The Digital Twin of your Fermenter - Part 2

Digital Twins - Enabling Smart Fermentors

Introduction

In part one of this post, we covered the basics of a digital twin, and how it provides a digital representation of a machine, facility or process. The purpose of the digital twin will differ, but to establish a digital twin of any sort, you need good input data. As with any other analysis: “sh** in – sh** out”.

The digital twin of your fermentor

For PLAATO, the data we capture covers the key parameters of a fermentation process. But setting up the data stream is just the beginning. Now you get to the value-adding part: turning data into decision support and decisions. There are many ways to deliver value in this way, and we wanted to give some examples of the benefits our customers see:

Visualizing data:

By providing professionals with access to real-time data (often for the first time), the data and trend lines can often make their next action look obvious. By taking the right action at the right time, they can avoid stalled fermentations, time a critical process windows at exactly the right moment (e.g. when to dry hop or do diacetyl rest), and also increase the yield of their fermenters when knowing when the fermentation process is finished.

Predictive analytics:

By combining real-time fermentation data with our global fermentation data set, the PLAATO Cloud can provide accurate estimates of when the fermentation process will stop, even weeks in advance. Research has shown that the typical turnaround time for a fermentation vessel today is 18 – 25 days (depending on what is being made). With improved data capture, and our machine learning algorithms, PLAATO can help reduce this to 13 – 21 days. Saving 4-5 days, which can lead to >20% production increase.

Data aggregation:

By combining data on the raw materials going into a fermentation process (e.g. malt, hops, water, and yeast) and also the metadata such as the number of times the yeast in question has been used (yeast generation), we can really learn what implications every change can have on a fermentation process. Say that the optimal yeast performance is hit in generation two or generation three, and that performance will decline in generation four and five – this will be critical data to provide accurate analytics (use case above).

Remote capabilities:

“If you can’t measure it, you can’t manage it”. With real-time data feeding the digital twin of your fermenter vessels, the team’s time can now be spent more effectively. Weekend sampling is an unwelcome chore for many, requiring on-site presence and time away from family and friends. With remote capabilities, checking the status takes seconds, and unnecessary trips can be saved.

Conclusion

Throughout this exploration, we’ve seen how the foundation of granular, real-time data capture is key to unlocking a world of improvements in fermentation processes. A digital twin translates this data into day-to-day value, offering easy-to-understand graphics, predictive scheduling capabilities, and a deep, multi-parameter view of your processes. This empowers professionals to make informed decisions for optimizing both fermentation quality and efficiency. Furthermore, by integrating automation into this digital framework, we can achieve 24/7 production support with intelligent adjustments, while still ensuring the essential role of human expertise in strategic oversight and continuous improvement.

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