey
A seismic survey is a method used during the exploration phase of oil and gas development. The energy produced by instruments such as a seismic vibrator (on land) or an airgun (in water) releases seismic waves that go through the earth’s layers and “bounce back” from different rock layers. The reflected and refracted seismic waves are recorded and manually interpreted by human experts. Labeled seismograms give a first idea of what is present underneath the ground or seabed (oil, gas, water, faults, folds, etc.) without having to actually drill. Months and years are usually needed to collect and analyze seismic data and to find a prospective place to drill a well. Reportedly, two geophysicists can interpret and label the same seismogram in two different ways.
By stamping out time-demanding tasks employees have to face, AI can analyze seismic surveys jointly with well-logs in real-time through deep learning, eliminating months and years of delivery, exploration, and markup of the data as well as human bias.
Offshore constructions, such as oil platforms, drilling rigs, ships, wind farms, and others, cost an arm and a leg—and their servicing no less. Imagine stopping a 30,000-feet feet oil rig, producing 5000 liters of crude oil daily, for a week or two for the inspection to take place.
Here, AI can take over as the “visual inspector,” auto-detecting corrosive surfaces, structural defects, and coating breakdowns, increasing asset lifetime, predicting risks, and sustaining corrosion management. This ultimately eliminates the need for reaching out to expensive offshore specialists (along with their transportation, accommodation, and equipment costs), limiting proneness to human error due to lack of specialist training or inexperience, and bolstering safety by avoiding dangerous, high-altitude, and confined inspection spots.
Drones, robots, and robust analytics tools are what keep the giant, called manufacturer, pushing industry boundaries. Without properly integrated software, however, even great factories leave much to be desired. Utilizing machine learning and advanced software integration, manufacturers can upkeep cost-beneficial lines, reduce equipment outages, automate real-time quality control and maintenance, predict and prevent device malfunctions in time—keeping released products high-quality and staying compliant with industry standards and regulations. A machine talking to a machine–this is where Industry 4.0 comes into full force.
Warehouse management intelligence
Warehouses, docks, oil rig pipe systems, and other supply chain elements are typically managed by humans. AI predominantly depends on data—and routine tasks usually lead humans to make mistakes when they enter information into databases. AI helps to ensure data accuracy is attained by tracking inventory item balance, location as well as state to boost warehousing operations efficiency, logistics, and cost-efficiency altogether.
$390 Billion of Savings Annually
According to McKinsey research, smart mining can save between $290 and $390 billion annually for mineral raw materials producers thanks to data analysis and automation.
80% Save on Locating New Mines
AI affords a better understanding of the terrain where exploitation is to begin. Mining and oil & gas companies can save up to 80% while locating new deposits of minerals and oil.
$37 Mln of Investments in AI
The US Department of Energy has announced $37 mln in funding for artificial intelligence R&D as there is tremendous potential to lower energy production costs.
20% Cheaper Green Energy
AI algorithms increase the wind energy value and save 20% of the energy cost.