What is the Difference Between Traditional and State-Based Process Analytics? How Do State-Based Analytical Tools Distinguish Conditions from States?

For coffee lovers, creating the perfect cup may be a lifelong pursuit. For sure there are dozens of factors that affect any particular pour. Basic ones include the specific type of coffee beans and the amount of sugar and/or cream used. If a more serious investigation were to be undertaken, then it’d be easy to imagine that the type of roast that the beans underwent would matter just like the brew’s water source. With the addition of each new attribute, assessing a cup of coffee’s merit becomes more thorough. So too, the process for determining which specific factors are central to a quality cup becomes more difficult.

Industrial manufacturers experience similar challenges when assessing the performance of critical production processes and while pursuing optimization. Whether a given facility’s outputs are referred to in the context of products, recipes, batches, loads, etc., they are all influenced by variability in their production inputs as well as by differences in the facility’s approach to control. As the number of those differences increases, optimization becomes more elusive. Fortunately, the introduction of state-based process analytics makes assessment and optimization easier. Consider the following:

Defining a Condition:

A condition is any factor that affects a production process. Though not applicable to every process, factors like controller mode, APC on vs. APC off, and product A vs. B vs. C are all examples of different conditions. As with the coffee metaphor, the roast of the coffee beans (light or dark) and the amount of sugar (one versus two teaspoons) would be examples of conditions. Conditions can range from basic to complex, with each having a different level of influence on the final product.

Defining a State:

A state is a specific combination of conditions; it’s just one of the many possible combinations. A state could involve the following combination for an industrial process: controllers operated in Automatic Mode with APC on while producing a recipe called Grade C. Back to coffee: One state might be a light roast served black with two teaspoons of sugar.

When using a traditional PID controller analytics tool to analyze a process operated across multiple states, the resulting calculations combine data from all the different states involved. As a result, the calculations fail to distinguish the unique performance characteristics of any individual state. The lost resolution limits a clear understanding of performance and prevents effective decision-making. These present a problem.

With processes getting more complex and rising expectations for more optimal production, tools equipped with state-based analytics represent a solution. They allow process manufacturers to thoroughly assess the performance of individual states. Equipped with those insights, these tools also allow manufacturers to implement control loop strategies that truly enable optimization. While you may have never tasted the perfect cup of coffee, state-based process analytics will help you tune your facility’s multi-state loops so you have more time to enjoy that cup of joe.