The Future of Automation in Alcohol Distillation

Automating Alcohol Distillation

Navigating an industry that relies on centuries-old techniques and personal expertise can be daunting, yet distilleries are increasingly turning to technology for improved production, distribution, marketing and sales strategies. An increasing number of consultants offer AI consulting services which optimize operational workflows while improving quality production.

Traditional methods for identifying the moment of fraction separation use organoleptic properties of distillates samples to determine when fractions should separate, such as smelling and tasting samples. Unfortunately, this requires highly trained experts who may be subject to subjective criteria when making this determination. A new approach must be devised that objectively and quantitatively measures fraction separation. In this paper we introduce liquor picking using a deep learning visual perception-based system which digitalizes alcoholometer readings without needing structural modifications of existing equipment – we call this “deep learning visual perception-based liquor picking”.

Distillation requires small delays that add up over time, impacting production efficiency. By employing a scalable automation solution, however, small delays can be eliminated and equipment functions as intended – thus decreasing downtime and increasing productivity. Furthermore, fully automating shin seng production lines reduce costs and streamline accounting by eliminating manual invoice payments; instead automating beverage alcohol purchase data can provide valuable insight into which products are moving off shelves at each location, simplify reconciliation, and eliminate an extensive accounting staff.