70% of global electricity is consumed by industrial machines, often inefficiently. Optimizing these processes can significantly cut costs and reduce environmental impact. Here are four ways industrial IoT energy management can help.
The Industry 4.0 era is an opportunity to make production more efficient. For example, industrial manufacturing solutions allow manufacturing and mining companies to predict how much energy they will need and then find ways to reduce it. This optimization typically has two levels or stages:
You can enhance IoT-based solutions with advanced tech, such as:
Managing energy with IoT is essential and trending due to rising concerns about carbon footprints. IoT energy monitoring promotes sustainability while reducing operating expenses, leading to increased adoption in the coming years.
Let’s take a look at the most promising application areas of industrial IoT energy management.
In mining, proper ventilation is crucial due to equipment emissions in confined spaces. However, ventilation can account for up to 40% of the total energy consumption of a mining operation. The ventilation-on-demand (VoD) system addresses this by providing the right air quality and quantity as needed. It uses tags to locate personnel and equipment, and sensors to measure airflow. The system then adjusts ventilation where needed and can schedule airflow for different mine areas.
While traditional systems ventilate the entire mine, including areas that don’t function, the VoD system can direct air only where and when it’s needed. Such optimization results in much lower electricity use for mining companies and reduced CO2 emissions.
Newmont Corporation, the world's largest gold mining company, aimed to boost productivity and reduce energy costs at their Eleonore mine in Canada. Partnering with Howden, they installed a VoD system, automating ventilation across 30 monitoring stations with flow and gas sensors.
Workers and vehicles use RFID tags for location tracking, allowing the VoD software to ensure optimal air supply in occupied zones. This system cut underground ventilation costs by half and surface ventilation by 73%, while improving working conditions with optimal fresh air for all employees.
Technical maintenance of industrial machines uses significant energy. For example, adjusting mining haul truck valves manually is time-consuming and costly. Sensors streamline this by flagging only the valves needing adjustment, saving time and money. These sensors allow for real-time health monitoring of equipment.
Digital twin models use data from motion, temperature, and spatial sensors to create virtual replicas of equipment. Machine learning software quickly detects anomalies and notifies operators of potential issues. Digital twins predict when maintenance is needed, helping companies prevent problems and forecast future outcomes.
Rolls-Royce, powering over 35 types of commercial aircraft with 13,000 engines globally, uses digital twins to monitor engine operations and schedule maintenance. Onboard sensors create virtual replicas of each engine, allowing real-time health and environmental monitoring.
This technology has made engines more efficient, increasing the time between maintenance by up to 50%, reducing parts inventory, and minimizing aircraft downtime.
Manual inspection is difficult, time-consuming, and expensive, often leading to fatigue and misclassification of defects. Human error risks factory floor machines' functionality, with costly restarts for non-stop operations.
Computer vision (CV), an AI subset, surpasses human capabilities by interpreting visual data, detecting objects, and identifying their locations. Integrated with cameras, CV helps energy companies monitor pipes and cables and identify utility service needs. CV algorithms can also detect smoke or fire on devices or power lines. Importantly, CV cameras have minimal impact on the existing infrastructure and can cover large areas.
Aramco, a Saudi Arabian petroleum and natural gas company, aimed to prevent drilling rig blowouts. Partnering with FogHorn, they implemented an edge-based computer vision (CV) solution using high-bandwidth cameras to monitor rigs in real time. Edge analytics detect potential failures with minimal latency, allowing workers to receive alerts and shut down operations if necessary.
Aramco also integrated AI into their surveillance system to detect fixed and moving assets and monitor worker safety. The system sends automatic alerts for safety guideline violations or hazards.
Oil and gas companies need to monitor drilling rigs, oil wells, refineries, remote offshore platforms, and miles of pipelines. These areas can be risky and often inaccessible for human workers, making equipment monitoring crucial to avoid costly downtime and energy loss.
Drones, equipped with cameras, can capture photos and videos from various angles. Machine learning software processes this visual data to reveal defects better than humans, helping oil and gas companies reduce operational costs.
The Beauly to Denny power line in Scotland spans 220 km with 615 transmission towers. Drones are used for inspection, operated by a two-person team—a drone pilot and a camera operator. The drone captures visuals while the camera operator photographs tower components. Processing software grades each component against defect standards, with results displayed on a visual asset management platform.
The industrial sector has a high energy demand, but replacing old technologies with new ones can save energy. IoT energy management allows companies to control energy supply automatically and respond quickly to changing needs and prices. Advances like computer vision, drone inspections, and digital twin monitoring contribute to this, helping you:
Contact us to make your digital transformation smooth, effective, and affordable.