Development of a Digital Twin for a Bakery Line With Predictive Analytics and Adaptive Control Functions

ABSTRACT This paper presents the development and validation of a digital twin system for a bakery production line, integrating real‐time sensor data, physics‐based process models and advanced predictive analytics with CNN + LSTM neural networks. The proposed architecture combines logistic growth, moisture evaporation and heat transfer equations with deep learning for accurate prediction and early detection of baking defects. Simulation and pilot implementation results demonstrate that the digital twin reproduces dough volume dynamics with an error below 3%, predicts humidity within ± 2% and stabilises oven temperature in a narrow range (± 1.2°C). The intelligent system enabled a 77% reduction in unplanned equipment downtime, decreased alarm events by over 60% and reduced the share of defective products from 8% to 2%. These outcomes highlight the practical impact and scalability of the hybrid digital twin framework for improving product quality, minimising losses and enhancing process reliability in food manufacturing.