Control Station

Assembly Line

MSG process plant adds a dash of optimization

đź“… Date:

đź”– Topics: Process Control, Model-predictive control

🏢 Organizations: Ajinomoto, Control Station


Ajinomoto’s process plant in Eddyville, Iowa, which produces monosodium glutamate (MSG), embodies a typical scenario. The facility has about 200 PID control loops, and the support team initially procured a PID loop-tuning software package followed by a plantwide control loop performance monitoring (CLPM) solution to look after these loops. Despite these investments, the team only had bandwidth for the most basic applications. The situation changed when an enhanced support offering introduced by the software vendor, Control Station, empowered the MSG producer to kick their process performance into high gear.

After six months of teaming with the CLPM supplier, approximately half of the facility’s loops were optimized. The site documented notable improvements in overall process stability, energy use and throughput. The size of crystals produced—a proxy for quality—has been maintained at the highest levels. Comprehensive use of the CLPM software’s capabilities and reporting provided better visibility into loops that are trending in problematic directions, so the team can proactively address underlying issues well before they lead to costly unplanned downtime.

Read more at Control Global

State-Based Control Uncovers Automation Gains

đź“… Date:

đź”– Topics: control loop performance monitoring, model predictive control

🏭 Vertical: Food

🏢 Organizations: Control Station


For this edible oil production company, the equipment was tasked with refining two different types of oil, each with its own characteristics. A key part of the process involved dosing the oil with clay to “bleach” it. This removes the color, chlorophyl and performs other conditioning. The clay must then be filtered out. Each type of oil requires a different clay dosing rate, and in turn the filters need to be cleaned at differing intervals for best overall performance.

Before implementing control loop performance monitoring (CLPM) technology, some aspects of the company’s operations were manual and others were automatic. For example, when production began after a product changeover, operators would manually manipulate the system to achieve a steady state, and then apply tuning settings and setpoints corresponding with the product type. While this would result in product that was well within specification, it used more clay than was necessary.

State-based CLPM was initially proven for the localized part of the process where clay was first dosed and then filtered for the facility’s various products. The technology was quickly extended to make improvements in other PIDs, including control of the bleaching process’ back-end temperature loops. These successes have led to an initiative to identify similar opportunities for control improvements within this individual facility as well as across the company’s more than 100 other sites operated globally.

CLPM software is now part of the standard toolkit for the company to improve operational performance, and to then maintain and sustain that performance over time. Analytics on their own don’t fix anything. But putting useful and accessible analytics in the hands of users, through software with advanced features like state-based control, empowers plant personnel to implement continuous improvement.

Read more at Food Engineering Magazine