MBAA TQ https://doi.org/10.1094/TQ-60-4-0213-01 | 
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Ian Weller. Analytics Project Manager, ITG Technologies, Jacksonville, FL, USA
Abstract
This article presents a comprehensive exploration of optimizing beer filtration processes through the integration of artificial intelligence/machine learning (AI/ML)-driven advanced process control (APC) systems. Leveraging Sorba.ai’s APC technology, in collaboration with ITG Technologies ML integrators, this study revolutionizes beer filtration, enhancing key performance indicators for breweries worldwide. The methodology encompasses various stages of analytics and ML, including data collection, preprocessing, exploratory data analysis, inferential analytics, predictive modeling, and prescriptive analytics. The Kieselguhr filter (K-filter) is central to achieving high-quality beer production. By employing predictive dosing algorithms, the APC system optimizes filtration parameters in real time, leading to increased beer volume per filter run, reduced turbidity, and minimized waste. Results demonstrate significant improvements over traditional programmable logic control (PLC) filtration methods, highlighting the efficacy of AI/ML technologies in the brewing industry. Through continuous monitoring and optimization, APC-driven filtration not only enhances product quality but also aligns with global sustainability initiatives. This innovative approach marks a change in thinking in beer production, emphasizing data-driven efficiency and quality enhancement.
