Evaluation of the operational performance of an automated deep-freezing tunnel: cycle times, thermal stability, and handling errors.

This study assesses the operational performance of an automated blast-freezing tunnel using a reproducible metric framework. Three scenarios are evaluated: (A) baseline operation, (B) improved sealing, and (C) PLC-optimized sequencing. Primary variables include total cycle time (broken down by stages), thermal stability across zones (standard deviation and inter-zone gradients at inlet–center–outlet), and the manipulation error rate (jams, retries, misalignments). The setup uses thermal probes sampled at 1 Hz, PLC/HMI timing markers, and a structured classification of operational events, while controlling for confounders (product mass, placement, and set-points). Findings indicate that enhanced sealing reduces thermal variability and inter-zone gradients, whereas optimized sequencing significantly shortens total cycle time without increasing errors, meeting non-inferiority criteria for thermal uniformity. Practical implications are discussed regarding traceability, operational availability, and continuous improvement of the freezing process in academic-industrial environments.