Selection and application of control principles in beer brewing processes based on MCDA framework

Beer production is a complex process involving multiple stages with diverse control requirements, including nonlinear biological reactions and energy-intensive operations. To ensure consistent product quality, operational efficiency, and compatibility with digital manufacturing technologies, the selection of appropriate control strategies is critical. This study presents a structured methodology for the evaluation and integration of control system principles tailored to beer production. The process was decomposed into key operational stages, mashing, boiling, fermentation, conditioning, and packaging, and specific control objectives were defined for each. A multi-criteria decision analysis (MCDA) framework, based on the Analytic Hierarchy Process (AHP), was applied to assess six control methods: PID, cascade, feedforward, fuzzy logic, model predictive control (MPC), and On/Off control. Evaluation criteria included control performance, ease of implementation, adaptability, energy efficiency, cost-effectiveness, and Industry 4.0 integration potential. The results indicated that a hybrid control approach, combining PID, fuzzy logic, and MPC, offers optimal performance across the production workflow. An integrated control architecture was designed to coordinate these methods within a scalable and intelligent automation framework. The proposed solution supports real-time monitoring, improved process stability, and readiness for future digital upgrades, providing a practical model for intelligent brewery operations.