An edible oil processing company struggled to run various products with unique characteristics until they applied state-based control loop performance monitoring software to handle the differences.
Good automation system designs are built to detect and compensate for many operating conditions. However, one food and beverage manufacturer simply couldn’t tune their equipment adequately when processing different types of edible oils due to unique product characteristics that dramatically affected loop tuning requirements. In an article we wrote for Food Engineering June 2023, titled State-Based Control Uncovers Automation Gains, we discuss how many manufacturing and processing operations with similar issues can benefit from applying state-based analytics to recognize and adapt to drastically different conditions.
Manual to automatic
The end user in this case operated a process to “bleach” edible oil, which removes the color and chlorophyl. Part of the process involved dosing clay into the oil at the proper rate. As two different types of oil were processed, it became apparent that they reacted very differently. At first the operators compensated manually, and eventually the site applied model predictive control (MPC). The team could get Product “A” to respond well, but Product “B” production became worse.
As a first step, the team installed an inline colorimeter to provide the process with immediate feedback, instead of relying on manual and time-delayed grab samples and lab measurements. Now they had an overload of data, but it wasn’t possible to tease out insights and underlying issues without additional help.
The team was already familiar with using Control Station PlantESP control loop performance monitoring (CLPM) software for tuning individual loops in a given state, such as running Product “A” or “B”. But if the analysis is performed across runs of different states—in this case different products—then the CLPM software operates on averages which will mask details, so it will not optimize performance for either state.
Fortunately, Control Station PlantESP CLPM software includes the capability to identify and distinguish different operating states, and to perform analysis on a per-state basis. These state-based analytics enable users to define many unique operating conditions so that analysis and optimization activities can be tailored to specific scenarios.
Plant personnel just informed the CLPM software about which product was running, a simple task. Once this information was entered, the CLPM software calculated optimal PID tuning parameters, which were then applied in real time with the MPC system. Control Station PlantESP enabled the oil processing facility to obtain an accurate performance assessment and optimize production for each product type.
This company has since used Control Station software to improve the operation of many other PID loops, at this facility and across more than 100 other sites globally. Analytics on their own don’t fix anything, but putting powerful tools like PlantESP into the hands of users enables them to use analytics results effectively.
The team at Control Station is happy to discuss how CLPM and state-based analytics can be applied to help with your applications. Contact us today!
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