A lesson in balancing efficiency with the maintenance of brand quality

At the Jim Beam distillery in Clermont, Kentucky, founded in 2015, legacy matters. So too does business efficiency. Senior electrical engineer for the firm’s owner, Beam Suntory, Kevin Ludwig and his colleagues set out to answer the question of how to get a few more gallons per minute out of a still.

“We had many processes in manual,” Ludwig says. “How could we optimise it without changing the product? We told them we’d automate it to run the way you expect it to run.”

With Jim Beam on its eighth generation of master distillers, producing 90 million bottles a year between its Clermont and Boston distilleries, “any changes we made needed to respect and maintain the process”, Ludwig adds.

Model predictive control (MPC) provided an answer for Ludwig when he attended an Endress+Hauser distillers’ event presentation.

Taking control

They opted for the Rockwell Pavilion8 console which delivers key performance indicators with baseline indications, controller performance statistics such as percent utilisation (percent of time manipulated variables are enabled), error from desired value and percent of time at constraints. “It’s all the operators need to know,” says Ludwig.

Management views allow users to enable or disable specific manipulated variables and to set targets and limits.

Instead of doing loop control, reading temperatures and setting setpoints, operators would be freed to monitor the process from grain to still, take samples and record information, “not controlling the system when they shouldn’t have to”, Ludwig comments.

The challenge was to push the plant to operational limits, adjusting to shifting constraints automatically and maximising product throughput, while maintaining product integrity

“We’d make as much Jim Beam as we can, exactly as it should be, without any compromise of quality or taste, so the challenge was to push the plant to operational limits, adjusting to shifting constraints automatically and maximising product throughput, while maintaining product integrity.”

At the distillery, MPC on low wine proof allowed the plant to move the process closer to optimum setpoints while reducing standard deviations, says Ludwig. “Operations had been boiling too much water, wasting energy and using still capacity to process water, [not] alcohol.”

On high wine proof, results were a little less impressive. “Operations paid a lot of attention to this parameter, so MPC didn’t move the target much, but it reduced standard deviation.”

Operators tended to set the process up to run safely and smoothly, not to maximise production. “Now, they get the still up and running, load the recipe, and MPC optimises the steady state,” says Ludwig. “Then, when it’s time to clean, they shut it down as they’ve always done.”

MPC manages distillation constraints, targets, material balance and key process constraints but prioritises quality: “If there’s not enough steam available, it will hold the beer feed down to maintain quality.”

The ability of MPC to increase distillation throughput has been stymied by limits in beer brewing and dry house capacity, so instead of more gallons per minute, cost savings are coming from decreased steam usage, maximised beer still feed and preventing alcohol loss.

“It frees operators to focus on highvalue tasks, not holding a tank at a level,” says Rockwell Automation principal engineer Kent Stephenson. “Now, they can spend time looking at how well the plant is running, not twiddling things around.”