The insights extracted from collected data are most often presented through the creation of models (step 3) that illustrate how selected process variables interact with other variables. These models could be soft measurements of unmeasurable process variables or product properties, complex models of process behavior for process control solutions, and/or calculations of process performance indices that help monitor the health of a process. To illustrate the idea, we will focus on soft sensors, the types of models that are based on machine learning and that produce inferred calculations of physical measurements.
So, what is a soft sensor? A soft sensor uses various process variables to infer or estimate a process variable or product quality measurement that cannot be measured in real-time or by a physical sensor. They are an addition to laboratory measurements
, which provide valuable insight into the final properties of a reel at end of production, but not during production, when actions can still be taken to affect the end result. In the world of papermaking, soft sensors can be applied to many different properties including: sheet quality, wet-end ash calculations, sheet strength, kappa value in pulping processes, sheet weight
, and more.
Building soft sensor models requires a significant investment in time and effort before they can be used online in the manufacturing process. Historical process and product quality data – the foundational inputs for soft sensor development – are usually stored in different databases. To create soft sensors, various exporting tools and techniques (examined in steps 1 and 2) are needed to extract and synchronize this data. Step 3 is the physical creation of the model using the insightful information extracted from the data. When done correctly – and within the context of a proper data strategy – soft sensors bring the value of real-time measurements to variables that were previously only measured periodically.
To make the most of soft sensors, they need to be integrated with controls systems by leveraging application programming interfaces (APIs), which provide connectivity, device management, software management, and data handling for various data-driven solutions. APIs are the backbone of online digital solutions, allowing data to flow seamlessly through control systems, edge devices, cloud platforms, and online data-driven analytics solutions. This means the output of such analytics solutions – of which soft sensors are one – can be made available to control systems for monitoring and/or control applications, completing step 4.
For example, soft sensors that provide measurements of sheet quality can stand in for traditional sheet measurements during start-up, sheet breaks, or while scanners are temporarily offline for service. With this additional insight, papermakers are given complete visibility of the process status and have full access to process controls to achieve smoother transitions, faster sheet break recovery, and better overall process conditions.