Akvelon | 3 Ways Businesses are Using Data to Optimize Automation with the Internet of Things
Data is helping businesses in several different industries become more efficient in their Internet of Things automation. From paper mills to the automotive industry.
Internet of things automation
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31 Aug 3 Ways Businesses are Using Data to Optimize Automation with the Internet of Things

Making data the basis for automation and control means converting the data and analysis collected through the Internet of Things into instructions that feed back through the network to actuators that in turn modify processes. Closing the loop from data to automated applications can raise productivity, as systems that adjust automatically to complex situations make many human interventions unnecessary. Early adopters are ushering in relatively basic applications that provide a fairly immediate payoff. Advanced automated systems will be adopted by organizations as these technologies develop further.

1. Process Optimization

The Internet of Things is opening new frontiers for improving processes. Some industries, such as chemical production, are installing legions of sensors to bring much greater granularity to monitoring. These sensors feed data to computers, which in turn analyze them and then send signals to actuators that adjust processes—for example, by modifying ingredient mixtures, temperatures, or pressures.

Sensors and actuators can also be used to change the position of a physical object as it moves down an assembly line, ensuring that it arrives at machine tools in an optimum position (small deviations in the position of work in process can jam or even damage machine tools). This improved instrumentation, multiplied hundreds of times during an entire process, allows for major reductions in waste, energy costs, and human intervention.

Internet of Things Paper industry

Automation in the paper industry Internet of Things has helped increase quality

In the pulp and paper industry, for example, the need for frequent manual temperature adjustments in lime kilns limits productivity gains. One company raised production 5 percent by using embedded temperature sensors whose data is used to automatically adjust a kiln flame’s shape and intensity. Reducing temperature variance to near zero improved product quality and eliminated the need for frequent operator intervention.

2. Optimized Resource Consumption

Networked sensors and automated feedback mechanisms can change usage patterns for scarce resources, including energy and water, often by enabling more dynamic pricing. Utilities such as Enel in Italy and Pacific Gas and Electric (PG&E) in the United States, for example, are deploying “smart” meters that provide residential and industrial customers with visual displays showing energy usage and the real-time costs of providing it. (The traditional residential fixed-price-per-kilowatt-hour billing masks the fact that the cost of producing energy varies substantially throughout the day.)

Based on time-of-use pricing and better information, residential consumers could shut down air conditioners or delay running dishwashers during peak times. Commercial customers can shift energy-intensive processes and production away from high-priced periods of peak energy demand to low-priced off-peak hours.

Data centers, which are among the fastest-growing segments of global energy demand, are starting to adopt power-management techniques tied to information feedback. Power consumption is often half of a typical facility’s total lifetime cost, but most managers lack a detailed view of energy consumption patterns. Getting such a view isn’t easy, since the energy usage of servers spikes at various times, depending on workloads. Furthermore, many servers draw some power 24/7, but are used mostly at minimal capacity, since they are tied to specific operations.

Data Center IoT Automation

IoT automation keeps data center energy consumption patterns in check

Manufacturers have developed sensors that monitor each server’s power use, employing software that balances computing loads and eliminates the need for underused servers and storage devices. Greenfield data centers are already adopting such technologies, which could become standard features of data center infrastructure within a few years.

3. Complex Autonomous Systems

The most demanding use of the Internet of Things involves the rapid, real-time sensing of unpredictable conditions and instantaneous responses guided by automated systems. This kind of machine decision making mimics human reactions, though at vastly enhanced performance levels.

The automobile industry, for instance, is stepping up the development of systems that can detect imminent collisions and take evasive action. Certain basic applications, such as automatic braking systems, are available in high-end autos. The potential accident reduction savings flowing from wider deployment could surpass $100 billion annually.

Some companies and research organizations are experimenting with a form of automotive autopilot for networked vehicles driven in coordinated patterns at highway speeds. This technology would reduce the number of “phantom jams” caused by small disturbances (such as suddenly illuminated brake lights) that cascade into traffic bottlenecks.

Learn more about the Internet of Things this November at CloudExpo in Santa Clara, CA. Akvelon expert, Sergey Grebnov will be hosting a session titled “Intelligent Bots: Manage Your Smart Home from the Cloud!” where he will present current possibilities with the Internet of Things and where the technology is headed in the future. Click here to register for a free pass to attend CloudExpo as Akvelon’s guest to see Grebnov’s presentation and more!

Sergey GrebnovSergey Grebnov is a Senior Software Engineer at Akvelon Inc., focused on IoT, Open Source and mobile related projects. He is an active Open Source member and Apache Cordova PMC. He holds a PhD in Computer Science from Ivanovo State Power University, Russia.

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