AI-Powered Beer Filtration Optimization

The client was confronted with several critical issues in their brewing process that impacted efficiency and drove up costs:

• Sudden Filter Clogging: This led to unexpected production stops and required immediate interventions.
• Persistent Beer Recirculation Issues: These prolonged filtration times and increased energy consumption.
• Costly Over-dosing of Filtration Aids: This resulted in unnecessary material costs and increased waste.

These factors culminated in significant operational inefficiencies and a noticeable rise in production costs.

To overcome these challenges, we adopted a collaborative and data-driven approach:

• Close Collaboration: We worked hand-in-hand with brewery operators and data experts to gain a deep understanding of the processes and challenges.
• Data-Driven Foundation: Filtration process data and our extensive domain know-how formed the basis for algorithm tuning.
• Tailored ML Model: We developed a specialized machine learning model for filtration optimization, utilizing the following techniques:
  1. Behavior Classification: Identifying patterns within the filtration process.
  2. Runtime Clustering: Grouping similar filtration runs for better analysis.
  3. Clogging Time Forecasting: Predicting when filter clogging is likely to occur.

By applying these methods, we enabled early anomaly detection and refined the dosing control of filtration aids.

The implementation of our AI model led to measurable improvements and sustainable benefits for the client:

• Improved Filtration Efficiency: Significant reduction in waste and losses.
• Optimized Filtration Time: Extended filter life through more efficient usage.
• Enhanced Beer Quality: Ensuring consistently high product quality through superior filtration.
• Scalability Plan: Definition of a plan to scale the solution to other beer types and brew houses, including controller integration and future app concepts.