Industrial process control has been around since the first century, automating processes to minimize or eliminate human intervention. Now, however, with AI, ML, and other advancements in the IoT, a lot is changing. Bob Banerjee, VP Products, EPIC IO, discusses the blurred lines between IoT and process control and what this means for organizations in the future.
Since the 1920s, process control has been used to automate production lines for everything from automobiles or baked goods to precision optics and semiconductors. In contrast, IoT technology only exists from the internet and is used to collect data from various environments so that it can be analyzed and acted upon. But with Industry 4.0 revolutionizing the way companies manufacture, improve, and distribute their products by integrating IoT, cloud computing, and analytics, AI, and machine learning into their production facilities, borders between process control and IoT are fading.
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IoT versus process control
We generally think that process control is used in continuous production processes or production lines. In the 1760s, process control inventions aimed to replace human operators with mechanized processes. For example, Oliver Evans created a water-powered flour mill that worked using buckets and screw conveyors in 1784. Henry Ford applied the same theory in 1910 when the assembly line was created to reduce the human intervention in the automotive production process.
Today, process control systems help us achieve levels of production consistency, economy, and safety that cannot be matched by manual control. In most cases, process control networks are hardwired to deliver very high levels of reliability and resiliency with very low latency. After all, if a production line makes thousands of widgets per hour and its process control system measures some defective widgets, it needs to get them off the line quickly.
In contrast, IoT networks have historically focused on collecting data rather than controlling devices. A true IoT network sees the big picture much more than just remote sensing. It “knows” not only when a door is open or a light has been left on, but also that there are still people in the hallway, for example. In smart buildings, IoT networks can be used to monitor ambient light (then inform an industrial control system to change the light intensity based on predefined thresholds) or to monitor and report the average number of people in a room in order to facilitate the arrangement of the space.
Another static example of IoT would be monitoring levels inside two sewage tanks side by side at a construction site. Even if one of them is full, the system will only give a slight warning because it knows that, based on the rate of recent deposits in the tanks, there is still a long time before they are full. both full and need to be recycled. A truly smart IoT network would even know the tank emptying schedule and calculate the overflow probability. True IoT, coupled with AI, seems far beyond simple remote sensing.
But there are many real-time applications, including those where wireless connectivity is essential, where the IoT is now making inroads into traditional process control territory. Some of them are time-sensitive and cannot afford the latency associated with slow or interrupted connectivity. Others are critical and cannot afford a single point of failure. Reliability layers create self-contained feature shells, protecting overall performance against established threat vectors. If the IoT sensors cellular connection dies, the local LAN continues to run everything until cellular service is restored. If the LAN goes down, each node on the LAN knows what to do and keeps doing it until everything reconnects and it can report its status. If the node itself dies or loses power, it should seamlessly resume upon resurrection.
Integration of IoT and process control: an example
A recent example of IoT technology to control a process is a food safety system protecting products in transit. The food industry has traditionally relied solely on refrigeration to control levels of pathogens like E. coli or Listeria, but refrigeration does not kill pathogens – it just slows the rate at which they multiply. In long-haul shipping scenarios (bringing fruit from Chile to the eastern United States, for example), even slow pathogen growth is undesirable. Scientists have shown that by adding ozone to the atmosphere inside a container, it is possible to kill these pathogens.
Recently, EPIC iO launched a system that injects a small, precise concentration of ozone into a food transport container to control pathogen levels without affecting the product it is trying to protect. (Company research has shown that injecting ozone can significantly extend the life of products by limiting common pathogens.) This briefcase-sized system injects ozone into a container refrigerated, monitors these levels every few seconds and adjusts its output accordingly. The system is autonomous, so decisions about ozone concentrations are made instantly, and the IoT is used to collect data on ozone levels over time and upload it to a data lake in the cloud.
In this case, the lines between IoT and process control have become blurred. This briefcase device brings IoT and AI to the world of process control – it finally meets the demands for reliability, accuracy, responsiveness and dependability.
Bringing AI into the picture
The system described below can be supplemented with video, which is a good example of adding AI. Imagine monitoring the container with a video camera and then automatically stopping the flow of ozone if someone enters the container. (Ozone is a strong oxidizer with many industrial and consumer applications, but you don’t want to inhale more than necessary). Sure, you could use more IoT devices, such as motion sensors, but what if you’re covering 10,000 square feet? A single camera would find it easy.
To accomplish this automated monitoring, the AI analyzes the video images and takes appropriate action if it detects a human presence. Video analytics is a common application for AI engines for applications such as facial recognition.
The future ahead
As cities and organizations seek to streamline their IT networks and converge operational IT and technology networks, AI and IoT will play an increasingly important role in automating all processes. For example, a remote sensor reports the temperature and relative humidity of a truck’s cargo. A more sophisticated IoT network merges this data with GPS information and the rate at which disinfectant ozone is consumed by cargo, not just from this truck but hundreds of trucks in the fleet. This IoT data ends up in a data lake – an invaluable repository of data waiting for AI and data scientists to crawl it for patterns and anomalies.
Process control, AI, IoT, latency, resiliency, connectivity, and autonomy are discrete concepts, but the lines between them are becoming blurred as distributed intelligence and computing power continue to grow.