Leveraging Causal AI Models for Enhanced Process Control in Food Production: A Case Study on Beer Fermentation Quality

Reproducible Kernel Hilbert Space (RKHS)-based methodologies are applied for Digital Twins (DT) modelling and causal analysis of the beer fermentation process. The model accounts for the key process variables (temperature, biomass, sugar, ethanol, and carbon dioxide) along with online beer quality monitoring through the concentration of diketones ( VDK ). The modelling dataset is based on integration of 10 fermentation profiles with the perturbed initial conditions. The focus is on de-confounding process data multicollinearity for development of a DT model with intervention potential aimed for optimising beer quality. The results are graphically presented as a Directed Acyclic Graph (DAG). Based on the DAG structure, a Bayesian Neural Network (BNN) is applied for the graphical presentation of probability density functions of non-linear dependencies of beer quality on the key causal process variables. In addition to the intervention potential of causal DT models, the potential use of counterfac - tual analysis in causal digital twin model is emphasised for improving process control and possible innovations of new products.

• Publication year

  2025

• Venue

  Kemija u Industriji

• Publication date

  2025-11-12

• Fields of study

  Not labeled

• Identifiers
  DOI 10.15255/kui.2025.018
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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