Predict and Maximize Filter Lifetime
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Beer is typically filtered using diatomaceous earth, a chalky substance made up of fossilized shells. However, the filtration process is prone to failure because of the large number of variables that affect the filters, they sometimes clog unexpectedly. When the filter clogs, it stops the filtration process, everything needs to be cleaned, possibly replace the filter, and then start the process over again.
Because there’s an expected timing of the flow of beer through the entire brewing process, these clogs impact subsequent steps. This is especially problematic in the peak summer season when breweries need to produce significantly more beer than at other times and can’t afford unscheduled downtime. With machine learning, though, possible filtration problems can be detected early so that appropriate action can be taken before filters are clogged and subsequent steps are impacted.
Benefits for the company
By having an early indicator that there’s something wrong, steps can be taken to make sure things run smoothly and production goals are met.
Feasability
High
Type of expertise/ AI domain
Regression analysis, Ranked scoring, real-time analysis and prediction
Internal data required
Ingredients and types of beers being produced in a brewery
Beer production—like any other complex, multi-step manufacturing process—is prone to slowdowns in certain areas that affect the rest of the process.