THE SCIENCE OF SPIRITS

On a trip to Kentucky, Chris Depcik stood in a warehouse surrounded by rows of whiskey barrels and started thinking about the science behind the spirits, asking a question only a researcher might: How can different distilleries just down the road from each other create such unique products? That scientific curiosity, combined with Depcik’s background in catalyst modeling, sparked an idea.

That idea is taking shape as Distinct DistillAge, a startup using predictive modeling to help distillers better understand the process during the hidden and unpredictable aspect of spirit maturation. Depcik developed a physics-based tool that brings engineering precision to a centuries-old craft, giving distillers real-time insights into flavor development, yield and chemistry across changing warehouse conditions. Unlike machine-learning approaches that rely on massive datasets for calibration and validation, this tool requires minimal input to provide maximum benefit—not just warehouse data collection but a model rooted in physical laws to predict outcomes with confidence. “It’s not going to replace the work that distillers have done for decades,” Depcik, a professor of mechanical engineering at the University of Kansas, said. “But what it can do is provide unique insight into what’s happening inside these barrels during the aging process.”

An inside view

The proprietary DistillAge3D model integrates chemistry, physics and environmental data to simulate aging conditions in real time. If it’s aged in a barrel, the model can provide an inside view of the process. “They can see essentially how much spirit they have left in the barrel, the amount of product in regards to not only the volume but potentially the economic benefit, and tell you what your chemistry is looking like,” Depcik said.

The model simulates key aging parameters to predict what will happen to the spirit in the barrel. By inputting barrel geometry, like height, diameter and circumference, combined with initial conditions—think the barrel’s internal temperature, type of wood and volume—the model can showcase the interaction between chemistry, barrel design and environmental factors. “Let’s say you have a very hot year. How is that going to change your product?” Depcik said. “The model will be able to predict out, essentially telling you, if it’s hotter, this is what you’re going to see. It’s going to change the chemistry. It’s going to change how much you have left. So you might want to move those barrels around or maybe start reenvisioning what you want to do with the product.”

TECH TAILORED FOR TRADITION

While the model can be implemented to allow distilleries to react to the data for desired outcomes, it can also be used before anything is aged in a barrel. “Instead of using it in a reactive manner, you use it in a predictive manner to help you design the ideal warehouse that gives you the highest yield and maybe a unique product at the same time,” Depcik said.

Depcik recently spoke at the 6th annual James B. Beam Institute Industry Conference, sharing the 3D model’s work of simulating chemical reactions happening inside the cask and the valuable insights it can provide to distillers. He showcased how the physics-based model can guide decisions about a lot of variables, including production strategies, batch uniformity, and improvements to efficiency, yield and quality, among others. “It’s meant to be adaptive,” Depcik said. “It’s meant to be able to work with companies and help them develop a unique model based on their own preferred characteristics.”

perks of the park

As a professor and researcher, Depcik never thought he’d end up creating a company. And while Distinct DistillAge is still young, KU Innovation Park provided the support he needed to move his idea out of the lab. “The work with KU Innovation Park has been incredibly helpful in navigating the process from taking the idea and taking a product and then translating it to a commercial venture,” Depcik said. “Being able to work with the Park has been fantastic in making the process relatively easy, a lot easier than I thought it was going to be.”