Industry 4.0 + IIoT = Smart Industrial Ovens & Furnaces

Effective process control and automation technologies link thermal processing equipment such as ovens and furnaces with the operator and the supply-and-delivery chain — in a seamless network of information exchange.

The industrial furnace of tomorrow is a smarter machine: safer, better connected, more flexible and more efficient. Based upon smart, connected products, the industrial furnace of tomorrow maximizes efficiency through preventive maintenance and intuitive collaboration with users while minimizing its environmental footprint and total cost of ownership.

Some obvious similarities and differences exist between IIoT and smart manufacturing (Industry 4.0) as well as areas of convergence. Smart manufacturing — or Industry 4.0 initiatives, as they are sometimes known — are focused on expanding manufacturing flexibility, increasing automation levels and implementing digitization. This is not so much the next industrial revolution but an evolution. In the long run, this will reshape complete factories and the way they operate. Such evolution requires embracing a multitude of technologies and ideas that will have a massive impact on end users and OEMs. This will take some time, and IIoT, with all its connected devices, will act as a key enabler.

The IIoT vision of the world is one where smart, connected assets (the things) with varying levels of intelligent functionality operate as part of a larger system. This functionality is tailored to the process needs and ranges from simple sensing and actuating to control, optimization and full autonomous operation.

These systems are based on open and standard internet and cloud technologies that enable secure access to devices and information. This access allows entities to mine process data analytics and exploit the insights gained, along with mobility technologies, to drive greater business value. IIoT solutions that employ a wrap-and-reuse approach rather than a rip-and-replace plan provide greater business control. In addition, this measured approach helps drive the evolution toward a smart manufacturing enterprise that is more efficient, safer and sustainable.

Smart Manufacturing

While the long-term effects of IIoT are at times difficult to predict, three distinct operational environments set the stage for the smart1 manufacturing enterprise to emerge. They are:

• Smart enterprise control.
• Asset performance management.
• Augmented operators.

Smart Enterprise Control. IIoT technologies will enable tight integration of smart, connected machines and manufacturing assets with the wider enterprise. This will facilitate more flexible and efficient — and hence, profitable — manufacturing. Smart enterprise control can be viewed as a mid-to-long-term trend. It is complex to implement and will require the creation of new standards to facilitate the convergence of information technology (IT) and operations technology (OT) systems.
Asset Performance Management. Deployment of cost-effective wireless sensors, easy cloud connectivity (including WAN) and data analytics will improve asset performance. These tools allow data to be gathered easily from the field and converted into actionable information in real time. This will result in better business decisions and forward-looking decision-making processes.

Smart Industrial Ovens and Furnaces

As part of a larger class of smart machines1, industrial ovens and furnaces will incorporate increasing levels of machine intelligence to accommodate the need for predictive planning and flexible business assets.

The term “smart machine” implies a machine that is better connected, more flexible, more efficient and safe. It can quickly respond to new demands. Based upon a collection of smart, connected products, it maximizes efficiency through intuitive collaboration with its users. A smart machine also is capable of participating in predictive maintenance practices while minimizing its own environmental footprint and total cost of ownership.

Efficiency and Self-Awareness. With the use of sensors and the intrinsic knowledge regarding its own capabilities and features, a smart machine will be able to monitor its own key components as well as environmental conditions. Embedded intelligence will correlate upstream and downstream behavior and adapt its own parameters within given business rules. By providing relevant information to several classes of users — operators, connected data consumers at the OEM, and the end user — the smart machine will allow manufacturing lines to produce in a more reliable, flexible and efficient manner. Such optimization can be implemented with respect to energy, time, overall equipment effectiveness (OEE), load shedding, quality or other parameters via upstream systems that provide setpoints based on analytics.

Specific heat treatment applications include:

• The addition of environmental sensors in control panels to monitor control instrument terminal temperatures. It has been demonstrated that controlling this temperature — via cabinet fans or air-conditioning units — can significantly improve calibration drift performance.
• The ability to use process material specifications to refine operating cycle parameters. For instance, embedding auto-calibration technology into sensors in online carburizing applications can significantly improve the overall process accuracy.
• The discrete monitoring of quality (defect information), maintenance (downtime periods) and production activities (turnaround times). In turn, quality, maintenance and production can be improved by utilizing IIoT solutions to coordinate and combine these separate activities into a single OEE key performance indicator, facilitating a better understanding of the overall plant performance.
• The use of power sequencing across an Ethernet backbone to enable automated load-balancing and load-shedding techniques via SCRs.

Silicon-controlled rectifier (SCR) power controllers, or thyristors, were developed to provide a precise method of electrical switching in the control of power circuits in heating applications. They also were designed to overcome the limitations and lifetime issues of mechanical contactors.

SCR technology now is being used to take an isolated controller and provide a system approach to managed power demand. A number of SCR devices will fire randomly at what could be many times per second. If the units fire at the same time, then the load demand increases. By using power sequencing across an Ethernet backbone to enable automated load-balancing and load-shedding techniques, it is possible to order the firing pattern and reduce the ultimate peak-load demand. These smart, connected devices can be used for single- or multi-zoned equipment and also can be leveraged across multi-equipment cells.

This level of machine monitoring also facilitates preventive maintenance supported by the supplier or OEM, helping to avoid component failure and associated downtime or damage to the machine or components. It also allows for maintenance to be scheduled — minimizing the impact on production — while increasing business opportunities for value-added services.

Efficiency and Data Management. Smart machines must have the appropriate level of intelligence to assess data quickly and in a decentralized fashion. Routing all data to a central control for analysis will quickly lead to delays as it is a non-scalable structure. Sensors, components and machinery with the intelligence to only share data that falls outside of set parameters will lead to better overall data management. Improving the level of data shared with the broader network/community will accelerate decision making and reduce backlogs that could lead to critical information being delayed or missed altogether.

An extension of this feature set in data-management systems provides security management and full audit trail functionality. These smart data-management systems and digital chart recorders have evolved into mini-productivity stations, allowing key parameters for thermocouple use, calibration tests and machine maintenance to be scheduled directly on the device.

The final storage of data also is an important consideration. To date, local hardware largely has been used to store production data, but this method can be time consuming and expensive to manage. Storing data in secure internet-based computing resources — more commonly known as the cloud — is increasingly becoming a viable option to help better manage data in a cost-effective manner for long-term storage. For instance, using the cloud, a company could employ retention periods of 30 years or more.

Process Data Safety and Security. Improvements in machine performance and lifetime cost reductions cannot be offset by reducing the safety or security of the machine or production line. With security built into their fundamental designs, smart machines will improve the safety of operators and minimize the security risk of increased networking. The ability to utilize a mix of safety components and controllers will allow machine builders to fit the solution to specific end-user application requirements, helping to improve overall performance and productivity.

Currently, data security is the leading inhibitor of end-user adoption of new networking technologies and work processes. The perceived risk of networking components and machinery in order to achieve production benefits is high.

Particularly with IIoT and increasing levels of connectivity, security needs to be considered at numerous levels. Security provision needs to be multilayer, incorporating hardware, software and services. Machine builders and automation-component vendors must ensure that end-users are aware of security vulnerabilities and can manage network infrastructure to minimize the risk of a breach. User cases and success stories can help educate end-users about the benefits of smart machines and how security can be maintained.

Any new smart machines will need to be compatible with the existing installations or machinery from multiple OEMs. End users want devices that can be installed within a short timeframe. Integration into the rest of the system must be easy.

Industry-hardened scalable data hubs for heat treatment are one way to take direct input — from wired sensors and digital signals from other local devices that capture data near the source — and provide a secure link to both plant-based business systems for internal analysis and storage in secure, remote data centers.